Bi-objective decision support system for task-scheduling based on genetic algorithm in cloud computing

Aziza, Hatem; Krichen, Saoussen

doi:10.1007/s00607-017-0566-5

Cited by 61 publications

(24 citation statements)

References 18 publications

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“…Examples include but not limited to the following. Aziza et al [34] proposed a time-shared and a space-shared genetic algorithm which are demonstrated to be able to outperform competed scheduling methods in terms of makespan and processing cost. Based on the ACO algorithm, Li et al [35] introduced a load balancing algorithm for task scheduling in cloud computing.…”

Section: Related Workmentioning

confidence: 99%

“…Different cloud computing systems (or computing resource providers) could have different requirements on the performance of task executions. Therefore, similar to some recent works [34], [41], we employ some weight values (i.e., w i ) for the above three functions to make our target function tunable, which leads the final optimization objective function as:…”

Section: Optimization Modelmentioning

confidence: 99%

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A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Chen

Cheng

Liu

et al. 2020

IEEE Systems Journal

215

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Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks. Index Terms-Cloud computing; task scheduling; whale optimization algorithm; metaheuristics; multi-objective optimization I. INTRODUCTION W Ith the ubiquitous growth of Internet access and big data, cloud computing becomes more and more popular in today's business world [1]. Compared to other distributed computing techniques (e.g., cluster and grid computing), cloud computing has provided an elastic and scalable way on delivering services to consumers. Namely, consumers do not need to possess the underlying technology and they can make use of computing resources and platforms in a pay-per-use fashion [2], [3]. The basic mechanism of cloud computing is to dispatch computing tasks to a resource pooling constituting of a

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Section: Related Workmentioning

confidence: 99%

Section: Optimization Modelmentioning

confidence: 99%

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Chen

Cheng

Liu

et al. 2020

IEEE Systems Journal

215

View full text Add to dashboard Cite

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“…Kumar et al [22] put forward a new task scheduling method, which integrated min-min algorithm and minmax algorithm in a genetic algorithm. e goal of the research is to shorten the generation time and execution time to the greatest extent [23].…”

Section: Relevant Workmentioning

confidence: 99%

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Wang

Pang

et al. 2020

Scientific Programming

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Task scheduling plays a critical role in the performance of the edge-cloud collaborative. Whether the task is executed in the cloud and how it is scheduled in the cloud is an important issue. On the basis of satisfying the delay, this paper will schedule tasks on edge devices or cloud and present a task scheduling algorithm for tasks that need to be transferred to the cloud based on the catastrophic genetic algorithm (CGA) to achieve global optimum. e algorithm quantifies the total task completion time and the penalty factor as a fitness function. By improving the roulette selection strategy, optimizing mutation and crossover operator, and introducing cataclysm strategy, the search scope is expanded. Furthermore, the premature problem of the evolutionary algorithm is effectively alleviated. e experimental results show that the algorithm can address the optimal local issue while significantly shortening the task completion time on the basis of satisfying tasks delays.

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“…Aziza. H et al [21] used Genetic Algorithm (GA) to estimate the time needed to run a group of tasks in task scheduling of cloud, so as to reduce the processing cost. Ref.…”

Section: Related Workmentioning

confidence: 99%

Resource Constrained Profit Optimization Method for Task Scheduling in Edge Cloud

et al. 2020

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Edge cloud is a cloud computing system built on edge infrastructure. Task scheduling optimization is the key technology to ensure the quality of service in edge cloud. However, the openness of the edge cloud environment challenges the load balancing and profit optimization of task scheduling. In this paper, we analyze the business process and optimization factors of task scheduling in edge cloud. First, we propose a resource constrained task scheduling profit optimization algorithm (RCTSPO), which consists of clustering preprocessing, classification, profit matrix construction and optimal scheduling strategy calculation. Clustering preprocessing gathers similar tasks into one class and perform a classification on the clustered tasks. Then construct the profit matrix for resource constrained task scheduling, and the optimal task scheduling strategy is obtained based on the constructed profit matrix. Second, Petri nets are used to construct the different components of edge cloud, such as resource, task, user request and virtual machine, thus forming the task scheduling model of edge cloud. Third, the properties of task scheduling model are verified by using the related theory and tools of Petri nets. Finally, several experiments are done to evaluate the proposed method, the simulation results show that the algorithm not only achieves the maximum profit, but also performs well in terms of time, reliability and load balancing of task scheduling.

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Bi-objective decision support system for task-scheduling based on genetic algorithm in cloud computing

Cited by 61 publications

References 18 publications

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Resource Constrained Profit Optimization Method for Task Scheduling in Edge Cloud

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