Symbiotic Organism Search optimization based task scheduling in cloud computing environment

A comprehensive survey for scheduling techniques in cloud computing

Cloud computing provides effective mechanisms for distributing the computing tasks to the virtual resources. To provide cost-effective executions and achieve objectives such as load balancing, availability and reliability in the cloud environment, appropriate task and workflow scheduling solutions are needed. Various metaheuristic algorithms are applied to deal with the problem of scheduling, which is an NP-hard problem. This paper presents an in-depth analysis of the Particle Swarm Optimization (PSO)-based task and workflow scheduling schemes proposed for the cloud environment in the literature. Moreover, it provides a classification of the proposed scheduling schemes based on the type of the PSO algorithms which have been applied in these schemes and illuminates their objectives, properties and limitations. Finally, the critical future research directions are outlined.

A Survey of PSO-Based Scheduling Algorithms in Cloud Computing

CLOUDRB: A framework for scheduling and managing High-Performance Computing (HPC) applications in science cloud

Dynamic on‐demand resource provisioning is one of the primary goals of the cloud computing task scheduling process. Task scheduling is a nondeterministic polynomial time (NP)‐hard problem and is responsible for assigning tasks to virtual machines (VMs) in a way that increases the resource utilization and performance, reduces response time, and keeps the whole system balanced. In this paper, we present a static task scheduling method based on the particle swarm optimization (PSO) algorithm where the tasks are assumed to be non‐preemptive and independent. We have improved the performance of the basic PSO method using a load‐balancing technique. We have compared our proposed method with round robin (RR) task scheduling, improved PSO task scheduling and a load‐balancing technique. The simulation results show that our method outperforms these algorithms by an increase of resource utilization of 22% and a decrease of makespan by 33%, compared with the basic PSO algorithm. The results illustrate that our proposed method converges to the near optimal solution faster than the basic PSO algorithm and is more efficacious with more tasks.

A PSO‐based task scheduling algorithm improved using a load‐balancing technique for the cloud computing environment

Job scheduling system problem is a core and challenging issue in cloud computing. How to use cloud computing resources efficiently and gain the maximum profits with job scheduling system is one of the cloud computing service providers’ ultimate goals. For characteristics of particle swarm optimization algorithm in solving the large-scale combination optimization problem easy to fall into the search speed slowly and partially the most superior, the global fast convergence of simulated annealing algorithm is utilized to combine particle swarm optimization algorithm in each iteration, which enhances the convergence rate and improves the efficiency. This paper proposed the improve particle swarm optimization algorithm in resources scheduling strategy of the cloud computing. Through experiments, the results show that this method can reduce the task average running time, and raises the rate availability of resources.

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Hongying Huo

Papers

Improved PSO-based Task Scheduling Algorithm in Cloud Computing