期刊
SIMULATION MODELLING PRACTICE AND THEORY
卷 119, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.simpat.2022.102589
关键词
Cloud computing; Fog computing; Maximized reliability; Workflow scheduling; Distributed computing
This paper proposes a method for modeling scientific workflow scheduling as a multi-objective optimization problem to balance energy consumption and scheduling reliability. By placing tasks with low computational requirements on fog resources and using a reliability-aware stepwise performance-to-power ratio procedure to reduce energy consumption. Simulation results show that this method has higher reliability under minimized energy constraints.
The ever-expanding processing of large-scale scientific workflow applications has increased power consumption and reliability costs. However, considering the complexity of computational data processing in fog-cloud computing, existing workflow scheduling techniques conceived for traditional distributed systems is limited and powerless. Therefore, for profit-driven cloud service providers, minimizing power consumption and maximizing scheduling reliability of scientific workflows is a pressing concern. In this paper, we model the scientific workflow scheduling as a multi-objective optimization problem to compromise the conflicting energy consumption and scheduling reliability solution in two steps. The first step places the tasks with lower computational requirements on fog resources and computationally complex tasks on cloud resources, avoiding resources with high failure rates. In the second step, we adapt the Reliability-Aware stepwise Performance-to-Power Ratio procedure to further reduce energy consumption by sampling the machine's utilization levels with a distinguished Performance to-Power Ratio (PPR). The PPR is defined as the calculation of the number of transactions performed at a specific time divided by the active power consumed. Simulation results under synthesized and real-world workflow applications demonstrated that dis-RMEE achieved considerably higher reliability values under the minimized energy constraint compared with some opponent workflow mapping algorithms.
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