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In cloud computing, task scheduling and resource allocation are the two core issues of the IaaS layer. Efficient task scheduling algorithm can improve the matching efficiency between tasks and resources. In this paper, an enhanced heterogeneous earliest finish time based on rule (EHEFT-R) task scheduling algorithm is proposed to optimize task execution efficiency, quality of service (QoS) and energy consumption. In EHEFT-R, ordering rules based on priority constraints are used to optimize the quality of the initial solution, and the enhanced heterogeneous earliest finish time (HEFT) algorithm is used to ensure the global performance of the solution space. Simulation experiments verify the effectiveness and superiority of EHEFT-R. [ABSTRACT FROM AUTHOR]

Cloud computing (CC) environment delivers the services requested by the computing devices over the internet. In recent years with the internet epoch, the cloud computing environment is progressed as a significant distributed platform. However, the prime concern associated with the CC environment as task scheduling. Several algorithms were proposed with the objective of optimizing the scheduling process in the CC environment. To fill this gap in this work an integrated Fisher linear discriminant and discrete global swarm-based task scheduling (FLD-DGSTS) method is proposed. Fisher linear discriminant independent task prioritizer algorithm is employed to accurately enhance the quality of solution with minimum makespan and memory. Next, the discrete glowworm swarm optimization process is applied for scheduling cloud user tasks. The fitness function (i.e., CPU cycles, bandwidth, memory, and energy) is measured for addressing optimization and avoiding local convergence. The experimental evaluation of the proposed FLD-DGSTS method is compared to existing task scheduling algorithms with the CloudSim toolkit. The results demonstrate that the FLD-DGSTS method gives better performance with reduces the makespan and memory and ensures high scheduling efficiency than the state-of-the-art task scheduling methods. [ABSTRACT FROM AUTHOR]

Recently, wide-area distributed computing environments have become popular owing to their huge resource capability. In a wide-area distributed computing environment, joint scheduling of tasks and data is the main strategy to improve system performance. However, the geographically distributed diverse resources exhibit high variations, making it challenging to design efficient joint scheduling of tasks and data. To accurately adapt to the dynamic variations of geographically distributed diverse resources and achieve a high system performance, this study proposes a hypergraph-partitioning-based online joint scheduling method. The proposed method constructs a hypergraph of geographically distributed tasks, data, and diverse resources to clearly describe the correlation among the three elements and quantitatively reflect the time cost of different process in the environment. The hypergraph is dynamically updated according to the generated scheduling scheme and the collected information to reflect the dynamic variations of resource states. Then, a hypergraph partition optimization mechanism is proposed to generate efficient joint scheduling schemes, thus reducing the overall completion time in the system. The experimental results indicate that compared with the state-of-the-art joint scheduling methods, the proposed method reduces the overall completion time by up to 25.67% and significantly reduces the task waiting time, although it makes a concession in the data migration time. [ABSTRACT FROM AUTHOR]

Cloud service providers acquire the computing resources and allocate them to their clients. To effectively utilize the resources and achieve higher user satisfaction, efficient task scheduling algorithms play a very pivotal role. A number of task scheduling technique have been proposed in the literature. However, majority of these scheduling algorithms fail to achieve efficient resource utilization that causes them to miss tasks deadlines. This is because these algorithms are not resource and deadline-aware. In this research, a Resource and deadline Aware Dynamic Load-balancer (RADL) for Cloud, tasks have been presented. The proposed scheduling scheme evenly distribute the incoming workload of compute-intensive and independent tasks at run-time. In addition, RADL approach has the capability to accommodate the newly arrived tasks (with shorter deadlines) efficiently and reduce task rejection. The proposed scheduler monitors/updates the task and VM status at run-time. Experimental results show that the proposed technique has attained up to 67.74%, 303.57%, 259.2%, 146.13%, 405.06%, and 259.14% improvement for average resource utilization, meeting tasks deadlines, lower makespan, task response time, penalty cost, and task execution cost respectively as compared to the state-of-the-art tasks scheduling heuristics using three benchmark datasets. [ABSTRACT FROM AUTHOR]

