A novel workflow scheduling with multi-criteria using particle swarm optimization for heterogeneous computing systems

Biswas, Tarun; Kuila, Pratyay; Ray, Anjan Kumar

doi:10.1007/s10586-020-03085-3

Cited by 14 publications

(10 citation statements)

References 30 publications

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“…Although PPSO performs better than N-GA for lesser number of tasks (for instance 5 and 10) but N-GA outperforms PPSO for larger number of tasks due to the fact that N-GA explores more with respect to ordering of tasks than PPSO which has a fixed sequence for all the iterations. H3CSA and N-GA performs better than PPSO for higher number of tasks because PPSO has a fixed ordering of tasks which is determined level wise 21 prior to its population initialization and remains intact across iterations. The search space for exploitation of VMs is large but the possibilities of obtaining other chronological ordering of tasks is reduced drastically resulting in its poor performance compared to H3CSA and N-GA.…”

Section: Results For Random Dagsmentioning

confidence: 99%

“…Biswas et al 21 Minimization of makespan and load balncing with maximisation of resource utilization and speed up using PSO.…”

Section: Evolutionarymentioning

confidence: 99%

“…In order to assess the performance of H3CSA, we compare its performance with three heuristic based solution strategies, viz., (i) HEFT-B; 2 (ii) HEFT-T; 2 (iii) HEFT-L 1 and two meta-heuristics namely (i) genetic-algorithm based approach (N-GA); 1 (ii) a particle swarm optimization based approach (PPSO) 21 as baselines. The proposed model considers makespan as the fitness evaluation parameter for comparison with the baselines.…”

Section: The Baseline Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

H3CSA: A makespan aware task scheduling technique for cloud environments

Mishra

Sahoo

Satpathy

2021

Trans Emerging Tel Tech

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Task scheduling is essential to enhance the performance of large scale collaborative and distributed e-business and e-science applications. A typical such application comprises multiple communicating tasks to be executed on virtualized resources also called as virtual machines (VMs). However, scheduling multiple tasks to VMs is non-trivial and is proven to be NP-Complete. The primary agenda of any scheduling technique is to reduce the makespan, which reflects the completion time of the exit task. Focusing on makespan reduction for scheduling multiple tasks across heterogeneous VMs, in this paper, we propose a model called H3CSA based on a meta-heuristic crow search algorithm (CSA).In order to assess the performance of H3CSA, its performance is compared with baselines such as HEFT-B, HEFT-T, HEFT-L, a genetic algorithm based technique (N-GA) and particle swarm optimization based technique (PPSO).Simulation results show improved performance of H3CSA with respect to N-GA and PPSO for smaller applications. However, considering sizeable applications H3CSA shows comparable performance with respect to N-GA and improved results for PPSO.H3CSA: A makespan aware task scheduling technique for cloud environments.

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Section: Results For Random Dagsmentioning

confidence: 99%

“…Biswas et al 21 Minimization of makespan and load balncing with maximisation of resource utilization and speed up using PSO.…”

Section: Evolutionarymentioning

confidence: 99%

Section: The Baseline Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

H3CSA: A makespan aware task scheduling technique for cloud environments

Mishra

Sahoo

Satpathy

2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…In Static scheduling algorithms, before starting the execution of the workflow, the scheduling is completely performed 16 . In Dynamic scheduling algorithms, each task is scheduled when it is ready to be executed and the execution of all its parent tasks is completed.…”

Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

“…In this type of scheduling, there is no initial scheduling plan before the start of workflow execution, and all decisions are made at runtime. In this way, at each stage, all the tasks are ready to determine and each of them is scheduled on the most appropriate available resource 16 …”

Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

Golmohammadi

Tabbakh

Ghaemi

2023

Trans Emerging Tel Tech

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The advent of distributed computing and mobile clouds made it possible to transfer and distribute the heavy processes of complex workflows to the cloud. Managing and with the help of the mobile cloud, virtualization and shared resources managing the executing large‐scale workflows is possible. Since virtual resources are used, scheduling and resource allocation are important and key research topics in public cloud environment and mobile clouds. The cloud model of mobile devices with public or virtual cloud, which consists of temporary mobile devices, is an interesting topic to be investigated. Workflow scheduling and availability of resources in mobile clouds by high mobility and Energy efficient, it is one of the challenges and problems that must be investigated one of the main challenges in this research area. The purpose of scheduling algorithms is to improve service quality criteria by observing the constraints. In this article, the main goal is to extensively review the scheduling algorithms for the complex scientific workflow in public and mobile clouds. Furthermore, a review of the use of heuristic and meta‐heuristic techniques in dynamic and static states, with various constraints for task scheduling in cloud and mobile computing has been performed. This article accomplishes a review of using existing techniques for scheduling tasks in mobile clouds. We also present a comprehensive analysis and systematic comparison between these scheduling algorithms.

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