Abstract
The jobs processed in cloud computing systems may consist of multiple associated tasks which need to be executed under ordering constraints. The tasks of each job are run on different nodes, and communication is required to transfer data between nodes. The processing and communication capacities of different components have great heterogeneity. For multiple jobs, simple task scheduling policies cannot fully utilize cloud resources and hence may degrade the performance of job processing. Therefore, careful multi-job task scheduling is critical to achieve efficient job processing. The performance of existing research on associated task scheduling for multiple jobs needs to be improved. In this paper, we tackle the problem of associated task scheduling of multiple jobs with the aim to minimize jobs’ makespan. We propose a task Duplication and Insertion based List Scheduling algorithm (DILS) which incorporates dynamic finish time prediction, task replication, and task insertion. The algorithm dynamically schedules the tasks based on the finish time of scheduled tasks, replicates some of the tasks on different nodes, and inserts the tasks into idle time slots to expedite successive task execution. We finally conduct experiments through simulations. Experimental results demonstrate that the proposed algorithm can effectively reduce the jobs’ makespan.
This work was partly supported by the National Key Research Development Plan of China under Grant 2018YFB2000505 and the Key Research and Development Project in Anhui Province under Grant 201904a06020024.
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Fan, Y., Wang, L., Chen, J., Jin, Z., Shi, L., Xu, J. (2020). Multi-job Associated Task Scheduling Based on Task Duplication and Insertion for Cloud Computing. In: Yu, D., Dressler, F., Yu, J. (eds) Wireless Algorithms, Systems, and Applications. WASA 2020. Lecture Notes in Computer Science(), vol 12384. Springer, Cham. https://doi.org/10.1007/978-3-030-59016-1_10
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