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Energy efficiency has become a major concern for manufacturing systems, due to industry being the largest user of scarce, finite energy sources, and also to recent events which have pushed energy prices to alarming levels. In the present Industry 4.0 context, Reconfigurable Manufacturing Systems (RMS) are therefore one of the most promising manufacturing paradigm. In this paper, we investigate the suitability of one of the most common types of RMS, the Parallel-Serial manufacturing line with Crossover, to help minimise the peak of the electric power consumption. More specifically, the balancing of such a production line is studied, so as to integrate power peak minimisation from the design stage. Thus, we define the Parallel-Serial-with-Crossover Assembly Line Balancing Problem with Power Peak Minimization, a new combinatorial NP-hard problem. We also propose a suitable time-indexed Integer Linear Program that integrates balancing and scheduling decisions and a matheuristic algorithm designed to tackle large-size instances. Both approaches are tested on a wide set of instances. The computational results show that relevant power peak reductions can be achieved (33% on average), opening up promising perspectives from both algorithmic and managerial viewpoints. [ABSTRACT FROM AUTHOR]

Cloud computing (CC) is a technology that enables the delivery of IT services outside of the workplace. CC, on the other hand, has had several drawbacks. The task scheduling issue is taken as one of the important difficulties because a solid mapping between available resources and users' activities is essential to reduce the execution time of users' jobs (i.e., minimize makespan) and maximize resource utilization. Because the service provider must offer several customers' benefits at distinct times and from distinct locations, task scheduling is indeed a serious challenge in CC. As a result, in the CC environment, these operations must be scheduled in a more dynamic and timely manner. The objective is to provide an enhanced task scheduling algorithm for allocating the task of the user to different computing resources. The major aim of the research work is to reduce the cost and the execution time as well as to improve the resource utilization of the task scheduling problem using the improved support vector machine (ISVM) and the optimization concept. The novel algorithm used here merges two familiar algorithms as squirrel search algorithm (SSA) and the horse herd optimization algorithm (HOA) leading to a new hybrid metaheuristic algorithm called the horse herd–squirrel search algorithm (HO–SSA). The developed HO–SSA assists in introducing a multiobjective optimization for efficiently handling task scheduling issues in the cloud sector. The proposed HO–SSA method for the task scheduling in CC model in terms of cost is 22.22%, 15.73%, and 38.74% better than SSA, HOA, and TSA, respectively. Similarly, the proposed HO–SSA method for the task scheduling in CC model with respect to energy is 9.68%, 5.35%, and 22.50% superior to SSA, HOA, and TSA, respectively. The proposed method outperformed the existing methods like SSA, HOA, and TSA in terms of cost, energy, degree of imbalance, makespan, speedup, and efficiency. [ABSTRACT FROM AUTHOR]

Scheduling tasks is a pivotal and practical action within the fog and cloud layers. This study introduces a two-tier task scheduling approach with improved version of the Harris Hawk optimization algorithm to lower delay and power consumption. We employ the ConvLSTM neural network in fog layer to predict the optimal location for task execution and estimate the workload of virtual machines. We harness the Jellyfish Search (JS) optimizer to schedule executable tasks within the fog layer efficiently. Furthermore, we present an enhanced version of the Harris Hawk optimization algorithm, incorporating sine and cosine searching for task scheduling optimization. Our proposed algorithm can predict virtual machine workloads and task execution locations based on task and resource characteristics, resulting in reduced task completion times and energy consumption. Our experiments demonstrate that our approach exhibits lower delays and energy consumption in cloud layer task scheduler compared to the MGWO, NSGA-II, and MOPSO algorithms. Furthermore, it outperforms several algorithms, including CGO, AOS, CSA, WOA, HGSWC, MPA, and CHMPAD, when it comes to minimizing delays in fog layer task scheduling, ultimately leading to faster task execution. [ABSTRACT FROM AUTHOR]

The recent focus on cloud computing is due to its evolving platform and features like multiplexing users on shared infrastructure and on-demand resource computation. Efficient use of computer resources is crucial in cloud computing. Effective task-scheduling methods are essential to optimize cloud system performance. Scheduling virtual machines in dynamic cloud environments, marked by uncertainty and constant change, is challenging. Despite many efforts to improve cloud task scheduling, it remains an unresolved issue. Various scheduling approaches have been proposed, but researchers continue to refine performance by incorporating diverse quality-of-service characteristics, enhancing overall cloud performance. This study introduces an innovative task-scheduling algorithm that improves upon existing methods, particularly in quality-of-service criteria like makespan and energy efficiency. The proposed technique enhances the Snake Optimization Algorithm (SO) by incorporating sine chaos mapping, a spiral search strategy, and dynamic adaptive weights. These enhancements increase the algorithm's ability to escape local optima and improve global search. Compared to other models, the proposed method shows improvements in cloud scheduling performance by 6%, 4.6%, and 3.27%. Additionally, the approach quickly converges to the optimal scheduling solution. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT) devices, IoT applications require real-time and low-latency responses. Fog computing is a suitable platform for processing Internet of Things applications. However, fog computing devices are distributed, dynamic, and have limited resources, so the allocation of fog computing resources to execute heterogeneous and delay-sensitive tasks in the Internet of Things is a significant challenge. In this paper, we mathematically formulate the resource allocation problem to minimize the makespan while meeting the quality of service (QoS) requirements of IoT tasks. Next, we propose two learning automata, namely an automaton for select tasks (ATF) and an automaton for select virtual machine (Avf) to efficiently map IoT tasks to FNs. In this approach, a task is selected from the set of ATF actions and then, a Fog node is selected from the set of Avf actions. If the requirements for executing the tasks on the fog nodes are met, then the resource is allocated to the task. The efficiency of the proposed algorithm is evaluated through conducting several simulation experiments under different fog configurations. The proposed algorithms are evaluated in a simulation environment by extending the iFogsim to simulate a realistic fog environment. The experimental results indicate that the performance of the proposed algorithm is better compared with existing algorithms in terms of MK, response time, delay, Processing time, and cost with the increasing number of task submissions. [ABSTRACT FROM AUTHOR]

Cloud computing has transformed data storage, management, and processing by offering scalable and flexible resources via the internet. A key component of this technology is the efficient allocation and management of resources, particularly through task scheduling at the level of virtual machines (VMs). Task scheduling is critical for maximizing resource utilization and system performance in cloud environments. However, it presents significant challenges due to the dynamic and distributed nature of these environments. Effective task scheduling algorithms are necessary to balance load, minimize response time, and optimize resource usage, making it a crucial area for ongoing research and development in cloud computing. This paper addresses the challenge of task scheduling in cloud computing by employing an analytical approach based on queuing theory. We model the system using a generalized processor sharing (GPS) queue and evaluate its performance through heavy traffic approximation. This method allows us to derive performance metrics for queuing systems prone to congestion, considering general interarrival and service time distributions, thus providing a comprehensive analysis of scheduling efficiency. [ABSTRACT FROM AUTHOR]