These days, the usage of the internet of Vehicle Things (IVoT) applications such as E-Business, E-Train, E-Ambulance has been growing progressively. These applications require mobility-aware delay-sensitive services to execute their tasks. With this motivation, the study has the following contribution. Initially, the study devises a novel cooperative vehicular fog cloud network (VFCN) based on container microservices which offers cost-efficient and mobility-aware services with rich resources for processing. This study devises the cost-efficient task offloading and scheduling (CEMOTS) algorithm framework, which consists of the mobility aware task offloading phase (MTOP) method, which determines the optimal offloading time to minimize the communication cost of applications. Furthermore, CEMOTS offers Cooperative Task Offloading Scheduling (CTOS), including task sequencing and scheduling. The goal is to reduce the application costs of communication cost and computational costs under a given deadline constraint. Performance evaluation shows the CTOS and MTOP outperform existing task offloading and scheduling methods in the VCFN in terms of costs and the deadline for IoT applications. [ABSTRACT FROM AUTHOR]

Cloud computing is very popular because of its unique features such as scalability, elasticity, on-demand service, and security. A large number of tasks are performed simultaneously in a cloud system, and an effective task scheduler is needed to achieve better efficiency of the cloud system. Task scheduling algorithm should determine a sequence of execution of tasks to meet the requirements of the user in terms of Quality of Service (QoS) factors (e.g., execution time and cost). The key issue in recent task scheduling is energy efficiency since it reduces cost and satisfies the standard parameter in green computing. The most important aim of this paper is a comparative analysis of 67 scheduling methods in the cloud system to minimize energy consumption during task scheduling. This work allows the reader to choose the right scheduling algorithm that optimizes energy properly, given the existing problems and limitations. In addition, we have divided the algorithms into three categories: heuristic-based task scheduling, meta-heuristic-based task scheduling, and other task scheduling algorithms. The advantages and disadvantages of the proposed algorithms are also described, and finally, future research areas and further developments in this field are presented. [ABSTRACT FROM AUTHOR]

Tasks have high requirements for response delay and security in intelligent manufacturing. Industrial data have the characteristics of high privacy. However, cloud services are difficult to implement for low latency-sensitive applications and privacy data tasks. Therefore, the offloading technology in edge computing can offload the computing tasks of terminal devices to the edge of the network, which can effectively reduce the delay and match the needs of intelligent manufacturing. Unreasonable task scheduling cannot meet the needs of real-time scheduling between edge servers and cloud servers. In this paper, we establish a joint low-delay optimization model of task scheduling and dynamic replacement-release caching (DRRC) mechanism, which couples a privacy selection strategy for tasks to protect privacy. Tasks are scheduled to different location by the privacy of sensitive data, which can improve the security of data and meet the calculation request of different tasks. DRRC mechanism caches tasks according to the size of the task and replaces it with the weight of the task data, and adds automatic release mechanism. To solve the task scheduling strategy, we design the improved genetic-differential evolution algorithm. Extensive simulations reveal that the proposed algorithm has a better performance in minimizing latency compared with other scheduling algorithms. At the same time, the caching mechanism has a better hit rate. [ABSTRACT FROM AUTHOR]

Cloud computing is an Internet-provisioned computing paradigm that provides scalable resources for the execution of the end user's tasks. The cloud users lease optimal resources that meet their demands with minimum cost and time. The cloud service providers need high utilization of cloud resources and minimized execution cost. To achieve high user satisfaction and improve utilization of cloud resources, the task scheduling techniques should be resource and deadline aware and distribute the workload in a balanced manner. A number of heuristic and meta-heuristic-based task scheduling approaches have been proposed; however, the majority of these approaches are not resource and deadline aware. Moreover, these schedulers either optimize a single objective or multiple objectives with non-conflicting parameters. However, there is a need for schedulers that can provide a balanced solution for conflicting parameters like time and cost. In this paper, a modified and adaptive PSO-based resource- and deadline-aware dynamic load-balanced (PSO-RDAL) algorithm is proposed. The PSO-RDAL scheduling technique aims to provide an optimized solution for the workload of independent and compute-intensive tasks with reasonable time and cost. Moreover, the proposed approach also supports multi-objective-based optimization with conflicting parameters like time and cost. The experimental results reveal that the PSO-RDAL has gained up to 66%, 162%, 56%, 89%, 98%, and 97% enhancement in terms of makespan, average resource utilization, task response time, meeting task deadline, penalty cost, and total execution cost, respectively, as compared to existing state-of-the-art tasks scheduling heuristics. [ABSTRACT FROM AUTHOR]

