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Tsinghua Science and Technology 2019, 24(5): 630-644 https://doi.org/10.26599/TST.2018.9010098
Open Access | Issue | Published: 29 April 2019

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

Show Author's Information 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.
Keywords:
cloud computing, data center, coflow scheduling, Virtual Machine (VM) placement
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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Abstract Full text About this article

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.


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About this article

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

Show Author's information 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.

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 computing, data center, coflow scheduling, Virtual Machine (VM) placement

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Received: 02 April 2018
Accepted: 01 May 2018
Published: 29 April 2019
Issue date: October 2019

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© The author(s) 2019

Acknowledgements

Acknowledgements

This work was supported by the National Key R&D Program of China (No. 2017YFB1003000), the National Natural Science Foundation of China (Nos. 61572129, 61602112, 61502097, 61702096, 61320106007, and 61632008), the International S&T Cooperation Program of China (No. 2015DFA10490), the National Science Foundation of Jiangsu Province (Nos. BK20160695 and BK20170689), the Jiangsu Provincial Key Laboratory of Network and Information Security (No. BM2003201), the Key Laboratory of Computer Network and Information Integration of Ministry of Education of China (No. 93K-9), and partially supported by the Collaborative Innovation Center of Novel Software Technology and Industrialization and Collaborative Innovation Center of Wireless Communications Technology.

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