In the context of heterogeneous computer systems, the utilization of task scheduling algorithms is crucial for enhancing system performance. A major challenge in the development of efficient scheduling algorithms for such systems lies in reducing execution time. However, achieving this objective is not a straightforward process. Due to the complexity of these problems, exact algorithms are not feasible for solving them, primarily because of the vast search space involved. Consequently, approximate algorithms become necessary. Among these, meta-heuristic algorithms are notable for their ability to explore large search spaces and generate suitable solutions. Nevertheless, these algorithms are not flawless as they may become trapped in local optima and failing to scan the entire sample space. To address task scheduling problems in heterogeneous computer systems, this research proposes the Intelligent Harris Hawk Optimization algorithm. The algorithm was employed to tackle the task scheduling problem in heterogeneous systems, while also considering common issues associated with such algorithms, including early convergence and limited exploration capability during the exploration phase. To overcome these challenges, the exploration phase was separated from the exploitation phase. This approach aimed to ensure that the algorithm explores the entire problem space without necessarily achieving optimality. However, if the algorithm becomes trapped in a local optimum during the exploitation phase, it may not be able to escape and achieve early convergence. To address this issue, a solution called Bigmax to Fitmax was proposed for escaping local optima in the soft besiege phase, while a unique technique called Crisscross Mutation was introduced for the hard besiege phase. In the final stage, the proposed algorithm will be compared with several other algorithms, including HHO (Harris Hawk Optimization), MOSCOA (A multi-objectives scheduling algorithm based on cuckoo optimization), HACG-TS (Hybrid approach based on cuckoo optimization algorithm and genetic algorithm), GA (Genetic Algorithm), HEFT (Heterogeneous Earliest-Finish-Time), and CPOP (Critical-Path-on-a-Processor), to solve the scheduling problem in a heterogeneous environment. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) is a version of the Internet of Things. It is getting the attention of researchers because it can be used in a wide range of smart healthcare systems. One of the main advancements employed recently is the IoMT-cloud, which allows users to access cloud services remotely over the internet. These cloud services require an efficient task scheduling approach that satisfies the Quality of Service parameters with a low energy consumption. This paper presents an overview of the integration of IoMT and cloud computing technologies. Besides,this work proposes an efficient Extended Water Wave Optimization (EWWO) task scheduling in the IoMT Cloud for healthcare applications. EWWO algorithm performs based on its operations propagation, refraction and breaking. The proposed EWWO scheduling technique minimizes the energy consumption, makespan time, execution time and increases the resource utilization. Cloudsim simulator is used to simulate the IoMT-Cloud environment to verify the effectiveness of EWWO technique. The performance has been evaluated based on various parameters such as energy consumption, makespan time and execution time. [ABSTRACT FROM AUTHOR]

In order to successfully address the multiprocessor work scheduling issues, a novel hybrid approach is presented in this paper. Executing a group of tasks on a small number of processors is the main goal of a task scheduling method. The job scheduling issue is viewed as a multi-objective optimisation issue in this study. The goals of this optimisation challenge are to determine the system's least energy consumption and minimum makespan (the sum of the schedule lengths for each task's execution). In order to lower the processors' energy usage, the Dynamic Voltage Frequency Scaling (DVFS) level has the task scheduling issue enabled. To determine the goals, a hybrid algorithm called Hybrid Grey Wolf Crow Search Optimisation (HGWCSO) is suggested. The Grey Wolf Optimisation (GWO) and Crow Search Optimisation (CSO) algorithms are combined to create the HGWCSO algorithm. The social structure and hunting strategy of wolves serve as the inspiration for the GWO metaheuristic algorithm. The CSO, on the other hand, is a metaheuristic algorithm that was motivated by crows' clever conduct. The proposed hybrid approach is put into practise using tasks that are produced at random, and the results of this comparison are classic methods. The proposed method's simulation results are contrasted with those from five existing widely used methods. The proposed technique has also been contrasted with some newly created metaheuristic algorithms using various real-world data sets. According to the simulation results, HGWCSO outperforms other algorithms in terms of lowering makespan and energy usage. The proposed algorithm has successfully improved the makespan and energy consumption with a maximum of 31.07% and 58.36%, respectively, compared to other algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing enables cost-effective resource sharing in hybrid clouds to tackle the problem of insufficient resources by elastically scaling the service capability based on the users' needs. However, task scheduling (TS) in cloud environments is challenging due to deadline-based performance and security constraints. To remove the existing drawbacks based on deadline and security constraints, a Security-Aware Deadline Constraint TS (SADCTS) approach is presented using a hybrid optimization algorithm of the Modified Flying Squirrel Genetic Chameleon Swarm Algorithm (MFSGCSA). The proposed MFSGCSA is developed by integrating the genetic operators into CSA and combining it with the modified Flying Squirrel Optimization (FSO) algorithm in which the position update and global search equations are enhanced by adaptive probability factor to reduce the local optimum problem. In this SADCTS approach, the task assignment process is modeled into an NP-hard problem concerning a multi-level security model using user authentication, integrity, and confidentiality. This maximizes tasks' completion rate and decreases the resource costs to process tasks with different deadline limitations. The optimal schedule sequence is obtained by MFSGCSA, where tasks are scheduled concurrently based on security constraints, demand, and deadlines to improve the prominence of cost, energy, and makespan. Experiments are simulated using the CloudSim toolkit, and the comparative outcomes show that the suggested SADCTS approach reduced the makespan, cost, and energy by 5-20% more than the traditional methods. Thus, SADCTS provides less task violation of 0.0001%, high energy efficiency of 700GHz/W, high resource utilization of 92%, less cost efficiency of 72GHz/$, and less makespan of 480minutes to satisfy the necessary security and deadline requirements for TS in shared resource hybrid clouds. [ABSTRACT FROM AUTHOR]

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Cloud computing is an emerging technology composed of several key components that work together to create a seamless network of interconnected devices. These interconnected devices, such as sensors, routers, smartphones, and smart appliances, are the foundation of the Internet of Everything (IoE). Huge volumes of data generated by IoE devices are processed and accumulated in the cloud, allowing for real-time analysis and insights. As a result, there is a dire need for load-balancing and task-scheduling techniques in cloud computing. The primary objective of these techniques is to divide the workload evenly across all available resources and handle other issues like reducing execution time and response time, increasing throughput and fault detection. This systematic literature review (SLR) aims to analyze various technologies comprising optimization and machine learning algorithms used for load balancing and task-scheduling problems in a cloud computing environment. To analyze the load-balancing patterns and task-scheduling techniques, we opted for a representative set of 63 research articles written in English from 2014 to 2024 that has been selected using suitable exclusion-inclusion criteria. The SLR aims to minimize bias and increase objectivity by designing research questions about the topic. We have focused on the technologies used, the merits-demerits of diverse technologies, gaps within the research, insights into tools, forthcoming opportunities, performance metrics, and an in-depth investigation into ML-based optimization techniques. [ABSTRACT FROM AUTHOR]

