A Bi- Objective Workflow Application Scheduling In Cloud Computing Systems

Abstract

1.INTRODUCTIONThe cloud computing system is a distributed system which offers the utility computing vision with some attractive qualities such as sharing the many dynamic resources by virtualization technology in order to meet the requirements of widely varying Requests.Generally, task scheduling process is an important issue in distributed systems because the requests should be mapped on resources in an efficient manner by considering the environment properties. Because of heterogeneous resources and many numbers of tasks with different characteristics in these systems, this issue is known as a NP-hard (Non-deterministic Polynomialtime hard) problem. Since a good scheduling method would impress on the system performance and there is no direct method to find an optimal solution in polynomial time, the scheduling process must rely on finding the best solution within possibilities. A number of meta-heuristic based methods were presented to solve NP problems such as: particle swarm optimization (PSO) [5], tabu search (TS) [6], simulated annealing (SA) [7], genetic algorithm (GA) etc. In contrast, GA by [8] [9] [10] are known to give good results in several

A parallel multi-objective genetic algorithm for scheduling scientific workflows in cloud computing

“…Verma and Kaushal 26 Synthetic Java Static Aryan and Delavar 27 Tasks Static Chen et al 28 Simple CloudSim Static Barrett et al 29 Tasks CloudSim Static Chen et al 30 Simple Static Casas et al 31 Scientific vSphere Dynamic Manasrah and Ba Ali 33 Scientific WorkflowSim Dynamic Choudhary et al 34 Scientific C + + Dynamic Xiumin et al 35 Scientific Java Dynamic Junlong et al 36 Scientific Dynamic Shojafar and Javanmardi 37…”

“…Blank cells mean that the desired parameter is not shown in the article. 13 Yes Yes ---Yes Zhu et al 16 Yes Yes ----Priyadarsini and Arockiam 22 Yes Yes ---Verma and Kaushal 26 -Yes Yes Yes Yes -Aryan and Delavar 27 Yes -----Chen et al 28 -Y e s ---Y e s Barrett et al 29 Yes Yes ---Chen et al 30 Yes Yes -Yes --Casas et al 31 Yes Yes ---Manasrah and Ba Ali 33 Yes Yes Yes ---Choudhary et al 34 Yes Yes ----Xiumin et al 35 Yes Yes ----Junlong et al 36 Yes Yes -Yes --Shojafar and Javanmardi 37 Yes Yes Yes ---Sardaraz and Tahir 38 Yes Yes Yes ---MS: makespan; LB: load balance; SLA: service-level agreement. Blank cells mean that the desired parameter is not targeted in the article.…”

“…A bi-objective workflow scheduling algorithm for cloud resources is presented in Aryan and Delavar. 27 In the first phase, the algorithm uses bi-directional task prioritization to create an initial population. Before the population is created, all tasks are sorted based on task priority.…”

Quality of Service (QoS) Aware Workflow Scheduling (WFS) in Cloud Computing: A Systematic Review

“…They have explained the "Best-Fit and Round-Robin" algorithm and have proved that HSGA provide results that are more efficient than other presented algorithms of this paper. • DWSGA Aryan and Delavar [95] have planned an algorithm that includes "Bi-Directional task prioritization" and "optimization of solutions". The main concern of DWSGA is to trim down the "GA operation iterations" by building up an optimized initial population in order to facilitate suitable workload distribution on resources.…”

Load Optimized M/M/M Queueing for Parallel Job Scheduling in Multiple Cloud Centers

“…In [16], Maximum Effective Reduction (MER) algorithm was designed to improve the resource efficiency while schedules in a cloud environment. Another method called bi-objective workflow scheduling was designed in [17] to improve the resource allocation on requests. A heuristic task scheduling algorithm [18] was designed using randomly generated graphs and set of task graphs to achieve best scheduling performance.…”