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عنوان فارسی مقاله:

بهینه سازی ازدحام ذره پیشرفته برای زمان بندی وظیفه در محیط های رایانش ابری

عنوان انگلیسی مقاله:

Enhanced Particle Swarm Optimization For Task Scheduling In Cloud Computing Environments

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2. Related WORK

There are many of research worked in resource scheduling to improve efficiency in cloud computing. Most of these researches improve the cost, waiting time, make span, resource utilization, execution time and round trip time. But, not consider other important parameters such as reliability, availability, scheduling success rate, speed and scalability. The complexity is reasoning to not consider these parameters. In 6 presented a Quality of service (QoS)-based Genetic Hybrid Particle Swarm Optimization (GHPSO) to schedule applications to cloud resources. In GHPSO, crossover and mutation of genetic algorithm is embedded into the particle swarm optimization algorithm (PSO). The simulation results show that the GHPSO achieves better performance than standard particle swarm algorithm used in minimize costs within a given execution time. In 7 formulated a model for task scheduling and propose a particle swarm optimization (PSO) algorithm which is based on small position value rule to minimize the cost of the processing. . 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. In 8 presented a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. It is used for workflow application by varying its computation and communication costs. The experimental results show that PSO can achieve cost savings and good distribution of workload onto resources. In 9 found a solution that meets the user-preferred Quality of Service (QoS) parameters. The work presented focuses on scheduling cloud workflows. With this algorithm, a significant improvement in CPU utilization is achieved. In 10 proposed an optimized scheduling algorithm to achieve the optimization or sub-optimization for cloud scheduling. In this algorithm an Improved Genetic Algorithm (IGA) is used for the automated scheduling policy. The tests illustrate that the speed of the IGA almost twice the traditional GA scheduling method and the utilization rate of resources always higher than the open-source IaaS cloud systems. In 11 improved cost-based scheduling algorithm for making efficient mapping of tasks to available resources in cloud. This scheduling algorithm measures both resource cost and computation performance, it also improves the computation/communication ratio by grouping the user tasks according to a particular cloud resource's processing capability and sends the grouped jobs to the resource.