The rapid growth of cloud computing has led to the widespread adoption of heterogeneous virtualized environments, offering scalable and flexible resources to meet diverse user demands. However, the increasing complexity and variability in workload characteristics pose significant challenges in optimizing energy consumption. Many scheduling algorithms have been suggested to address this. Therefore, a self-attention-based progressive generative adversarial network optimized with Dwarf Mongoose algorithm adopted Energy and Deadline Aware Scheduling in heterogeneous virtualized cloud computing (SAPGAN-DMA-DAS-HVCC) is proposed in this paper. Here, a self-attention based progressive generative adversarial network (SAPGAN) is proposed to schedule activities in a cloud environment with an objective function of makespan and energy consumption. Then Dwarf Mongoose algorithm is proposed to optimize the weight parameters of SAPGAN. Outcome of proposed approach SAPGAN-DMA-DAS-HVCC contains 32.77%, 34.83% and 35.76% higher right skewed makespan, 31.52%, 33.28% and 29.14% lower cost when analysed to the existing models, like task scheduling in heterogeneous cloud environment utilizing mean grey wolf optimization approach, energy and performance-efficient task scheduling in heterogeneous virtualized Energy and Performance Efficient Task Scheduling Algorithm, energy and make span aware scheduling of deadline sensitive tasks on the cloud environment, respectively. [ABSTRACT FROM AUTHOR]

In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches. [ABSTRACT FROM AUTHOR]

This paper establishes a multivariate function model for natural human load-carrying walking in some typical scenarios such as college equipment and material relocation by students and a large amount of identical freight relocation in commercial activities. For classified material relocation needs and constraints, we obtain the relationship between walking speed and load weight for a single person, as well as the time cost for different round trips. By establishing an integer programming model with the minimum total transportation time cost and shelf life as the objective function and the requirements of negative weight and speed as the constraint conditions, we reach the optimal item allocation methods considering time cost and shelf life. We discover that there is an approximate linear relationship between the change in natural walking speed and travel time when the load is small, thus obtaining the time cost of student transportation under different round-trip situations. The Monte Carlo simulation algorithm, which is more efficient compared with other methods such as the integer programming method, is used to obtain the optimal allocation scheme that meets the efficiency and quality requirements. The analysis methods and results can be used as guidance for task scheduling optimization for material relocation in educational organizations as well as commercial agencies. [ABSTRACT FROM AUTHOR]

In Industry 4.0, the focus of task scheduling is more on smart services such as robotic services under the paradigm of mobile edge computing, which are widely used to improve the efficiency of smart manufacturing. Existing scheduling research efforts on optimizing efficiency such as swarm algorithm, reinforcement learning, but with little real-time dynamic scheduling. Online learning and adaptation is a critical function when tackling real challenges in unpredictable and dynamically changing edge computing environments. Our goal is to propose algorithms that can meet the real-time dynamic task scheduling even reach the level of communication. We propose a Deep Reinforcement Learning (DRL)-based online real-time task scheduling method which has the new exploration and exploitation strategy using the improved sparrow search algorithm (ISSA). We replace the traditional strategy with a meta-heuristic algorithm, while introducing natural disturbances and modifying the population position update formula to converge to the optimal position. The algorithm has two layers. The first layer is scheduling generation layer, which is responsible for making decisions, and the second layer is scheduling policy update layer, which optimizes the strategy and finds the best set of hyperparameters. The experimental results show that the running time of the method to solve the problem can reach 0.0092 seconds, and the real-time performance reaches up to the millisecond communication level. At the same time other methods are compared, effectively reducing the running cost and meeting the requirements of production scheduling tasks. [ABSTRACT FROM AUTHOR]

Cloud computing is a popular paradigm that enables on-demand access to shared resources over the internet. Task scheduling is an important aspect of cloud computing that involves allocating resources to tasks in an efficient manner. The rapid growth of cloud computing has led to an increasing demand for efficient task scheduling algorithms. In cloud computing, task scheduling is critical for achieving high performance and resource utilization, while minimizing costs. However, traditional task scheduling algorithms often struggle to handle the intricacy and fluctuation in cloud computing environments. Therefore, a novel task scheduling framework called Matched Bald Eagle (MABLE) task scheduling framework for Cloud Computing to schedule tasks on Virtual Machines (VMs) in a cloud environment. The proposed framework consists of three major phases: matching, sorting and scheduling. The matching phase identifies the most suitable VMs for each task, while the sorting phase prioritizes the tasks based on their requirements and the types of VMs available. Finally, the scheduling phase uses the Enhanced Bald Eagle optimization (EBEO) algorithm in scheduling tasks on the chosen VMs. The simulated MABLE technique is proposed and its performance is compared with existing methods in terms of load balance, cost, resource utilization and makespan under two different scenarios. The outcomes demonstrate that the MABLE method outperforms existing techniques and is an efficient task scheduling framework for cloud computing. [ABSTRACT FROM AUTHOR]

Fog computing utilizes user premises resources to provide better services than traditional cloud computing. Due to the heterogeneity of fog devices, scheduling poses a challenge. This paper proposes a novel version of artificial rabbits optimization (ARO) called the Nonlinear based chaotic artificial rabbits optimization (NCARO) and utilizes NCARO for task scheduling in the fog computing environment (TSNCARO). The NCARO optimizes ARO by using chaotic and nonlinear control parameters. In the proposed method, chaotic maps are used to improve the exploratory behavior of ARO. ARO's exploratory and exploitative behaviors are also adjusted by means of a nonlinear control parameter. Three objectives are considered: service time, cost, and energy consumption. It improves performance by prioritizing tasks according to deadlines. An extensive scenario compares NCARO and TSNCARO algorithms with other algorithms. On the basis of the comparison results, the proposed algorithms achieved the best results in terms of makespan, service time, total cost, energy consumption, carbon dioxide emission rate, and percentage of deadline satisfaction. [ABSTRACT FROM AUTHOR]