The central cloud facilities based on virtual machines offer many benefits to reduce the scheduling costs and improve service availability and accessibility. The approach of cloud computing is practical due to the combination of security features and online services. In the tasks transfer, the source and target domains have differing feature spaces. This challenge becomes more complicated in network traffic, which leads to data transfer delay, and some critical tasks could not deliver at the right time. This paper proposes an efficient optimization method for task scheduling based on a hybrid multi-verse optimizer with a genetic algorithm called MVO-GA. The proposed MVO-GA is proposed to enhance the performance of tasks transfer via the cloud network based on cloud resources' workload. It is necessary to provide adequate transfer decisions to reschedule the transfer tasks based on the gathered tasks' efficiency weight in the cloud. The proposed method (MVO-GA) works on multiple properties of cloud resources: speed, capacity, task size, number of tasks, number of virtual machines, and throughput. The proposed method successfully optimizes the task scheduling of a large number of tasks (i.e., 1000–2000). The proposed MVO-GA got promising results in optimizing the large cloud tasks' transfer time, which reflects its effectiveness. The proposed method is evaluated based on using the simulation environment of the cloud using MATLAB distrusted system. [ABSTRACT FROM AUTHOR]

The cloud runtime environment is dynamic; therefore, allocating tasks to computing resources might include various scenarios. Metaheuristic algorithms are usually used to choose appropriate scheduling scenarios; however, they suffer from premature convergence, trapping in local optima, and imbalance between the exploration and exploitation of search space. The multi-verse optimizer (MVO) algorithm also suffers from similar problems. In this research, both Gabor filter and opposition-based learning methods are applied in the MVO algorithm to present the new algorithm GOMVO. The multi-tracker optimization (MTO) is applied in the GOMVO to present the new MTO-GOMVO hybrid algorithm. Then the scheduling framework MTOA-GOMVO@DSF is presented that applies the MTO-GOMVO metaheuristic algorithms in cloud computing scheduling. In the sequel, at first, the GOMVO algorithm is benchmarked applying CEC2017 benchmark functions and compared with several baseline algorithms in terms of mean error. Second, MTOA-GOMVO is also evaluated against related baseline algorithms in terms of mean error. Finally, MTOA-GOMVO is also applied in cloud computing to schedule independent tasks to virtual machines to improve average execution time, response time, throughput, and SLA violations. Simulation results applying NASA-iPSC real dataset showed that MTOA-GOMVO outweighs the baseline metaheuristic algorithms and performs well in scheduling cloud computing tasks. [ABSTRACT FROM AUTHOR]

In order to realize globalization of cloud computing, joint use of different services of different cloud providers has become an inevitable trend. The geo-distributed cloud consists of several different clouds, providing a general environment for cloud computing. In data placement, many recently proposed data placement algorithms unilaterally use a single performance index to evaluate the performance of the algorithm. In task scheduling, when tasks are allocated with excess cloud resources, resources are wasted. When little cloud resources are allocated to the complex task, cause the overall progress of the system to stagnate, the overall progress of the system is stalled. For solving the above problems, the data placement method and the task scheduling method are proposed. In the proposed data placement scheme, multiple performance indicators are considered. The detection of the straggling nodes and the reasonable allocation of cloud resources are taken into account when the task is scheduled. For proving the superiority of the proposed methods, extensive experiments are conducted. In terms of the data placement, when the number of files is set as 800, the safety level of the proposed data placement algorithm is 7.0, which is 27.3% higher than that of the IDP algorithm, 45.8% higher than that of the GA-DPSO algorithm and 16.7% higher than that of the H2DP algorithm. As for the task scheduling, the percentage improvement in the time overhead of the proposed task scheduling method is the lowest, which implies that the time overhead of the proposed task scheduling algorithm is closest to the optimal time and is the shortest. [ABSTRACT FROM AUTHOR]

