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

Multi-prediction based scheduling for hybrid workloads in the cloud data center 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. Keywords Cloud data center  Hybrid workloads  Task scheduling  Multi-prediction  ARMA model  Feedback based online AR model 1 Introduction center resources is an important cost factor for many organizations [3]. Cloud computing can solve the problem Data centers have gained significant popularity http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Cluster Computing Springer Journals

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

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Processor Architectures; Operating Systems; Computer Communication Networks
ISSN
1386-7857
eISSN
1573-7543
D.O.I.
10.1007/s10586-018-2265-1
Publisher site
See Article on Publisher Site

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. Keywords Cloud data center  Hybrid workloads  Task scheduling  Multi-prediction  ARMA model  Feedback based online AR model 1 Introduction center resources is an important cost factor for many organizations [3]. Cloud computing can solve the problem Data centers have gained significant popularity

Journal

Cluster ComputingSpringer Journals

Published: Jun 4, 2018

References

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