An efficient task scheduling is mandatory in cloud computing for providing virtual resources used to carry out the tasks. An effective allocation of VM with the presence of diverse resource requirements, inaccurate information and uncertainties existing in the system is difficult. In this research, an effective task scheduling is done by using the fuzzy logic (FL) with best-fit-decreasing (BFD) in a cloud computing environment. The developed FL–BFD is optimized using resource usage, power, cost and time. Accordingly, the FL–BFD reallocates virtual machine (VM) in the cloud, based on the user demands. Therefore, the adaptability of FL is leveraged to handle uncertainties and imprecise information, which is helpful for an appropriate allocation of VM using BFD according to user requirements. The developed FL–BFD is analyzed using makespan, execution time, degree of imbalance, energy consumption and service level agreements (SLA) violations. The existing approaches named minimum completion time (MCT), particle swarm optimization (PSO), improved wild horse optimization with levy flight algorithm for task scheduling in cloud computing (IWHOLF-TSC), inverted ant colony optimisation (IACO), fuzzy system and modified particle swarm optimization (FMPSO), and task-scheduling using whale optimization (TSWO) are used for comparison. The makespan of FL–BFD with 1000 tasks is 9.2 ms, which is higher when compared to the IWHOLF-TSC and MCT-PSO. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is considered one of the key technologies for large-scale network services. Task scheduling helps to improve the task completion rate of MEC, by properly mapping tasks generated by devices onto MEC resources. However, the mobility of devices introduces complexities, potentially resulting in failed task offloading or unavailable task results. To tackle this issue, we propose a mobile-aware task scheduling scheme. We first model the trajectory of mobile devices and introduce a strategy for the fastest task offloading, coupled with an efficient result return method. Subsequently, to improve the task completion rate, we present a task scheduling model based on task migration and formulate the relevant problem as a Mixed Integer Non-linear Programming (MINLP) problem. To achieve a solution within a reasonable time complexity, we propose a Particle Swarm Optimization and Genetic Algorithm with a Rescheduling operator (PSOGAR). In PSOGAR, particles update their positions using a mating operator, while maintaining diversity by a mutation operator. In addition, a rescheduling operator is used to further improve the task completion rate. Finally, through simulation experiments, compare PSOGAR with state-of-the-art and classic algorithms. The experimental results show that PSOGAR can improve the task completion rate by 18–31% and can be applied to scenarios with tight task deadlines. [ABSTRACT FROM AUTHOR]

Mission planning for multi-agent autonomous systems aims to generate feasible and optimal mission plans that satisfy given requirements. In this article, we propose a tool-supported mission-planning methodology that combines (i) a path-planning algorithm for synthesizing path plans that are safe in environments with complex road conditions, and (ii) a task-scheduling method for synthesizing task plans that schedule the tasks in the right and fastest order, taking into account the planned paths. The task-scheduling method is based on model checking, which provides means of automatically generating task execution orders that satisfy the requirements and ensure the correctness and efficiency of the plans by construction. We implement our approach in a tool named MALTA, which offers a user-friendly GUI for configuring mission requirements, a module for path planning, an integration with the model checker UPPAAL, and functions for automatic generation of formal models, and parsing of the execution traces of models. Experiments with the tool demonstrate its applicability and performance in various configurations of an industrial case study of an autonomous quarry. We also show the adaptability of our tool by employing it in a special case of an industrial case study. [ABSTRACT FROM AUTHOR]

Cloud computing revolutionizes as a technology that succeeds in serving large-scale user demands. Workload prediction and scheduling tend to be factors dictating cloud performance. Forecasting the future workload in due to avoid unfair resource allocation, emerges to be a crucial inspecting feature for enhanced performance. The aforementioned issues of interest are addressed in our work by soliciting a Deep Learning driven Max-out prediction model, which efficiently forecasts the future workload by providing a balanced approach for enhanced scheduling with the Tasmanian Devil-Bald Eagle Search (TDBES) optimization algorithm. The results obtained proved that the TDBES scored efficacy in makespan with 16.75%, migration cost with 14.78%, and a migration efficiency rate of 9.36% over other existing techniques like DBOA, WACO, and MPSO, with additional error analysis of prediction performance using RMSE, MAP, and MAE, among which our contributed approach overrides traditional methods with least error. [ABSTRACT FROM AUTHOR]

Research into Dew computing environments has recently emerged as a result of the increasing prevalence and processing power of mobile and IoT devices. In these settings, even low-powered devices can share some of their computational resources with their neighbors. This paper proposes a novel approach to workflow scheduling in dew enabled cloud computing environment, called Deep Q-learning (DQN) + Chronological Geese Migration Optimization (CGMO). DQN is a deep learning-based method for scheduling workflows, while CGMO is a hybrid optimization algorithm that combines the chronological idea and the Wild Geese Migration Optimization (GMO) algorithm. The proposed approach aims to optimize multiple objectives, including predicted energy, Quality of Service (QoS), and resource usage, by scheduling workflows in the cloud. The approach also takes into account the current state of the Virtual Machine (VM) and the job. The assessment measures employed for DCGM include maximum QoS, minimum energy usage, and maximum resource utilization. The results show that DCGM achieved the highest QoS (0.865), lowest energy usage (0.0322), and highest resource utilization (1.000) compared to other approaches. [ABSTRACT FROM AUTHOR]

The exponential growth of technology and advent of the Internet of Things (IoT) paradigm have caused large volumes of data to be continuously generated from the intelligent devices. One common feature of these devices is their limited capabilities, hence, they are not able to process large volumes of generated data. However, the processing of these data in the cloud leads to high latency and high power consumption. Hence, providing services to the latency-sensitive IoT applications in the cloud is a challenging issue. Fog computing as a complement to the cloud, allows data to be processed near IoT devices. However, the resources in the fog layer are heterogeneous. Thus, the proper distribution of tasks among heterogeneous nodes while serving the task within the intended deadline is of great importance. In this paper, we have presented a task scheduling model in the fog-cloud paradigm, which formulates the task scheduling problem as a multi-objective optimization problem with the aim of minimizing service response time and the total energy consumption of the system, while considers deadline and load balancing constraints. Since the problem of task scheduling is np-hard, we have proposed a modified version of Strength Pareto Evolutionary Algorithm II (SPEA-II) with customized operators to achieve the optimal scheduling strategy. The experimental results reveal that the proposed scheme outperforms some benchmarking algorithms in terms of service response time and energy consumption. Furthermore, by optimal distribution of tasks among heterogeneous computing nodes, it leads to better resource utilization and improvement in the percentage of missed-deadline tasks. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has been widely applied to numerous areas and aspects of human life and modern society. Many such applications can be represented as directed acyclic graphs (DAG). Device-edge-cloud fusion provides a new kind of heterogeneous, distributed, and collaborative computing environment to support various MEC applications. DAG scheduling is a procedure employed to effectively and efficiently manage and monitor the execution of tasks that have precedence constraints on each other. In this paper, we investigate the NP-hard problems of DAG scheduling and energy-constrained DAG scheduling on mobile devices, edge servers, and cloud servers by designing and evaluating new heuristic algorithms. Our contributions to DAG scheduling can be summarized as follows. First, our heuristic algorithms guarantee that all task dependencies are correctly followed by keeping track of the number of remaining predecessors that are still not completed. Second, our heuristic algorithms ensure that all wireless transmissions between a mobile device and edge/cloud servers are performed one after another. Third, our heuristic algorithms allow an edge/cloud server to start the execution of a task as soon as the transmission of the task is finished. Fourth, we derive a lower bound for the optimal makespan such that the solutions of our heuristic algorithms can be compared with optimal solutions. Our contributions to energy-constrained DAG scheduling can be summarized as follows. First, our heuristic algorithms ensure that the overall computation energy consumption and communication energy consumption does not exceed the given energy constraint. Second, our algorithms adopt an iterative and progressive procedure to determine appropriate computation speed and wireless communication speeds while generating a DAG schedule and satisfying the energy constraint. Third, we derive a lower bound for the optimal makespan and evaluate the performance of our heuristic algorithms in such a way that their heuristic solutions are compared with optimal solutions. To the author’s knowledge, this is the first paper that considers DAG scheduling and energy-constrained DAG scheduling on edge and cloud servers with sequential wireless communications and overlapped communication and computation to minimize makespan. [ABSTRACT FROM AUTHOR]

