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Multi-prediction based scheduling for hybrid workloads in the cloud data center

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Abstract

Cloud computing can leverage over-provisioned resources that are wasted in traditional data centers hosting production applications by consolidating tasks with lower QoS and SLA requirements. However, the dramatic fluctuation of workloads with lower QoS and SLA requirements may impact the performance of production applications. Frequent task eviction, killing and rescheduling operations also waste CPU cycles and create overhead. This paper aims to schedule hybrid workloads in the cloud data center to reduce task failures and increase resource utilization. The multi-prediction model, including the ARMA model and the feedback based online AR model, is used to predict the current and the future resource availability. Decision to accept or reject a new task is based on the available resources and task properties. Evaluations show that the scheduler can reduce the host overload and failed tasks by nearly 70%, and increase effective resource utilization by more than 65%. The task delay performance degradation is also acceptable.

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Acknowledgements

This work is supported by the National Key project of Scientific and Technical Supporting Programs of China (Grant No. 2014BAK15B01); the Cosponsored Project of Beijing Committee of Education; Engineering Research Center of Information Networks, Ministry of Education.

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Haiou Jiang, Haihong E & Meina Song

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  1. Haiou Jiang
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  2. Haihong E
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  3. Meina Song
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Correspondence to Haiou Jiang.

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Jiang, H., E, H. & Song, M. Multi-prediction based scheduling for hybrid workloads in the cloud data center. Cluster Comput 21, 1607–1622 (2018). https://doi.org/10.1007/s10586-018-2265-1

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  • DOI: https://doi.org/10.1007/s10586-018-2265-1

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