An enhanced genetic algorithm with new operators for task scheduling in heterogeneous computing systems

Akbari, Mohammad; Rashidi, Hassan; Alizadeh, Sasan H.

doi:10.1016/j.engappai.2017.02.013

Cited by 97 publications

(26 citation statements)

References 38 publications

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“…Keshanchi et al [17] proposed an improved heuristic-based genetic algorithm, called N-GA. e N-GA is used for the static task scheduling in the cloud. Akbari et al [18] improved the performance of genetic algorithm by significantly changing genetic operators to ensure the sample diversity and reliable coverage of the entire space. In [19], a hybrid metaheuristic algorithm is offered, which uses the HEFT (Heterogeneous Earliest Completion Time) algorithm combined with PSO and GA to improve performance.…”

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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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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“…Heterogeneous Computing Systems (HCS) provides a large scale computing platform encompassing multiple geographically distributed computing resources, from different administrative domains-ranging from campus-wide resources to worldwide federated resources, to execute complex applications [1]- [3]. This aggregation and sharing of multiple resources from distributed computing sites for executing large user applications is known as co-allocation or cross-site allocation [4] [5].…”

Section: Introductionmentioning

confidence: 99%

“…Cloud platform is established on the concept of utility computing and virtualization for offering different types of services such as Infrastructure as a service (IaaS), Platform as a service (PaaS) and Software as a service (SaaS) to users [7]. One of the main challenges in HCS platforms from last two decades is to develop efficient job scheduling algorithms in order to meet the expectations of the service provider and user [3]. Job scheduling problem (JSP) in HCS platforms primarily deals with two scheduling issues; first to decide the order of execution of waiting jobs (also known as job ordering) and second is allocation or mapping of jobs (also refereed as tasks or applications) to HCS resources [8].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

2019

IJRTE

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Over two decades, Heterogeneous Computing Systems (HCS) are offering large amount of federated computing resources, spanning across different administrative domains, to compute-intensive user applications. Efficient job schedulers are required to allocate HCS resources to user applications to satisfy system provider and user requirements. Offline scheduling is most popular kind of job scheduling in heterogeneous system, in which jobs are collected in batch and scheduled together. Job scheduling in HCS has become NP-hard problem due to system scale, federated structure and high resource as well as job heterogeneity. Simple queuing and deterministic heuristics have failed to provide optimal solution to NP-hard job scheduling problem. Due to NP-hard nature of job scheduling problem, there is always a scope to propose new scheduling solutions using meta-heuristics. Offline scheduling in HCS has been focused more on scheduling independent sequential tasks viz. Bag-of-tasks or Many-tasks. Offline scheduling of parallel jobs (composed of collaborating tasks with no precedence) in HCS has not gained much attention. In this paper, a novel hybrid multi-objective meta-heuristic known as HCSPSO, which combines the qualities of Cuckoo search (CS) and Particle Swarm Optimization (PSO), has been proposed to schedule batch of parallel jobs in multi-cluster HCS platform. Proposed HCSPSO policy is extensively compared with different heuristics and metaheuristics using different resource configurations and real supercomputing workload logs. Comparative results have showed the dominance of the proposed hybrid scheduling algorithm over other algorithms.

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“…This meta-heuristic algorithm is simple, flexible, and ergodic [4]. Besides generating schedules with lowered repetitions, which allows for maximum parallelization for improved performance of the tasks, it also reduces execution time and helps to avoid conflicts with local optimums [5]. Although genetic allocation is improved to guarantee variety and consistent coverage of problem space, it is laborious to complete the overall configuration at the beginning of data transfer to make the system operate under supervision.…”

Section: Introductionmentioning

confidence: 99%

A task-based multi-core allocation mechanism for packet acceleration

Guo

Liu

et al. 2018

IEICE Electron. Express

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Task allocation is a traditional and complicated problem for a system with many peripherals. Normal allocation methodology focuses on fuzzy data transfer but is unable to analyze some specific experimental statistics and does not improve system performance. Data Director (DD) is an innovative packet-based manager, is comprised of Data Index (DI) and Packet Direct Memory Access (PDMA) and realizes the enhanced data transfer in a complex system. This paper presents an improved task allocation model to enhance data transfer in a task-based model with three different allocation methodologies. The experimental results for the optimization allocations show that the proposed methodologies reduce the delay of transfer differently due to the schedule costs in these cases.

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An enhanced genetic algorithm with new operators for task scheduling in heterogeneous computing systems

Cited by 97 publications

References 38 publications

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

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

Hybrid Metaheuristic Based Offline Parallel Job Scheduling in Heterogeneous Computing Systems

A task-based multi-core allocation mechanism for packet acceleration

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