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Scheduling multi-tenant cloud workflow tasks with resource reliability

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Abstract

Resource reliability is crucial in scheduling workflow instances for different tenants. Both cloud resource reliability and precedence constraints in workflows bring about great challenges for these kinds of scheduling problems. In this paper, we construct a hybrid resource reliability model which is adaptively evaluated in every time window. The objective is to optimize the QoS (quality of service) for tenants which is measured by the introduced AISE (all instance success entropy) index. A scheduling algorithmic framework is proposed for the studied workflows which consider cloud resource reliability. Deadline and budget division (BD) methods are presented to divide deadlines and budgets of instances into those of tasks. A tenant sequence method is developed to determine the order of tenants. A task allocation strategy is investigated to schedule tasks that are ready to appropriate available resources. Parameters and algorithm component candidates are statistically calibrated over a comprehensive set of random instances using the analysis of variance technique. The performance of the proposed algorithm is also evaluated in practical instances.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (Grant No. 2017YFB1400800), National Natural Science Foundation of China (Grant Nos. 61872077, 61832004), and Collaborative Innovation Center of Wireless Communications Technology. Rubén Ruiz was partly supported by the Spanish Ministry of Science, Innovation, and Universities, under the Project “OPTEP-Port Terminal Operations Optimization” (Grant No. RTI2018-094940-B-I00) Financed with FEDER Funds.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, 211189, China

    Xiaoping Li & Dongyuan Pan

  2. Institute of Data and Knowledge Engineering, School of Computer and Information Engineering, Henan University, Kaifeng, 475004, China

    Yadi Wang

  3. Grupo de Sistemas de Optimización Aplicada, Universitat Politècnica de València, Camino de Vera s/n, València, 46022, Spain

    Rubén Ruiz

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  1. Xiaoping Li
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  2. Dongyuan Pan
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  3. Yadi Wang
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  4. Rubén Ruiz
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Correspondence to Xiaoping Li.

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Li, X., Pan, D., Wang, Y. et al. Scheduling multi-tenant cloud workflow tasks with resource reliability. Sci. China Inf. Sci. 65, 192106 (2022). https://doi.org/10.1007/s11432-020-3295-2

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  • DOI: https://doi.org/10.1007/s11432-020-3295-2

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