Cloud computing will continue to play a critical role due to its ability to deliver various types of on-demand information technology (IT) services according to customer needs. Unfortunately, cloud computing has faced many challenges. The task scheduling problem is considered one of the main challenges because the user has to pay for a resource. Therefore, a good mapping of users' tasks on the available resources is required to reduce the total execution time of the users' tasks (i.e., reduce makespan), execution cost, enhance load balancing, increase resource utilization, etc. Many heuristic algorithms have been existed to solve task scheduling problems. Although, the Heterogeneous Earliest Finish Time (HEFT) heuristic algorithm is considered one of the best task scheduling algorithms in a heterogeneous environment, it does not concern load balancing. In this paper, a modification has been introduced to the HEFT algorithm to satisfy load balancing. The modified algorithm is called Load Balancing HEFT (LB-HEFT). A comparative study has been done among the proposed LB-HEFT, the Enhancement Heterogeneous Earliest Finish Time (E-HEFT), and HEFT algorithms to evaluate the performance of the proposed LB-HEFT algorithm. According to the comparative results, it is found that the proposed LB-HEFT algorithm outperforms the existing E-HEFT and HEFT algorithms by improving load balancing by 43.49% and 72.59%, respectively in average, enhancing resource utilization by 2.28% and 5.61%, respectively in average, and reducing makespan by 7.55%, and 3.75% respectively in average. [ABSTRACT FROM AUTHOR]

Recent advancements of virtualization technologies for parallel processing involve scheduling containerized tasks in a workflow. Since a container can include multiple tasks, it can be reused or shared among applications. If every task in a workflow uses its dedicated container without sharing among any tasks, each container image must be downloaded for each task. As a result, many computational resources are required to process and the communication latency related to container image downloading can become a bottleneck for the makespan. In task scheduling algorithms for workflows, this characteristic produces a new challenging issue that how effectively shares containers among tasks to avoid redundant container image download processes and redundant task allocations. One of the fundamental problems is that no policy has been established for simultaneously satisfying effective container sharing, maintaining the degree of task parallelism, and effective computational resource utilization. In this paper, we propose a clustering-based containerized task scheduling algorithm for clouds, namely, shareable functional task clustering for utilizing virtualized resources (SF-CUV). The objective of SF-CUV is to minimize the makespan with less computational resources and containers than other algorithms by clustering tasks and sharing each container among tasks. SF-CUV consists of two phases: (i)task clustering and pre-virtual CPU (vCPU) allocation phase to derive an accurate scheduling priority, and (ii)task ordering and actual task reallocation phase. Experimental results obtained via simulation and in a real environment show that SF-CUV can utilize both vCPUs and containers with a shorter makespan compared with other approaches. [ABSTRACT FROM AUTHOR]

Industrial manipulators should be able to execute difficult tasks in the minimum cycle time in order to increase performance in a robotic work cell. This paper is focused on determining the near optimum route of a manipulator's end-effector which is requested to reach a predefined set of demand points in a robotic work cell. Two subproblems are related with this goal: the motion planning problem and the task scheduling problem. A new approach is presented in this paper for simultaneously planning collision-free motion and scheduling time near optimum route along the demand points. A combination of a geometrical approach and an adaptive neuro-fuzzy system is employed to consider the multiple manipulator's configurations, while a special genetic algorithm is designed to solve the derived optimization problem. The experiments show that the proposed method has the capacity to determine both the near optimum manipulator configurations and the near optimum sequence of demand points. [ABSTRACT FROM AUTHOR]

Cloud computing is a scalable computing paradigm that provides computing services to users over the Internet. To achieve high user satisfaction and improve the performance of the Cloud, a balanced distribution of users tasks plays a vital role. In the literature, a number of task scheduling and load-balancing schemes have been proposed. However, the majority of these scheduling heuristics focus on a single evaluation parameter (i.e., makespan or resource utilization, etc.) as a scheduling objective. Improving one parameter may not guarantee an increase in the overall performance of the Cloud. There is a need to have such algorithms that focus on improving the overall performance of the Cloud by taking into account multiple evaluation parameters. In this paper, an Overall Performance-based Resource-aware Dynamic Load-balancer (OG-RADL) for deadline constrained Cloud tasks is proposed. OG-RADL has the ability to distribute the workload of independent and compute-intensive tasks according to the resource computation capability at run time. Moreover, a novel normalization technique is proposed that overcome the limitations of existing normalization techniques. The OG-RADL enhance load balancing, support deadline constrained tasks, and improve the overall performance gain of the Cloud. The experimental result shows that the proposed approach OG-RADL outperforms as compared to existing task scheduling algorithms named DLBA, DC-DLBA, Dy-MaxMin, RALBA, PSSELB, and MODE in terms of the overall performance of the Cloud. [ABSTRACT FROM AUTHOR]