In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution. This article proposes a model for task scheduling utilizing the multi-objective optimization and deep learning (DL) model. Initially, the multi-objective task scheduling is carried out by the incoming user utilizing the proposed hybrid fractional flamingo beetle optimization (FFBO) which is formed by integrating dung beetle optimization (DBO), flamingo search algorithm (FSA) and fractional calculus (FC). Here, the fitness function depends on reliability, cost, predicted energy, and makespan, the predicted energy is forecasted by a deep residual network (DRN). Thereafter, task scheduling is accomplished based on DL using the proposed deep feedforward neural network fused long short-term memory (DFNN-LSTM), which is the combination of DFNN and LSTM. Moreover, when scheduling the workflow, the task parameters and the virtual machine’s (VM) live parameters are taken into consideration. Task parameters are earliest finish time (EFT), earliest start time (EST), task length, task priority, and actual task running time, whereas VM parameters include memory utilization, bandwidth utilization, capacity, and central processing unit (CPU). The proposed model DFNN-LSTM+FFBO has achieved superior makespan, energy, and resource utilization of 0.188, 0.950J, and 0.238, respectively. [ABSTRACT FROM AUTHOR]

Data-intensive applications must be run on systems with high-performance processing and enough storage. When compared to conventional distributed systems like the data grid, cloud computing offers these features on a platform that is more flexible, scalable, and inexpensive. Moreover, retrieving data files is crucial to operate these services. Typically, accessing data causes the entire cloud workflow system to experience a bottleneck, thus significantly reducing system performance. Two key strategies that can enhance the efficiency of data-intensive applications are task scheduling and data replication. This research proposes a novel Data replication and scheduling in the cloud. Initially, the workflow management process is performed with 3 phases (1) workflow placement, (2) clustering of tasks, and (3) scheduling and replication. Initially, the workflow placement takes place. Then, the clustering of tasks is performed via an improved K-means algorithm. Finally, the tasks and datasets are replicated during the scheduling and replication phase. Further, the scheduling and replication are performed using the Self Modified Pelican Optimization Algorithm (SM-POA) based on the execution cost, migration cost, storage cost and replication. [ABSTRACT FROM AUTHOR]

Summary: The metaverse, a future digital world for living, working, learning, and interacting, is rapidly gaining significance, particularly in the domain of remote work and collaboration. This emerging digital landscape demands high‐performance, low‐latency, and scalable services to provide an immersive user experience. The current technology used in metaverse systems has some limitations, which emphasize the importance of adopting emerging technologies like Edge Computing (EC). However, as the number of users and data volume increases, it can impact both system performance and scalability of the edge‐assisted metaverse system. Additionally, the uneven distribution of edge servers can cause inconsistencies and result in high latency. To overcome these challenges, this paper proposes a dynamic edge clustering and task scheduling approach for edge‐assisted metaverse systems (DTAM) in the field of remote work and collaboration. The proposed approach addresses the challenges of high user volume and uneven resource distribution by incorporating dynamic clustering and edge server assistance to improve clustering performance. Furthermore, a Prioritized Experience Replay‐based Deep Q‐learning algorithm with state augmentation (PERDQSA) for task scheduling is introduced to improve sample efficiency and performance. The performance of the proposed DTAM is evaluated against existing techniques, and experimental results demonstrate its significant superiority in terms of specific metrics such as bandwidth, task response time, energy efficiency, and latency. The experiments demonstrated that DTAM outperforms Transformation‐based Edge Computing Deep Q‐Learning (TransEC‐DQL) in several key metrics. Specifically, DTAM achieves 28.5% reduction in latency, 13.7% reduction in response time, and 6.4% improvement in bandwidth compared to TransEC‐DQL. These results signify that DTAM can deliver a significantly enhanced user experience in the metaverse, particularly in the context of remote work and collaboration. [ABSTRACT FROM AUTHOR]

In the rapidly evolving domain of edge computing, efficient task scheduling emerges as a pivotal challenge due to the increasing complexity and volume of tasks. This study introduces a sophisticated dual-layer hybrid scheduling model that harnesses the strengths of Graph Neural Networks and Deep Reinforcement Learning to enhance the scheduling process. By simplifying task dependencies with Graph Neural Network at the upper layer and integrating Deep Reinforcement Learning with heuristic algorithms at the lower layer, this model optimally allocates tasks, significantly improving scheduling efficiency and reducing response times, particularly beneficial for logistics cloud robots operating in edge computing contexts. We validated the effectiveness of this innovative model through rigorous simulation experiments on the EdgeCloudSim platform, comparing its performance against traditional heuristic methods such as Shortest Job First, First Come First Serve and Heterogeneous Earliest Finish Time. The results confirm that our model consistently achieves superior task scheduling performance across various task volumes, effectively meeting the scheduling demands. This study demonstrates the effectiveness of integrating advanced machine learning techniques with heuristic algorithms to enhance task scheduling processes, making it particularly suitable for scenarios with high demands on response times. This approach not only facilitates more efficient task management but also aligns with the needs of modern edge computing applications, streamlining operations and boosting overall system performance. [ABSTRACT FROM AUTHOR]

The escalation of Internet of Things (IoT) devices has led to increased data generation at the network edge that has burdened the cloud infrastructure in terms of handling and processing of data. This has led to the rapid adoption of fog computing because of its ability to bring computation and storage closer to the edge and support for real-time applications and services by reducing latency. One of the foremost challenges in the fog computing arena is minimizing turnaround time. This research paper proposes a Modified Levy Flight Firefly Algorithm (MLFFA) to optimize task scheduling for fog computing environments. Specifically, the objective is to minimize the turnaround time of tasks. Moreover, genetic operators like crossover and mutation are also employed to achieve an optimal balance between exploration and exploitation. Experimental observations undertaken show that the proposed method improves the average turnaround time by 55%, 22%, and 13%, average waiting time by 59%, 45%, and 37%, average energy consumption by 19%, 7%, and 4%, and average failure rate by 50%, 28%, and 7% compared to the existing studies, namely Load Balancing and Optimization Strategy (LBOS), Technique for Resource Allocation and Management (TRAM), and Fuzzy Golden Eagle Load Balancing (FGELB), respectively. [ABSTRACT FROM AUTHOR]

Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. We finally validate the feasibility of the multi-task planning algorithm proposed by conducting experiments in an open environment, a narrow environment and a large-scale environment. Experimental results demonstrate the method's capability to find feasible collision-free and cost-optimal task access paths in diverse and complex multi-region planning scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a Q-learning-based task scheduling approach for mixed-critical application on heterogeneous multi-cores (QSMix) to optimize their main design challenges is proposed. This approach employs reinforcement learning capabilities to optimize execution time, power consumption, reliability and temperature of the heterogeneous multi-cores during task scheduling process. In QSMix, a reward function is defined to consider all target design parameters simultaneously and is tuned based on applying punishment for unwanted conditions during the learning. The learning process of QSMix is led by utilizing the defined reward function during constructing the Q-table for various execution scenarios. Afterward, the best solution is selected from the constructed Q-table based on the system's policy to achieve a near-optimal solution that meets the existing trade-offs among objectives while considering its constraints properly. To evaluate our proposed QSMix, several experiments are performed to show its effectiveness in finding appropriate solutions and its gradual behavior during learning process. Moreover, the performance of QSMix in terms of optimizing the target design parameters is compared to various related research. The results confirm that QSMix has average improvement about 9% over related studies in joint optimization of execution time, power consumption, reliability and temperature. [ABSTRACT FROM AUTHOR]

In recent decades, fog computing has played a vital role in executing parallel computational tasks, specifically, scientific workflow tasks. In cloud data centers, fog computing takes more time to run workflow applications. Therefore, it is essential to develop effective models for Virtual Machine (VM) allocation and task scheduling in fog computing environments. Effective task scheduling, VM migration, and allocation, altogether optimize the use of computational resources across different fog nodes. This process ensures that the tasks are executed with minimal energy consumption, which reduces the chances of resource bottlenecks. In this manuscript, the proposed framework comprises two phases: (i) effective task scheduling using a fractional selectivity approach and (ii) VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO). The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local exploitation. This balance enables the use of a wide range of solutions that leads to minimal total cost and makespan, in comparison to other traditional optimization algorithms. The FSCPSO algorithm's performance is analyzed using six evaluation measures namely, Load Balancing Level (LBL), Average Resource Utilization (ARU), total cost, makespan, energy consumption, and response time. In relation to the conventional optimization algorithms, the FSCPSO algorithm achieves a higher LBL of 39.12%, ARU of 58.15%, a minimal total cost of 1175, and a makespan of 85.87 ms, particularly when evaluated for 50 tasks. [ABSTRACT FROM AUTHOR]

This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm (DJS). Emphasizing on-demand resource sharing, typical to Cloud Service Providers (CSPs), the research focuses on minimizing job completion delays through efficient task allocation. Utilizing Johnson's rule from operations research, the study addresses the challenge of resource availability post-task completion. It advocates for queuing models with multiple servers and finite capacity to improve job scheduling models, subsequently reducing wait times and queue lengths. The Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences, showcasing their efficacy through comparative analysis. The research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators, ultimately positioning the proposed technique as superior to existing approaches, offering a robust framework for enhanced task scheduling and resource allocation in cloud computing. [ABSTRACT FROM AUTHOR]

In recent years, the proliferating of IoT (Internet of things)-originated applications have generated huge amounts of data, which has put enormous pressure on infrastructures such as the network cloud. In this regard, scholars have proposed an architectural model for "cloud-fog" computing, where one of the obstacles to fog computing is how to allocate computing resources to minimize network resources. A heuristic-based TDCC (Time, distance, cost and computing-power) algorithm is proposed to optimize the task scheduling problem in this heterogeneous system for genetic algorithm-based "cloud-fog" computing, including execution time, operational cost, distance and total computing power resources. The algorithm uses evolutionary genetic algorithms as a research tool to combine the advantages of cloud computing, fog computing and genetic algorithms to achieve a balance between latency, cost, link length and computing power. In the hybrid computing task scheduling, this algorithm has a better balance than TCaS algorithm which only considers a single metric; this algorithm has a better adaptation value than traditional MPSO algorithm by 2.61%, BLA algorithm by 6.92% and RR algorithm by 33.39%, respectively. The algorithm is also flexible enough to match the user's needs for high performance distance-cost-computing power, enhancing the effectiveness of the system. [ABSTRACT FROM AUTHOR]

Task scheduling in the edge computing environment poses significant challenges due to its inherent NP-hard nature. Several researchers concentrated on minimizing simple makespan, disregarding the reduction of the mean time to complete all tasks, resulting in uneven distributions of mean completion times. To address this issue, this study proposes a novel mean makespan task scheduling strategy (MMTSS) to minimize simple and mean makespan. MMTSS optimizes the utilization of virtual machine capacity and uses the mean makespan optimization to minimize the processing time of tasks. In addition, it reduces imbalance by evenly distributing tasks among virtual machines, which makes it easier to schedule batches subsequently. Using genetic algorithm optimization, MMTSS effectively lowers processing time and mean makespan, offering a viable approach for effective task scheduling in the edge computing environment. The simulation results, obtained using cloudlets ranging from 500 to 2000, explicitly demonstrate the improved performance of our approach in terms of both simple and mean makespan metrics. [ABSTRACT FROM AUTHOR]

Efficient offloading and scientific task scheduling are crucial for managing computational tasks in research environments. This involves determining the optimal location for executing a workflow task and allocating the task to computing resources to optimize performance. The challenge is to minimize completion time, energy consumption, and cost. This study proposes three methods: latency-centric offloading (LCO) for delay-sensitive applications; energy-based offloading (EBO) for energy-saving; and efficient offloading (EO) for balanced task distribution across tiers. Scheduling in this paper uses a genetic algorithm (GA) with a weighted sum objective function considering makespan, cost, and energy for internet of things-cloud-fog (IoT-fog-cloud). Comparative studies involving montage, Cybershake, and epigenomics workflows indicate that LCO excels in terms of makespan and cost but ranks the lowest in energy. EBO excels in energy efficiency, aligning closely with the base method. EO competes effectively with the base method in terms of makespan and cost but consumes more energy. This research enables the selection of the most suitable method based on the type of application and its prioritization of makespan, energy, or cost. [ABSTRACT FROM AUTHOR]

Cloud computing enables cost-effective resource sharing in hybrid clouds to tackle the problem of insufficient resources by elastically scaling the service capability based on the users’ needs. However, task scheduling (TS) in cloud environments is challenging due to deadline-based performance and security constraints. To remove the existing drawbacks based on deadline and security constraints, a Security-Aware Deadline Constraint TS (SADCTS) approach is presented using a hybrid optimization algorithm of the Modified Flying Squirrel Genetic Chameleon Swarm Algorithm (MFSGCSA). The proposed MFSGCSA is developed by integrating the genetic operators into CSA and combining it with the modified Flying Squirrel Optimization (FSO) algorithm in which the position update and global search equations are enhanced by adaptive probability factor to reduce the local optimum problem. In this SADCTS approach, the task assignment process is modeled into an NP-hard problem concerning a multi-level security model using user authentication, integrity, and confidentiality. This maximizes tasks’ completion rate and decreases the resource costs to process tasks with different deadline limitations. The optimal schedule sequence is obtained by MFSGCSA, where tasks are scheduled concurrently based on security constraints, demand, and deadlines to improve the prominence of cost, energy, and makespan. Experiments are simulated using the CloudSim toolkit, and the comparative outcomes show that the suggested SADCTS approach reduced the makespan, cost, and energy by 5-20% more than the traditional methods. Thus, SADCTS provides less task violation of 0.0001%, high energy efficiency of 700GHz/W, high resource utilization of 92%, less cost efficiency of 72GHz/$, and less makespan of 480minutes to satisfy the necessary security and deadline requirements for TS in shared resource hybrid clouds.

