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VirtCO: Joint Coflow Scheduling and Virtual Machine Placement in Cloud Data Centers

Dian Shen, Junzhou Luo, Fang Dong(), Junxue Zhang
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China.
SING Group, Hong Kong University of Science and Technology, Hong Kong 999077, China.
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Abstract

Cloud data centers, such as Amazon EC2, host myriad big data applications using Virtual Machines (VMs). As these applications are communication-intensive, optimizing network transfer between VMs is critical to the performance of these applications and network utilization of data centers. Previous studies have addressed this issue by scheduling network flows with coflow semantics or optimizing VM placement with traffic considerations. However, coflow scheduling and VM placement have been conducted orthogonally. In fact, these two mechanisms are mutually dependent, and optimizing these two complementary degrees of freedom independently turns out to be suboptimal. In this paper, we present VirtCO, a practical framework that jointly schedules coflows and places VMs ahead of VM launch to optimize the overall performance of data center applications. We model the joint coflow scheduling and VM placement optimization problem, and propose effective heuristics for solving it. We further implement VirtCO with OpenStack and deploy it in a testbed environment. Extensive evaluation of real-world traces shows that compared with state-of-the-art solutions, VirtCO greatly reduces the average coflow completion time by up to 36.5%. This new framework is also compatible with and readily deployable within existing data center architectures.

Keywords

cloud computingdata centercoflow schedulingVirtual Machine (VM) placement

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Tsinghua Science and Technology
Volume 24 Issue 5,
October 2019
Pages 630-644
DOI: 10.26599/TST.2018.9010098
Cite this article:
Shen D, Luo J, Dong F, et al. VirtCO: Joint Coflow Scheduling and Virtual Machine Placement in Cloud Data Centers. Tsinghua Science and Technology, 2019, 24(5): 630-644. https://doi.org/10.26599/TST.2018.9010098
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