Abstract
Cloud computing has a powerful ability to handle a large number of tasks. Correspondingly, it also consumes a lot of energy. Reducing the energy consumption of cloud service platforms while ensuring the quality of service has become a crucial issue. In this paper, we propose a heuristic energy-saving scheduling algorithm named Real-time Multi-workflow Energy-efficient Scheduling (RMES) with the aim to minimize the total energy consumption in container cloud. RMES executes tasks as parallel as possible to enhance the resource utilization of the running machines in cluster, therefore reducing the time of the global process, saving energy as a result. RMES takes advantage of the affinity between containers and machines to meet the resource quantity and performance requirements of containers during scheduling. In order to follow the change of the system state overtime, we introduce the re-scheduling mechanism, which can automatically adjust the scheduling decisions of the tasks that have not yet been executed in the scheduling scheme. The experimental results show that RMES has obvious advantages over other scheduling algorithms in terms of energy consumption and success ratio.
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This work is supported by the Shenzhen Science and Technology Program under Grant No. GXWD20220817124827001, and No. JCYJ20210324132406016.
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Appendices
Appendix for “An Energy-Efficient Scheduling Method for Real-Time Multi-workflow in Container Cloud”
A Detailed Pseudocode of the Proposed Algorithm
The detailed procedure is given in Algorithm 1. \(task_{ready}\) and \(task_{scheduled}\) represent task sets of type task ready and task scheduled in the task pool, respectively. \({<}Q_{n,i,j}, c_i, p_j{>}\) means assign \(task_n\) to \(c_i\) running in \(p_j\). Firstly, we evaluate the task state in the system and judge whether to reschedule the scheduled tasks according to the current task state of the system, as shown in lines 2–10 of Algorithm 1. Then, for the task to be scheduled, the system calculates the Q value of the task deployed on the existing container, and deploys the task to the container with the lowest Q value, as shown in lines 11–24 of Algorithm 1. For a task without a suitable container to run, the system will create a new container for it, calculate the Q value of the container deployed to each PM in the cluster, and select the PM with the lowest Q value to run the container, as shown in lines 25–41 of Algorithm 1.
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Sun, Z., Li, Z., Gu, C., Huang, H. (2024). An Energy-Efficient Scheduling Method for Real-Time Multi-workflow in Container Cloud. In: Wu, W., Guo, J. (eds) Combinatorial Optimization and Applications. COCOA 2023. Lecture Notes in Computer Science, vol 14461. Springer, Cham. https://doi.org/10.1007/978-3-031-49611-0_12
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