Abstract Task scheduling problem (TSP) is huge challenge in cloud computing paradigm as number of tasks comes to cloud application platform vary from time to time and all the tasks consists of variable length, runtime capacities. All these tasks may generated from various heterogeneous resources which comes onto cloud console directly effects the performance of cloud paradigm with increase in makespan, energy consumption, resource costs. Traditional task scheduling algorithms cannot handle these type of complex workloads in cloud paradigm. Many authors developed Task Scheduling algorithms by using metaheuristic techniques, hybrid approaches but all these algorithms give near optimal solutions but still TSP is a highly challenging and dynamic scenario as it resembles NP hard problem. Therefore, to tackle the TSP in cloud computing paradigm and schedule the tasks in an effective way in cloud paradigm, we formulated Adaptive Task scheduler which segments all the tasks comes to cloud console as sub tasks and fed these to the scheduler which is modeled by Improved Asynchronous Advantage Actor Critic Algorithm(IA3C) to generate schedules. This scheduling process is carried out in two stages. In first stage, all incoming tasks are segmented as sub tasks. After segmentation, all these sub tasks according to their size, execution time, communication time are grouped together and fed to the (ATSIA3C) scheduler. In the second stage, it checks for the above said constraints and disperse them onto the corresponding suitable processing capacity VMs resided in datacenters. Proposed ATSIA3C is simulated on Cloudsim. Extensive simulations are conducted using both fabricated worklogs and as well as realtime supercomputing worklogs. Our proposed mechanism evaluated over baseline algorithms i.e. RATS-HM, AINN-BPSO, MOABCQ. From results it is evident that our proposed ATSIA3C outperforms existing task schedulers by improving makespan by 70.49%. Resource cost is improved by 77.42%. Energy Consumption is improved over compared algorithms 74.24% in multi cloud environment by proposed ATSIA3C.

Abstract In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches.

Distributed real time system has developed into an outstanding computing platform for parallel, high-efficiency applications. A real time system is a kind of planning where tasks must be completed with accurate results within a predetermined amount of time. It is well known that obtaining an optimal assignment of tasks for more than three processors is an NP-hard problem. This article examines the issue of assigning tasks to processors in heterogeneous distributed systems with a view to reduce cost and response time of the system while maximizing system reliability. The proposed method is carried out in two phases, Phase Ⅰ provides a hybrid HPSOGAK, that is an integration of particle swarm optimization (PSO), genetic algorithm (GA), and k-means technique while Phase Ⅱ is based on GA. By updating cluster centroids with PSO and GA and then using them like initial centroids for the k-means algorithm to generate the task-clusters, HPSOGAK produces 'm' clusters of 'r' tasks, and then their assignment onto the appropriate processor is done by using GA. The performance of GA has been improved in this article by introducing new crossover and mutation operators, and the functionality of traditional PSO has been enhanced by combining it with GA. Numerous examples from various research articles are employed to evaluate the efficiency of the proposed technique, and the numerical results are contrasted with well-known existing models. The proposed method enhances PIR values by 22.64%, efficiency by 6.93%, and response times by 23.8 on average. The experimental results demonstrate that the suggested method outperforms all comparable approaches, leading to the achievement of superior results. The developed mechanism is acceptable for an erratic number of tasks and processors with both types of fuzzy and crisp time.

Cloud computing plays a vital role, which is used to access computing resources and information online. There are a lot of challenges in accessing cloud computing systems. One of the major challenges among these is resource Management which includes scheduling, allocation, and sharing. In this paper, the Hybrid Genetic Modified Whale optimization algorithm which is a combined Genetic and Modified Whale optimization algorithm to analyze the performance of the cloud computing system such as task completion time, execution cost, speedup, and efficiency with proper allocation and sharing of resources The performance of the proposed algorithm is compared with Genetic algorithm and Whale optimization algorithm. The main target of this Proposed system is to reduce the completion time of the task by increasing the speed. Cloud Sim environment tool kit is used for the testing of the proposed system.

Abstract Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. We finally validate the feasibility of the multi-task planning algorithm proposed by conducting experiments in an open environment, a narrow environment and a large-scale environment. Experimental results demonstrate the method's capability to find feasible collision-free and cost-optimal task access paths in diverse and complex multi-region planning scenarios.

An online distributed system which is helpful for users to contribute their surplus resources to handle the wide range of tasks is called volunteer computing. In Volunteer Computing System (VCS) technology, there exists heterogeneous devices which vary in terms of processing power, latency, cost, and energy efficiency. VCS is heterogeneous in nature so, it is essential to utilize all the resources to provide high Quality of Service (QoS), innovation and cost effectiveness for all requests. The dynamism and heterogeneity of VCS makes it essential to utilize all the resources for providing excellent Quality of Service(QoS). This innovation is also cost effective for all the requests. Task scheduling problems that are classified as NP-hard are very challenging in heterogeneous VCS environments. Therefore, two scheduling algorithms Min-CCV and Min-V are proposed for volunteer computing systems. The primary objective of the proposed algorithms is to improve network performance and also enhance the QoS by reducing the computational, communicational, and violational costs for various Internet of Things (IoT) based applications which in turn improves the quality of life. A task scheduler module allocates tasks Ti as a group of n jobs that are identified T1, T2, T3,…….,Tn for a volunteer computing system. These tasks have specific attributes like memory requirement, input and output file sizes, QoS requirements, etc. The outcome of the simulator describes that the proposed algorithm can allocate tasks in a more efficient way to the volunteer fog-cloud environment in comparison to existing methods. As compared to the generic-based algorithms, the proposed Min-CCV and Min-V algorithms enhance the deadline satisfaction rate to approximately 99.5% and decrease the costs incurred by 15 to 53%. The comprehensive simulation results give the better outcome of proposed work as compared to existing practices.

