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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]

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]

Recently, edge-cloud has attracted much attention by its promising prospect in terms of facilitating the benefits of edge and cloud together. It is promising for urban video systems that require efficient and effective processing for their intelligent monitoring drives on various ends, like sky drones and land cameras. For instance, to support crowd recognition for public safety, the tasks to crowd recognition need to be placed into all processing nodes in the video systems for processing effectively. This is a challenging problem to facilitate the edge-cloud orchestrated scenarios. However, the variability of tasks based on their complexities is not considered fully in existing strategies. In this regard, we model and analyse task placement for crowd recognition in edge-cloud intelligent video systems. Then, our strategies are proposed which are referred to Node-Graph based Task Placement (NGTP) and Cluster-Graph based Task Placement (CGTP). Specifically, with the help of data dependencies, NGTP utilises the greedy approach with node graphs in the centralised way for general scenarios. Comparatively, CGTP utilises data dependency and similarity for task placing in the decentralised way for emergency scenarios. The experiments demonstrate the superior and effectiveness performance in forming tasks cost and running time of our proposed approaches. [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]

This research paper proposes a novel approach named priority-based load balancing (PLB) for cloud computing environment. The PLB provides a resilient and adaptive task scheduling using multi-queues. Numerous strategies have already been proposed in the past researches to prioritize the tasks and mapping all the tasks to different resources available on the cloud. There is still a hindrance in the performance due to the negligible attention paid to the unused resources and tasks having low priority, eventually leading to starvation problem. To this end, the PLB algorithm has been partitioned into four sub-procedures, namely (i) Starvation-free task allocation, (ii) Inserting tasks into the dispatcher, (iii) Re-ordering tasks inside the queues and eventually, (iv) Mapping tasks onto the Virtual Machines (VMs) calculating the cost incurred for all the corresponding VMs. The sole motivation of this research work is to optimize the performance parameters by allocating all the jobs to all the available resources in the workflow model. It also consolidates the job categorization in the priority-based multi-queues, while filtering tasks from all the queues to overcome the deprivation of low priority tasks. In this paper, a test-bed setup has been deployed using CloudSim 3 and TCS WAN emulator for experimentation and results evaluation. The experimental setup imbibes different aspects such as performance measures, average response time, makespan time in order to ascertain efficiency, resource utilization ratio and bandwidth of the workflow model. The obtained results are further compared with five different approaches including- First Come First Serve, Round Robin, Min–Min, Max–Min and ACO and it was observed that the proposed strategy yielded more efficiency and accuracy in most of the cases. The experimental results have been further validated and demonstrated in order to justify the claims of the proposed approach, being able to tackle out different priority tasks and resource allocation in a stable and optimum manner. [ABSTRACT FROM AUTHOR]

Due to the limitations associated with the processing capability of mobile devices in cloud environments, various tasks are offloaded to the cloud server. This has led to an increase in the efficiency of mobile applications in the two decades since the advent of the cloud paradigm. However, task offloading may not be a suitable option for delay-sensitive mobile applications because the cloud server is usually located remotely from mobile users. To overcome this problem, fog computing, also known as "Cloud at the Edge", has been introduced as a complementary solution. On the other hand, although fog computing brings computing and radio resources closer to mobile devices, fog nodes cannot adequately meet users' needs due to limited computing resources. To minimize delays in responding to mobile users' requests, it is necessary to establish a trade-off between local execution of requests on end-devices and the fog environment. In this paper, we present task offloading in the form of a multi-objective optimization problem with a focus on reducing both total power consumption of the system and the delay in executing tasks. Then, considering the NP-hardness of the problem, we solve it using two meta-heuristic methods, namely the non-dominated sorting genetic algorithm (NSGA-II) and the Bees algorithm. The simulation results supported the robustness of both meta-heuristic algorithms in terms of energy consumption and delay reduction. The proposed methods achieve a better tradeoff concerning both offloading probability and the power required for data transmission. [ABSTRACT FROM AUTHOR]

Mobile cloud computing augments smart-phones with computation capabilities by offloading computations to the cloud. Recent works only consider the energy savings of mobile devices while neglecting the cost incurred to the tasks which are offloaded. We might offload several tasks to minimize the total energy consumption of mobile devices; however, this could incur a huge monetary cost. Furthermore, these issues become more complex in considering the multi-tenant cloud, which is not addressed in literature adequately. Thus, to balance the trade-off between monetary cost and energy consumption of the mobile devices, we need to decide whether to offload the task to the cloud or run it locally. In this article, first, we have formulated a 'MinEMC' optimization problem to minimize both the energy as well as the monetary cost of the mobile devices. The 'MinEMC' problem is proven to be NP-hard. We formulate a special case with an equal amount of resource requirement by each task for which a polynomial-time solution is presented. Further various policies are proposed, the cloud can employ to solve the general case. Then we proposed an efficient heuristic named 'Off-Mat' based on distributed stable matching, the solution for which determines whether the tasks are to be offloaded or not under multi-constraints. We also analyze the complexity of this proposed heuristic algorithm. Finally, performance evaluation through simulation results demonstrates that the Off-Mat algorithm attains high-performance in computational tasks offloading and scale well as the number of tenants increases. [ABSTRACT FROM AUTHOR]

