Opposition Learning-Based Grey Wolf Optimizer Algorithm for Parallel Machine Scheduling in Cloud Environment

Abstract

Cloud computing is a novel developing computing paradigm where implementations, information, and IT services are given over the internet. The parallel-machine scheduling (Task-Resource) is the important role in cloud computing environment. But parallel-machine scheduling issues are premier that associated with the efficacy of the whole cloud computing facilities. A good scheduling algorithm has to decrease the implementation time and cost along with QoS necessities of the consumers. To overcome the issues present in the parallel-machine scheduling, we have proposed an oppositional learning based grey wolf optimizer (OGWO) on the basis of the proposed cost and time model on cloud computing environment. Additionally, the concept of opposition based learning is used with the standard GWO to enhance its computational speed and convergence profile of the proposed method. The experimental results show that the proposed method outperforms among all methods and provides quality schedules with less memory utilization and computation time.

Optimization of Task Scheduling Algorithm Using Modified Mean Grey-Wolf

“…G. Natesan, and Arun Chokkalingam, [22] proposed an oppositional grey wolf's optimization (OGWO) scheduling method based on cost and time model on CC. The computational speed and convergence profile were enhanced by opposition based learning with standard GWO.…”

“…The MS parameter is used to check the performance result of the proposed modified-MGWO. The proposed work is compared with PSO [20] and standard GWO [22] for TS. The tasks executed nearly 30 times and 50 VMs used in the simulation part for all LS-RS dataset.…”

An efficient Task Scheduling Algorithm using Modified Whale Optimization Algorithm in Cloud Computing

“…In this part, the exchange of existing methods are exhibited, which are makes to protect the information in distributed computing. The points of interest and impediment of the current systems are likewise examined from [11][12][13][14][15][16]. Kan Yang and Xiaohua Jia., [11] structured an inspecting system for information stacking in CC.…”

“…The SPSO calculation bit by bit builds the vitality cost go in heterogeneous condition. G. Natesan, and Arun Chokkalingam, [16] has proposed a multi-target parallel machine booking procedure dependent on the OGWO. Premise cost and time model on distributed computing condition is limit.…”

“…Performance Analysis of Proposed MWOA in terms of Makespan The MWOA introduction result is checked for makespan. The MWOA is compared with GA, ACO, PSO [15] and standard GWO [16] for task planning. The are various from 100-500 errands.…”