A high-performance execution of programs predominately depends on the efficient scheduling of tasks. An application consists of a sequence of tasks that can be represented as a directed acyclic graph (DAG). The tasks in the DAG have precedence constraints between them and each task has a different timeline on different processors. In this paper, a new list-based scheduling algorithm is proposed which schedules the tasks which are represented as a DAG structure. The main focus of this algorithm is to schedule the tasks to the suitable processing node in fog environment as the fog nodes have limited processing capacity. The assignment of tasks on the fog node should consider both the computation cost of the node and the execution finishing time of the node. The proposed algorithm has three phases. (1) the level sorting phase, where the independent tasks are identified (2) in the Task prioritization phase the proposed algorithm assigns priority to the task which has more successors so that more tasks in the next level can start their execution and (3) in the task selection phase a balanced combination of local optimal and global optimal approach is considered to assign a task to a suitable processor which further enhances the processor selection phase results in minimizing both the makespan and overall computation cost of the processors. Extensive experiments are carried out using randomly generated graphs and graphs from the real-world to analyze the performance of the proposed algorithm. The results show that the proposed algorithm outperforms all other well-known algorithms like predict earliest finish time, heterogeneous earliest finish time algorithm, minimal optimistic processing time, and SDBBATS in terms of performance matrices like average scheduling length ratio, speedup, and makespan. [ABSTRACT FROM AUTHOR]

In cloud computing, task scheduling plays a major role and the efficient schedule of tasks can increase the cloud system efficiency. To successfully meet the dynamic requirements of end-users' applications, advanced scheduling techniques should be in place to ensure optimal mapping of tasks to cloud resources. In this paper, a modified Henry gas solubility optimization (HGSO) is presented which is based on the whale optimization algorithm (WOA) and a comprehensive opposition-based learning (COBL) for optimum task scheduling. The proposed method is named HGSWC. In the proposed HGSWC, WOA is utilized as a local search procedure in order to improve the quality of solutions, whereas COBL is employed to improve the worst solutions by computing their opposite solutions and then selecting the best among them. HGSWC is validated on a set of thirty-six optimization benchmark functions, and it is contrasted with conventional HGSO and WOA. The proposed HGSWC has been proved to perform better than the comparison algorithms. Moreover, the performance of HGSWC has also been tested on a set of synthetic and real workloads including fifteen different task scheduling problems. The results obtained through simulation experiments demonstrate that HGSWC finds near optimal solutions with no computational overhead as well as outperforms six well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

The shift minimization personnel task scheduling problem is an NP-complete optimization problem that concerns the assignment of tasks to multi-skilled employees with a view to minimize the total number of assigned employees. Recent literature indicates that hybrid methods which combine exact and heuristic techniques such as matheuristics are efficient as regards to generating high quality solutions. The present work employs a constructive matheuristic (CMH): a decomposition-based method where sub-problems are solved to optimality using exact techniques. The optimal solutions of sub-problems are subsequently utilized to construct a feasible solution for the entire problem. Based on the study, a time-based CMH has been developed which, for the first time, solves all the difficult instances introduced by Smet et al. (Omega 46:64–73, 2014) to optimality. In addition, an automated CMH algorithm that utilizes instance-specific problem features has also been developed that produces high quality solutions over all current benchmark instances. [ABSTRACT FROM AUTHOR]

Due to different heterogeneous cloud resources and diverse and complex applications of the users, an optimal task scheduling, which can satisfy users and cloud service providers with energy-saving and cost-effective use of resources, is a major issue in cloud computing. On the one hand, network users are demanding the quality assurance of their requested services, minimizing their costs, and their own data security, and on the other hand, the service providers consider less power consumption, more efficient use of resources, and optimal utilization. In dependent tasks dealing with massive data, resource scheduling is considered as an important challenge. Due to the time limitation of online scheduling process of dependent tasks, many existing methods of the literature are not able to guarantee the best resource utilization. In this paper, a reinforcement learning approach is exploited in a multi-agent system for task scheduling and resource provisioning, in order to reduce the makespan, minimize the required power, optimize the cost of using the resources, and maximize the utilization of the resources (considering their expiration time), simultaneously. The proposed algorithm has two phases. In the first phase, the tasks are scheduled using reinforcement learning techniques, and in the second one, considering the information obtained from the scheduling phase, resources are allocated in a multi-agent environment. The results of experiments show that this method improves the efficiency of the use of resources and reduces their costs. Moreover, the expiration time of the tasks is observed and the total execution time and energy consumption will be significantly reduced. [ABSTRACT FROM AUTHOR]