The trade-off between energy efficiency and resource utilization incurred by the real-time tasks scheduling strategy in a cloud computing environment is still a challenging issue because of the large scale of heterogeneous tasks and physical machines. In this paper, we propose an energy-efficient scheduling framework for cloud–edge–end architecture, which includes two-level internal schedulers, a task dispatcher and a group migrator, based on a multi-queue model. The aim of the proposed scheduling framework is to achieve improvement in energy efficiency and resource utilization without reducing task schedulability obviously during the execution of a large number of real-time tasks. To achieve this, the framework keeps tasks in a pending queue and divides these waiting tasks into independent groups with the same execution speed, and then dispatches and migrates them to the suitable machines that have the optimal energy efficiency and resource utilization. We conduct extensive simulate experiments under various workload conditions and evaluate our scheduling strategy using eight performance metrics. The experimental results show that our scheduling strategy can improve the performance metrics when compared with the competing algorithms. [ABSTRACT FROM AUTHOR]

Task scheduling in cloud computing systems is an important and challenging NP-Hard problem that involves the decision to allocate resources to tasks in a way that optimizes a performance metric. The complexity of this problem rises due to the size and scale of cloud systems, the heterogeneity of cloud resources and tasks, and the dynamic nature of cloud resources. Metaheuristics are a class of algorithms that have been used effectively to solve NP-Hard cloud scheduling problems (CSP). Bonobo optimizer (BO) is a recent metaheuristic-based optimization algorithm, which mimics several interesting reproductive strategies and social behaviour of Bonobos and has shown competitive performance against several state-of-the-art metaheuristics for many optimization problems. Besides its good performance, it still suffers from inherent deficiencies such as imbalanced exploration-exploitation and trapping in local optima. This paper proposes a modified version of the BO algorithm called mBO to solve the cloud scheduling problem to minimize two important scheduling objectives; makespan and energy consumption. We have incorporated four modifications namely Dimension Learning-Based Hunting (DLH) search strategy, (2) Transition Factor (TF), (3) Control Randomization (DR), and 4) Control Randomization Direction in the traditional BO to improve the performance, which helps it to escape local optima and balance exploration-exploitation. The efficacy of mBO is initially tested on the popular standard CEC'20 benchmarks followed by its application on the CSP problem using real-world supercomputing workloads namely CEA-Curie and HPC2N. Results and observations reveal the supremacy of the proposed mBO algorithm over many contemporary metaheuristics by a competitive margin for both CEC'20 benchmarks and the CSP problem. Quantitatively for the CSP problem, mBO was able to reduce makespan and energy consumption by 8.20–23.73% and 2.57–11.87%, respectively against tested algorithms. For HPC2N workloads, mBO achieved a makespan reduction of 10.99–29.48% and an energy consumption reduction of 3.55–30.65% over the compared metaheuristics. [ABSTRACT FROM AUTHOR]

Various resources as the essential elements of data centers, and their utilization is vital to resource managers. In terms of the persistence, the periodicity and the spatial-temporal dependence of stream workload, a new Storm scheduler with Advantage Actor-Critic is proposed to improve resource utilization for minimizing the completion time. A new weighted embedding with a Graph Neural Network is designed to depend on the features of a job comprehensively, which includes the dependence, the types and the positions of tasks in a job. An improved Advantage Actor-Critic integrating task chosen and executor assignment is proposed to schedule tasks to executors in order to better resource utilization. Then the status of tasks and executors are updated for the next scheduling. Compared to existing methods, experimental results show that the proposed Storm scheduler improves resource utilization. The completion time is reduced by almost 17% on the TPC-H data set and reduced by almost 25% on the Alibaba data set. [ABSTRACT FROM AUTHOR]

Heterogeneous multi-core processor has the ability to switch between different types of cores to perform tasks, which provides more space and possibility for realizing efficient operation of computer system and improving computer computing power. Current research focuses on heterogeneous multiprocessor systems with high performance or low power consumption to reduce system energy consumption. However, some studies have shown that excessive voltage reduction may lead to an increase in transient failure rates, reducing system reliability. This paper studies the energy optimal scheduling problem of HMSS with DVFS under the constraints of minimum time and reliability, and proposes an improved wild horse optimization algorithm (OIWHO), which improves the efficiency of heterogeneous task scheduling and shortens the task completion time. The algorithm uses the learning and chaos perturbation strategies based on opposition and crossover strategies to balance the search and utilization capabilities, and can further improve the performance of OIWHO. Compared with previous work, our proposed algorithm has more advantages than existing algorithms. Experimental results show that the average computing time of OIWHO algorithm is 12.58%, 11.42%, 7.53%, 4.20% and 3.21% faster than DRNN-BWO, PSO, GWO-GA, GACSH and OIWOAH, respectively. Especially when solving large-scale problems, our algorithm takes less time than other algorithms. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging technology incorporating various hardware devices and software applications to exchange, analyze, and process a huge amount of data. IoT uses cloud and fog infrastructures, comprising different hardware and software components like computing machines, networking components, storage, and virtualization elements. They can receive, process, store, and exchange data in real time. A cloud is a centralized system containing large data centres that are far from client devices. However, as IoT generates massive amounts of data, issues like latency, response time, execution of tasks within their deadline, and bandwidth arise when data is sent to the cloud for processing. Compared to the cloud, fog computing is vital as a distributed system consisting of millions of devices located at the minimum distance from the client devices. In addition, fog infrastructure reduces bandwidth and latency because it is closer to the end-user. However, maximizing utilization of resources, minimizing response time, and ensuring the completion of deadline-constrained tasks within their deadline are important research problems in fog computing. This research proposes a task scheduling technique called Resource Aware Prioritized Task Scheduling (RAPTS) in a heterogeneous fog computing environment. The aim is to execute deadline-constrained tasks within their deadlines, minimize response time and cost, as well as makespan, and maximize resource utilization of the fog layer. The RAPTS is implemented using iFogSim and its performance is evaluated regarding response time, resource utilization, task deadlines, cost, and makespan. The results have been compared with state-of-the-art fog schedulers like RACE (CFP) and RACE (FOP). The results reveal that the RAPTS have shown up to 29%, 53%, 15%, 11%, and 43% improvement in terms of resource utilization, response time, makespan, cost, and meeting task deadlines, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as an efficient distribution platform in modern distributed computing offering scalability and flexibility. Task scheduling is considered as one of the main crucial aspects of cloud computing. The primary purpose of the task scheduling mechanism is to reduce the cost and makespan and determine which virtual machine (VM) needs to be selected to execute the task. It is widely acknowledged as a nondeterministic polynomial-time complete problem, necessitating the development of an efficient solution. This paper presents an innovative approach to task scheduling and allocation within cloud computing systems. Our focus lies on improving both the efficiency and cost-effectiveness of task execution, with a specific emphasis on optimizing makespan and resource utilization. This is achieved through the introduction of an Advanced Max–Min Algorithm, which builds upon traditional methodologies to significantly enhance performance metrics such as makespan, waiting time, and resource utilization. The selection of the Max–Min algorithm is rooted in its ability to strike a balance between task execution time and resource utilization, making it a suitable candidate for addressing the challenges of cloud task scheduling. Furthermore, a key contribution of this work is the integration of a cost-aware algorithm into the scheduling framework. This algorithm enables the effective management of task execution costs, ensuring alignment with user requirements while operating within the constraints of cloud service providers. The proposed method adjusts task allocation based on cost considerations dynamically. Additionally, the presented approach enhances the overall economic efficiency of cloud computing deployments. The findings demonstrate that the proposed Advanced Max–Min Algorithm outperforms the traditional Max–Min, Min–Min, and SJF algorithms with respect to makespan, waiting time, and resource utilization. [ABSTRACT FROM AUTHOR]