2024 Volume 14 Issue 1
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Cuiyan Yi, Tianhao Zhao, Xingjuan Cai, Jinjun Chen. RESEARCH ON SCHEDULING OF TWO TYPES OF TASKS IN MULTI-CLOUD ENVIRONMENT BASED ON MULTI-TASK OPTIMIZATION ALGORITHM[J]. Journal of Applied Analysis & Computation, 2024, 14(1): 436-457. doi: 10.11948/20230266
Citation: Cuiyan Yi, Tianhao Zhao, Xingjuan Cai, Jinjun Chen. RESEARCH ON SCHEDULING OF TWO TYPES OF TASKS IN MULTI-CLOUD ENVIRONMENT BASED ON MULTI-TASK OPTIMIZATION ALGORITHM[J]. Journal of Applied Analysis & Computation, 2024, 14(1): 436-457. doi: 10.11948/20230266
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RESEARCH ON SCHEDULING OF TWO TYPES OF TASKS IN MULTI-CLOUD ENVIRONMENT BASED ON MULTI-TASK OPTIMIZATION ALGORITHM

  • 1.

    Shanxi Key Laboratory of Big Data Analysis and Parallel Computing, Taiyuan University of Science and Technology, Taiyuan, Shanxi 030024, China

  • 2.

    School of State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China

  • 3.

    Department of Computing Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia

    Abstract

  • The multi-cloud environment (MCE) tasks can be classified as CPU-intensive or I/O-intensive. Using a single model to handle two tasks often results in system performance issues due to mismatches between task requirements and resource demands, caused by differing data characteristics. In this paper, a multi-task multi-objective optimization (MTMO) model is constructed. A multi-objective evolutionary algorithm with quadratic crossover is used to simultaneously schedule two types of tasks. This improves scheduling efficiency. First, according to the different data characteristics of tasks in MCE, tasks are separated into CPU-intensive tasks with large amounts of computation and high demand for CPU resources and I/O-intensive tasks that require frequent memory access. Different multi-objective optimization models are constructed according to the characteristics of per-task. Secondly, each multi-objective optimization model is constructed as a sub-task in a multi-task environment to build a MTMO model. Then, a multi-objective multi-factor evolutionary algorithm based on quadratic crossover, Ⅰ-MOMFEA-Ⅱ, is proposed to schedule the two types of tasks simultaneously. Finally, the proposed algorithm in this paper improved cost, time, and energy consumption for CPU-intensive tasks by 7.6%, 20.1%, and 16.1% respectively, for I/O-intensive tasks, it improved cost, time, and VM throughput by 10%, 17.7%, and 36.5% respectively. The experimental results from simulations confirm the effectiveness of Ⅰ-MOMFEA-Ⅱ in elevating task scheduling productivity.

    MSC: 68M20, 68W99, 93A30
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