A Cluster-Based Cooperative Co-Evolutionary Algorithm for Multiobjective Workflow Scheduling in a Cloud Environment

Shuo Qin

doi:10.1109/TASE.2022.3183681

A Cluster-Based Cooperative Co-Evolutionary Algorithm for Multiobjective Workflow Scheduling in a Cloud Environment

2023-07-01

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DOI：10.1109/TASE.2022.3183681

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The cloud workflow scheduling problem has important applications in modern commercial and industrial areas. In the public cloud environment, the workflow suffers from security threats because of the multiple tenants and the distribution of computational resources. This paper models cloud workflow scheduling as a novel multi-objective optimization problem that aims to minimize execution time, cost, and risk. Due to the complexity of the considered problem, a multi-objective cluster-based cooperative co-evolutionary (CBCC) algorithm with several novel designs is proposed. First, a new initialization strategy is presented to generate potential non-dominated solutions. Based on the cluster-based multi-objective optimization framework, a novel collaboration model is proposed, and it adopts four populations to address the subproblems, respectively. Moreover, a diversification strategy is designed to maintain the diversity of the global archive. Furthermore, a problem-specific intensification strategy is designed to intensify the potential solutions. A comprehensive computational and statistical campaign was carried out to verify the performance of CBCC. The results show that the proposed CBCC outperforms several meta-heuristics adapted from closely related scheduling models in the literature by a significantly considerable margin. Note to Practitioners-This paper describes a novel approach called CBCC for minimizing the cost, time, and risk when scheduling a workflow in the cloud environment. CBCC seamlessly combines the cluster-based multi-objective optimization framework and several problem-specific components such as initialization, diversification, and intensification strategies. As the considered problem has not been previously addressed in the literature, five state-of-the-art algorithms for closely related problems, which include I_MaOPSO (improved many objective particle swarm optimization), EMS-C (evolutionary multi-objective scheduling for cloud), ch-PICEA-g (enhanced multi-objective co-evolutionary algorithm), VaEA (vector angle-based evolutionary algorithm), and DQN-based MARL (Deep-Q-network-based Multi-agent Reinforcement Learning) are adopted as baselines. The results demonstrate that CBCC significantly outperforms the baselines with a 95% confidence level.

Keywords:

Cloud computing

Optimization

Task analysis

Costs

Processor scheduling

Security

Clustering algorithms

Workflow scheduling

cloud computing

security threats

multiobjective optimization

cooperative co-evolutionary algorithm

A Cluster-Based Cooperative Co-Evolutionary Algorithm for Multiobjective Workflow Scheduling in a Cloud Environment

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