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A Multi-Objective Based Scheduling Framework for Effective Resource Utilization in Cloud Computing
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DOI：10.1109/ACCESS.2023.3266294.png)
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Abstract
En 中文
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.
Keywords:
Task analysis
Cloud computing
Heuristic algorithms
Processor scheduling
Resource management
Dynamic scheduling
Computational modeling
Crow swarm optimization
linear scaling
Manhattan distance
partitioning around medoid
recurrent neural network
workflow scheduling
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3.6
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9.8W
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29.4W
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