With the increasing and elastic demand for cloud resources, finding an optimal task scheduling mechanism become a challenge for cloud service providers. Due to the time-varying nature of resource demands in length and processing over time and dynamics and heterogeneity of cloud resources, existing myopic task scheduling solutions intended to maximize the performance of task scheduling are inefficient and sacrifice the long-time system performance in terms of resource utilization and response time. In this paper, we propose an optimal solution for performing foresighted task scheduling in a cloud environment. Since a-priori knowledge from the dynamics in queue length of virtual machines is not known in run time, an online reinforcement learning approach is proposed for foresighted task allocation. The evaluation results show that our method not only reduce the response time and makespan of submitted tasks, but also increase the resource efficiency. So in this thesis a scheduling method based on reinforcement learning is proposed. Adopting with environment conditions and responding to unsteady requests, reinforcement learning can cause a long-term increase in system's performance. The results show that this proposed method can not only reduce the response time and makespan but also increase resource efficiency as a minor goal. [ABSTRACT FROM AUTHOR]

The rapid evolution of Internet of Things and cloud computing have endorsed a novel computing paradigm called edge computing. Here tasks are processed by edge devices before sent to the cloud to reduce the computational latency and overhead of cloud server. In edge computing efficient classification and distribution of the tasks among the constituent nodes is a challenging issue because of their resource limitedness and heterogeneity. In this paper a novel scheme named KTCS (K-means Clustering-based Task Classification and Scheduling) is proposed which classifies the task based on the type of resource requirement in terms of CPU, I/O, or COMM before distributed to the edge node. Using the K-means algorithm modeled with the M/M/c queuing theory, the proposed scheme efficiently schedules and assigns the task so that the utilization of the edge devices can be increased. The simulation result reveals that the proposed scheme significantly improves the performance of edge nodes in terms of task execution time and resource utilization. [ABSTRACT FROM AUTHOR]

The current task scheduling is separated from the virtual machine (VM) allocation, which, to some extent, wastes resources or degrades application performance. The scheduling algorithm influences the demand of VMs in terms of service-level agreement, while the number of VMs determines the performance of task scheduling. Workload plays an indispensable role in both dynamic VM allocation and task scheduling. To address this problem, we coordinate task scheduling and VM allocation based on workload characteristics. Workload is empirically time-varying and stochastic. We demonstrate that the acquired workload data set has Markov property which can be modeled as a Markov chain. Then, three workload characteristic operators are extracted: persistence, recurrence and entropy, which quantify the relative stability, burstiness, and unpredictability of the workload, respectively. Experiments indicate that the persistence and recurrence of workloads has a direct bearing on the average response time and resource utilization of the system. A nonlinear model between the load characteristic operators and the number of VMs is established. In order to test the performance of the collaborative framework, we design a scheduling algorithm based on genetic algorithm (GA), which takes the estimated number of VMs as input and the task completion time as the optimization target. Simulation experiments have been performed on the CloudSim platform, testifying that the estimated average absolute VMs error is only 2.6%. The GA-based task scheduling algorithm could improve resource utilization and reduce task completion time compared with the first come first serve and greedy algorithm. The proposed coordination mechanism in this paper has proved able to find the optimal match and reduce the resource cost by utilizing the interaction between VM allocation and task scheduling. [ABSTRACT FROM AUTHOR]

