Fog computing task scheduling optimization based on multi-objective simplified swarm optimization

Task scheduling is still a challenge in cloud computing as no existing scheduling algorithms are not effectively provisioning and scheduling the resources in the cloud. Existing authors considered only metrics like makespan, execution time and turnaround time etc. and the previous authors concentrated only to optimize the above mentioned metrics. But no existing authors were considered about the effective provisioning of the resources in the cloud i.e, compute, storage and network capacities and still many resources in the cloud were underutilized. In this paper, we want to propose an algorithm which can effectively utilize the resources in the cloud by extending Particle Swarm Optimization by addressing the metrics Bandwidth utilization and Memory utilization particularly. We have simulated this algorithm by using cloudsim and compared the modified Dynamic PSO with the PSO algorithm and it outperforms in terms of Bandwidth and Memory utilization and the makespan is also optimized.

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Evolutionary Algorithms to Optimize Task Scheduling Problem for the IoT Based Bag-of-Tasks Application in Cloud–Fog Computing Environment

Applied Sciences ◽

10.3390/app9091730 ◽

2019 ◽

Vol 9 (9) ◽

pp. 1730 ◽

Cited By ~ 13

Author(s):

Binh Minh Nguyen ◽

Huynh Thi Thanh Binh ◽

Tran The Anh ◽

Do Bao Son

Keyword(s):

Task Scheduling ◽

Completion Time ◽

Fog Computing ◽

Operating Cost ◽

Operating Costs ◽

Scheduling Problem ◽

Computing Environment ◽

Swarm Optimization ◽

New Approach ◽

Modified Particle Swarm Optimization

In recent years, constant developments in Internet of Things (IoT) generate large amounts of data, which put pressure on Cloud computing’s infrastructure. The proposed Fog computing architecture is considered the next generation of Cloud Computing for meeting the requirements posed by the device network of IoT. One of the obstacles of Fog Computing is distribution of computing resources to minimize completion time and operating cost. The following study introduces a new approach to optimize task scheduling problem for Bag-of-Tasks applications in Cloud–Fog environment in terms of execution time and operating costs. The proposed algorithm named TCaS was tested on 11 datasets varying in size. The experimental results show an improvement of 15.11% compared to the Bee Life Algorithm (BLA) and 11.04% compared to Modified Particle Swarm Optimization (MPSO), while achieving balance between completing time and operating cost.

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Balanced Global and Local Properties Multi-objective Particle Swarm Optimization (GL-MOPSO): A microgrid scheduling optimization algorithm

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/772/1/012036 ◽

2021 ◽

Vol 772 (1) ◽

pp. 012036

Author(s):

Hongcheng Zhao ◽

Zhipeng Zhang ◽

Junjie Zhang ◽

Haidong Liang ◽

Fei Zeng ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm ◽

Scheduling Optimization ◽

Swarm Optimization ◽

Multi Objective ◽

Local Properties ◽

Scheduling Optimization Algorithm ◽

Global And Local

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HYBRID CAT SWARM OPTIMIZATION AND SIMULATED ANNEALING FOR DYNAMIC TASK SCHEDULING ON CLOUD COMPUTING ENVIRONMENT

Journal of Information and Communication Technology ◽

10.32890/jict2018.17.3.8260 ◽

2018 ◽

Author(s):

Danlami Gabi ◽

Abdul Samad Ismail ◽

Anazida Zainal ◽

Zalmiyah Zakaria ◽

Ahmad Al-Khasawneh

Keyword(s):

Cloud Computing ◽

Local Search ◽

Task Scheduling ◽

Execution Time ◽

Computing Environment ◽

Swarm Optimization ◽

Cloud Computing Environment ◽

Multi Objective ◽

Dynamic Task ◽

Dynamic Task Scheduling

The unpredictable number of task arriving at cloud datacentre and the rescaling of virtual processing elements can affect the provisioning of better Quality of Service expectations during task scheduling in cloud computing. Existing researchers have contributed several task scheduling algorithms to provide better QoS expectations but are characterized with entrapment at the local search and high dimensional breakdown due to slow convergence speed and imbalance between global and local search, resulting from lack of scalability. Dynamic task scheduling algorithms that can adjust to long-time changes and continue facilitating the provisioning of better QoS are necessary for cloud computing environment. In this study, a Cloud Scalable Multi-Objective Cat Swarm Optimization-based Simulated Annealing algorithm is proposed. In the proposed method, the orthogonal Taguchi approach is applied to enhance the SA which is incorporated into the local search of the proposed CSMCSOSA algorithm for scalability performance. A multi-objective QoS model based on execution time and execution cost criteria is presented to evaluate the efficiency of the proposed algorithm on CloudSim tool with two different datasets. Quantitative analysis of the algorithm is carried out with metrics of execution time, execution cost, QoS and performance improvement rate percentage. Meanwhile, the scalability analysis of the proposed algorithm using Isospeed-efficiency scalability metric is also reported. The results of the experiment show that the proposed CSM-CSOSA has outperformed Multi-Objective Genetic Algorithm, Multi-Objective Ant Colony and Multi-Objective Particle Swarm Optimization by returning minimum execution time and execution cost as well as better scalability acceptance rate of 0.4811−0.8990 respectively. The proposed solution when implemented in real cloud computing environment could possibly meet customers QoS expectations as well as that of the service providers.

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