A modified PSO algorithm for task scheduling optimization in cloud computing

2018 ◽  
Vol 30 (24) ◽  
pp. e4970 ◽  
Author(s):  
Zhou Zhou ◽  
Jian Chang ◽  
Zhigang Hu ◽  
Junyang Yu ◽  
Fangmin Li
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Pso Algorithm ◽  
Scheduling Optimization ◽  
Modified Pso

scholarly journals Dynamic PSO for Task Scheduling Optimization in Cloud Computing

2019 ◽  
Vol 8 (2S11) ◽  
pp. 332-338
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Execution Time ◽  
Pso Algorithm ◽  
Turnaround Time ◽  
Bandwidth Utilization ◽  
Scheduling Optimization ◽  
Swarm Optimization ◽  
Memory Utilization

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.

Pso Optimization algorithm for Task Scheduling on The Cloud Computing Environment

2014 ◽  
Vol 13 (9) ◽  
pp. 4886-4897 ◽  
Author(s):  
Zahraa Tarek Abdelhamed Elmana ◽  
Magdy Zakria ◽  
Fatma A. Omara
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Pso Algorithm ◽  
Scheduling Problem ◽  
Computing Environment ◽  
Swarm Optimization ◽  
Cloud Computing Environment ◽  
Computation Cost ◽  
Modified Pso ◽  
Available Resources

The Cloud computing is a most recent computing paradigm where IT services are provided and delivered over the Internet on demand. The Scheduling problem for cloud computing environment has a lot of awareness as the applications tasks could be mapped to the available resources to achieve better results. One of the main existed algorithms of task scheduling on the available resources on the cloud environment is based on the Particle Swarm Optimization (PSO). According to this PSO algorithm, the applications tasks are allocated to the available resources to minimize the computation cost only. In this paper, a modified PSO algorithm has been introduced and implemented for solving task scheduling problem in the cloud. The main idea of the modified PSO is that the tasks are allocated on the available resources to minimize the execution time in addition to the computation cost. This modified PSO algorithm is called Modified Particle Swarm Optimization (MPOS).The MPOS evaluations have been illustrated using different time, and cost parameters and their effects in the performance measures such as utilization, speedup, and efficiency. According to the implementation results, it is found that the modified MPOS algorithm outperforms the existed PSO.

A PSO Algorithm Based Task Scheduling in Cloud Computing

2019 ◽  
Vol 10 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Mohit Agarwal ◽  
Gur Mauj Saran Srivastava
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Pso Algorithm ◽  
Emerging Technology ◽  
The Internet ◽  
Hard Problem ◽  
Swarm Optimization ◽  
Np Hard Problem ◽  
Simulation Results

Cloud computing is an emerging technology which involves the allocation and de-allocation of the computing resources using the internet. Task scheduling (TS) is one of the fundamental issues in cloud computing and effort has been made to solve this problem. An efficient task scheduling mechanism is always needed for the allocation to the available processing machines in such a manner that no machine is over or under-utilized. Scheduling tasks belongs to the category of NP-hard problem. Through this article, the authors are proposing a particle swarm optimization (PSO) based task scheduling mechanism for the efficient scheduling of tasks among the virtual machines (VMs). The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism. The simulation results clearly show that the PSO-based task scheduling mechanism clearly outperforms the others as it results in almost 30% reduction in makespan and increases the resource utilization by 20%.

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