Cloud computing is an emerging paradigm that offers various services for both users and enterprisers. Scheduling of user tasks among data centers, host and virtual machines (VMs) becomes challenging issues in cloud due to involvement of vast number of users. To address such issues, a new multi-criteria approach i.e., technique of order precedence by similarity to ideal solution (TOPSIS) algorithm is introduced to perform task scheduling in cloud systems. The task scheduling is performed in two phases. In first phase, TOPSIS algorithm is applied to obtain the relative closeness of tasks with respect to selected scheduling criteria (i.e., execution time, transmission time and cost). In second phase the particle swarm optimization (PSO) begins with computing relative closeness of the given three criteria for all tasks in all VMs. A weighted sum of execution time, transmission time and cost used as an objective function by TOPSIS to solve the problem of multi-objective task scheduling in cloud environment. The simulation work has been done in CloudSim. The performance of proposed work has been compared with PSO, dynamic PSO (DPSO), ABC, IABC and FUGE algorithms on the basis of MakeSpan, transmission time, cost and resource utilization. Experimental results show approximate 75% improvement on average utilization of resources than PSO. Processing cost of TOPSIS–PSO reduced at approximate 23.93% and 55.49% than IABC and ABC respectively. The analysis also shows that TOPSIS–PSO algorithm reduces 3.1, 29.1 and 14.4% MakeSpan than FUGE, ant colony optimization (ACO) and multiple ACO respectively. Plotted graphs and calculated values show that the proposed work is very innovative and effective for task scheduling. This TOPSIS method to calculate relative closeness for PSO has been remarkable. [ABSTRACT FROM AUTHOR]

Load balancing is the process of distributing customer tasks among multiple computing resources, such as virtual machines (VMs), servers and networks. It is a major concern in cloud computing as the number of customers demanding the service is growing exponentially. An efficient load balancing approach can monitor the load of the VMs proactively and assigns the customer tasks to the VMs accordingly. This paper presents a load balanced task scheduling algorithm in the cloud, which is based on probability theory. The proposed algorithm is shown to be a 2-approximation algorithm with a time complexity of O(lm), where l is the number of customer tasks and m is the number of VMs. The algorithm is simulated extensively. The simulation results demonstrate that our proposed algorithm can remarkably balance the load of the VMs as compared to the existing algorithms in four different performance measures, namely standard deviation of VM loads, maximum load, minimum load and zero load. The performance is also validated through statistical test by means of analysis of variance and 95% confidence interval. [ABSTRACT FROM AUTHOR]

As an emerging business computing model, cloud computing needs to deal with the scientific workflow submitted by user groups. How to efficiently schedule massive tasks of scientific workflow is an important problem in cloud computing. In order to minimize the total execution time of workflow, reduce the consume of cloud resources, reduce execution costs of users, a new task scheduling algorithm based on task duplication and task grouping is proposed in this paper. The new algorithm is composed of four steps. Firstly, the join nodes are duplicated, a DAG is converted into an in-tree graph, then all tasks are divide into task groups, it reduces communication overhead between tasks; then some task groups are merged by utilizing the idle time between tasks in a task group, it reduces the use of the processors; lastly, Assign the tasks to processors by making full use of the idle time of the processors, it increases resource utilization. The new algorithm is compared with TDS and TDCS by simulation platform CloudSim. The performance indicators for comparison include makespan of workflow, the number of used processors and resource utilization. The experiment results show that the new algorithm has a smaller makespan of workflow, fewer processors are used, and has higher resource utilization for both compute-intensive and data-intensive workflow, especially for data-intensive workflow, the new algorithm has obvious advantages on the three performance indicators. [ABSTRACT FROM AUTHOR]

In the era of big data, the traditional capacity of computing and storage has been unable to meet the growing demand. In this case, Cloud Computing technology is emerging. Researching on task scheduling is a way from the perspective of resource allocation and management to improve performance of Hadoop system. In this paper, a speculative task scheduling strategy that based on SDN technology is improved. For LATE mechanism, some slow tasks are slower than speculative tasks. This is not only unable to reduce task turnaround time and a waste of system resources. In this paper, we join the slow task compared with the speculative task for the speculative task scheduling strategy of LATE. Wherein, the run time of speculative tasks contains the input data transfer time, real-time bandwidth corresponding to a bandwidth of the link. Based on this model, we propose a bandwidth-aware speculative task run time estimation model (BWRE) based on SDN, using this model to accurately speculative the backup task run time. And we use SDN to provide bandwidth guarantees for the speculative task. Finally, BWRE is verified by simulation experiments. Evaluation results show that BWRE outperforms the shortening job turnaround time by an average of 9.85%. [ABSTRACT FROM AUTHOR]

