Journal Article10.4304/JNW.7.3.547-553
Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm
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TL;DR: A model for task scheduling is formulated and a particle swarm optimization (PSO) algorithm which is based on small position value rule is proposed which proves that the PSO algorithm is more suitable to cloud computing.
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Abstract: Cloud computing is an emerging technology and it allows users to pay as you need and has the high performance. Cloud computing is a heterogeneous system as well and it holds large amount of application data. In the process of scheduling some intensive data or computing an intensive application, it is acknowledged that optimizing the transferring and processing time is crucial to an application program. In this paper in order to minimize the cost of the processing we formulate a model for task scheduling and propose a particle swarm optimization (PSO) algorithm which is based on small position value rule. By virtue of comparing PSO algorithm with the PSO algorithm embedded in crossover and mutation and in the local research, the experiment results show the PSO algorithm not only converges faster but also runs faster than the other two algorithms in a large scale. The experiment results prove that the PSO algorithm is more suitable to cloud computing.
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Citations
Task scheduling techniques in cloud computing: A literature survey
TL;DR: A comprehensive survey of task scheduling strategies and the associated metrics suitable for cloud computing environments is presented and the various issues related to scheduling methodologies and the limitations to overcome are discussed.
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Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization
TL;DR: A Task-based System Load Balancing method using Particle Swarm Optimization (TBSLB-PSO) that achieves system load balancing by only transferring extra tasks from an overloaded VM instead of migrating the entire overloaded VM is proposed.
273
A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems
Xuan Chen,Long Cheng,Cong Liu,Qingzhi Liu,Jinwei Liu,Ying Mao,John Murphy +6 more
- 16 Jan 2020
TL;DR: The proposed Improved WOA for Cloud task scheduling (IWC) has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms, and can also achieve better performance on system resource utilization.
Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends
TL;DR: In this paper, the authors provide a brief on traditional and heuristic scheduling methods before diving deeply into the most popular meta-heuristics for cloud task scheduling followed by a detailed systematic review featuring a novel taxonomy of those techniques, along with their advantages and limitations.
253
References
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James Kennedy,Russell C. Eberhart +1 more
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TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
44.1K
•Journal Article
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TL;DR: The experimental results show that the PSO is a promising optimization method and a new approach is suggested to improve PSO's performance near the optima, such as using an adaptive inertia weight.
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