Clouds are heterogeneous service-oriented systems which are increasingly considered as platforms of choice for scientific workflow applications. Because resource and communication failures are inevitable in large complex distributed systems, insuring the reliability of heterogeneous service-oriented systems poses a major challenge. As it affects the quality of user service requirements, reliability has become an important criterion in workflow scheduling. Replication-based fault-tolerance is one approach for satisfying the requirements set to safeguard the reliability of an application. In order to minimize the workflow execution cost while respecting the user-defined deadline and reliability, the present paper proposes Improving CbCP with Replication (ICR) which includes three algorithms: the Scheduling, the Fix Up, and the Task Replication. The Scheduling employs the CbCP algorithm, where CbCP stands for Clustering based on Critical Parent and it is a previously developed algorithm by the same authors, to generate a schedule map of the workflow. The Fix Up algorithm checks the possibility of starting each task earlier in the leased resource without imposing any extra cost. The Task Replication algorithm utilizes the rest of the idle time slots in leased resources to replicate tasks. Experimental results from real and randomly generated applications at different scales demonstrate that the proposed heuristic, for the majority of studied scenarios, increases the execution reliability of workflows while reducing the workflows execution costs. [ABSTRACT FROM AUTHOR]

With the increasing number of sophisticated power-intensive applications, embedded systems require energy-efficient computing devices. Most real-time applications of embedded systems are subject to timing constraints that should be met for the applications to run properly. Unfortunately, local computational devices have limited computation and storage capacity. Moreover, the real-time applications that perform complex computations consume large amounts of power. Therefore, offloading the power-intensive computational tasks to a more powerful entity is an efficient technique to overcome the limited computational resources of local devices and reduces the overall power consumption. In this paper, we propose two algorithms for making an efficient offloading decision for soft and weakly hard (firm) real-time applications while guaranteeing the schedulability of tasks. We have performed different experiments and investigated the technical and economic feasibility of using offloading to perform various processes that require different computational power. Experimental results show that significant power can be saved by offloading the resources of the power-intensive applications into the cloudlet for weakly hard real-time tasks and and cloud for soft real-time tasks. [ABSTRACT FROM AUTHOR]

Focus on node cloud computing environment due to the randomness of the user task arrival caused a large number of idle energy consumption. To solve this problem, a dynamic energy-efficient scheme in virtual machines based on user task characteristic is proposed. First, according to the amount of user tasks arrival and the expectations of task completion time, user tasks characteristic information table is established. Thus it can predict the request arrival probability and user expect to reach the finish time. Then, according to user expectations of completion time, psychological expectations QOS value of the user is obtained, and the threshold based on the integration of energy-saving value is dynamically adjusted. Finally, it can achieve energy-saving by dynamic integrating of the user task arrival rate and closing idle node. Experimental results show that the method can reach a better balance of performance and energy saving to meet user requirement. [ABSTRACT FROM AUTHOR]

In a cloud environment, scheduling problem as an NP-complete problem can be solved using various metaheuristic algorithms. The metaheuristic algorithms are very popular for scheduling tasks because of their effectiveness. A bacterial foraging is a swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. This paper proposes a task scheduling algorithm based on bacterial foraging optimization to reduce the idle time of virtual machines whereas the load balancing and reducing of runtime have occurred. The Cloudsim toolkit has assessed the performance of the proposed method in comparison with some scheduling algorithms. According to the obtained results, the makespan and energy consumption were reduced by using the proposed algorithm. [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]

In this work the Numerical Aerodynamic Simulation (NAS) benchmarks have been executed in a systematic way on two clusters of rather different architectures and CPUs, to identify dependencies between MPI tasks mapping and the speedup or resource occupation. To this respect, series of experiments with the NAS kernels have been designed to take into account the context complexity when running scientific applications on HPC environments (CPU, I/O or memory-bound, execution time, degree of parallelism, dedicated computational resources, strong- and weak-scaling behaviour, to cite some). This context includes scheduling decisions, which have a great influence on the performance of the applications, making difficult to achieve an optimal exploitation with cost-effective strategies of the HPC resources. An analysis on how task grouping strategies under various cluster setups drive the execution time of jobs and the infrastructure throughput is provided. As a result, criteria for cluster setup arise linked to maximize performance of individual jobs, total cluster throughput or achieving better scheduling. To this respect, a criterion for execution decisions is suggested. This work is expected to be of interest on the design of scheduling policies and useful to HPC administrators. [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]

Spatial crowdsourcing is a promising architecture that collects various types of data online with the help of participants powerful mobile devices. Humans are involved in the crowdsourcing process, thereby increasing its accuracy; however, it is also associated with some privacy and security problems. The crowd tasks are executed in participants mobile devices, and the results are send to the server through networks, so that attackers could eavesdrop participants location information. Thus, we studied and proposed a spatial task assignment method for privacy-aware spatial crowdsourcing using a secure grid-based index. The secure grid index used an encrypted grid number and grid cell-based local coordinate system to protect participants location privacy. By using the grid based index in spatial task management process, it also could increase the spatial task processing time. In the experimental test, we showed that the proposed method is faster than the current method and extremely efficient when the spatial crowdsourcing tasks are geometry based tasks. [ABSTRACT FROM AUTHOR]

A correction to this paper has been published: https://doi.org/10.1007/s10586-021-03335-y [ABSTRACT FROM AUTHOR]