Cloud computing has become a highly required platform in fields of information technology due to providing inexpensive services with high availability and scalability. The dynamic and diverse nature of the cloud computing systems makes scheduling of workflow tasks a pivotal issue. This paper proposes an algorithm to schedule applications' tasks to virtual machines (VMs) of cloud computing systems. The algorithm has three phases: level sorting, task-prioritizing and virtual machine selection. The three-phase process successfully assigns the virtual machine for each task without making any difficulties for evaluating the algorithm performance; extensive simulation experiments are performed. The introduced ICTS algorithm analyzes each incoming task which is sorted and ranked while assigning the virtual machine to the particular task which improves the overall scheduling process because it processes the job according to the importance. Then the efficiency of the system is evaluated using experimental results that indicate the improved cost task scheduling (ICTS) algorithm provides an improvement in schedule length as well as significant monetary cost saving. [ABSTRACT FROM AUTHOR]

An efficient scheduling algorithm is the key to handling various tasks simultaneously for the multifunction radar. A radar task comprises the transmitting, waiting and receiving durations. The waiting duration can be utilized to transmit or receive signals. This scheduling technique is called pulse interleaving. In this article, a novel online pulse interleaving scheduling algorithm is proposed. The algorithm divides the pulse interleaving analysis into the time resource and the energy resource analyses and adaptively interleaves different types of tasks. Thereby the pulse interleaving can be simplified, the waiting durations can be fully utilized, and different workload situations can be well matched. Simulation results show that the proposed algorithm achieves higher successful scheduling, time utilization and high-value ratios compared with the three existing algorithms while maintaining an efficient runtime. [ABSTRACT FROM AUTHOR]

Efficient scheduling of tasks in heterogeneous computing systems is of primary importance for high-performance execution of programs. The programs are to be considered as multiple sequences of tasks that are presented as directed acyclic graphs (DAG). Each task has its own execution timeline that incorporates into multiple processors. Moreover, each edge on the graph represents constraints between the sequenced tasks. In this paper, we propose a new list-scheduling algorithm that schedules the tasks represented in the DAG to the processor that best minimizes the total execution time by taking into consideration the restriction of crossover between processors. This objective will be achieved in two major phases: (a) computing priorities of each task that will be executed, and (b) selecting the processor that will handle each task. The first phase, priorities computation, focuses on finding the best execution sequence that minimizes the makespan of the overall execution. In list-scheduling algorithm, the quality of the solution is very sensitive to the priority assigned to the tasks. Therefore, in this paper, we include an enhanced calculation of weight that is used in the ranking equation for determining the priority of tasks. The second phase, processor selection, primarily focuses on allocating a processor that is a best fit for each task to be executed. In this paper, we enhance the processor selection by introducing a randomized decision mechanism based on a threshold which decides whether the task be assigned to the processor with the lowest execution time or to the processor that produces the lowest finish time. This mechanism considers a balanced combination of the local and global optimal results to explore the search space efficiently to optimize the overall makespan. The proposed algorithm is evaluated on different randomly generated DAGs, and results are compared with the well-known existing approaches to show the effectiveness of the proposed algorithm in reducing the makespan of execution. The experiment's results show improvement in the makespan that reaches up to 6-7%. [ABSTRACT FROM AUTHOR]

Service-level agreement (SLA) is a major issue in cloud computing because it defines important parameters such as quality of service, uptime, downtime, period of service, pricing, and security. However, the service may vary from one cloud service provider (CSP) to another. The collaboration of the CSPs in the heterogeneous multi-cloud environment is very challenging, and it is not well covered in the recent literatures. In this paper, we present two SLA-based task scheduling algorithms, namely SLA-MCT and SLA-Min-Min for heterogeneous multi-cloud environment. The former algorithm is a single-phase scheduling, whereas the latter one is a two-phase scheduling. The proposed algorithms support three levels of SLA determined by the customers. Furthermore, the algorithms incorporate the SLA gain cost for the successful completion of the service and SLA violation cost for the unsuccessful end of the service. We simulate the proposed algorithms using benchmark and synthetic datasets. The experimental results of the proposed SLA-MCT are compared with three single-phase task scheduling algorithms, namely CLS, Execution-MCT, and Profit-MCT, and the results of the proposed SLA-Min-Min are compared with two-phase scheduling algorithms, namely Execution-Min-Min and Profit-Min-Min in terms of four performance metrics, namely makespan, average cloud utilization, gain, and penalty cost of the services. The results clearly show that the proposed algorithms properly balance between makespan and gain cost of the services in comparison with other algorithms. [ABSTRACT FROM AUTHOR]