A Multi-Objective Based Scheduling Framework for Effective Resource Utilization in Cloud Computing

Abstract

Cloud computing is a promising platform for running massive workflow applications based on a pay-per-use model. In cloud computing, the reduction of energy consumption and providing security to workflow scheduling are the key research areas. The primary focus of the existing algorithms, viz., particle swarm optimization (PSO), crow Search optimization (CSO) and Other non-metaheuristic algorithms like Round Robbin(RR), SJF, Min-Min, Min-Max etc., is based on the execution time and cost of the workflow applications as a budget constraint. However, these algorithms failed to adequately determine energy consumption, resource utilization, and security in workflow scheduling. To address this issue, a multi-objective scheduling framework is proposed. In this paper, the framework performs dynamic workflow scheduling using universal unique identification-Blake (UUID-Blake), Manhattan Distance-Partition around algorithm (MD-PAM), Linear Scaling-Crow Search Optimization (LS-CSO), Anova-Recurrent Neural Network. The implementation of this framework was achieved in three phases (Phase 1, Phase 2, and Phase 3). Phase 1 is about user registration and authentication using UUID-Blake, which enhances security by allowing legitimate users into the cloud environment. Phase 2 deals with clustering and resource monitoring using MD-PAM and A-RNN, to reduce makespan the similar tasks are clustered using task length and maximize the resource utilization by predicting the resource availability. Phase 3 deals with the scheduling of dynamic workflows using LS-CSO by selecting suitable virtual machines. We have considered the heterogeneous computing scheduling problem (HCSP) and grid workload archive (GWA)-T-12 Bitbrains datasets for comparing our proposed framework with existing works. Based on the result analysis, the proposed LS-SCO outperformed when compared with the algorithms CSO,PSO and RR has achieved better performance. INDEX TERMSCrow swarm optimization, Linear scaling, Manhattan distance, Partitioning around medoid, Recurrent neural network, Workflow scheduling. I. INTRODUCTION C Loud computing is transforming into a highperformance computing environment with enormous computational resources such as storage, networking, databases, and applications. These resources are allocated to users from the collective resource pool with reduced management or interaction [1]. The three major services delivered by the cloud technology include Software-as-a-Service (SaaS), Platform-asa-Service (PaaS), along with Infrastructure-as-a-Service (IaaS) [2] out of which the most prominent model is IaaS. This model provides pre-configured virtual machines (VMs) to the user from the cloud framework [3] [4]. An infinite quantity of computational resources and storage facilities can be attained by using VMs [5].With the increased demand for such cloud services, the number of users is expanding day by day [6]. Because of this, the unpredictable resource usage of VMs has increased, due to which the servers of cloud data centers get either over-utilized or un...

Multi-objective and multi constrained task scheduling framework for computational grids

“…To find non-dominated solutions for the multi-objective issue and to optimise and search for the best Grid resources, the Functional Code Sieve algorithm is used. Similarly, various task scheduling algorithms 40 – 47 based on multi-objective optimization are studied.…”