Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (1): 185-206.doi: 10.1007/s11390-019-1890-3

Special Issue: Data Management and Data Mining; Computer Networks and Distributed Computing

• Regular Paper • Previous Articles     Next Articles

On Maximum Elastic Scheduling in Cloud-Based Data Center Networks for Virtual Machines with the Hose Model

Shuai-Bing Lu1,2, Student Member, IEEE, Jie Wu2,*, Fellow, IEEE, Huan-Yang Zheng2, and Zhi-Yi Fang1   

  1. 1 College of Computer Science and Technology, Jilin University, Changchun 13012, China;
    2 Department of Computer and Information Sciences, Temple University, Philadelphia, PA19122, U.S.A.
  • Received:2018-07-15 Revised:2018-08-30 Online:2019-01-05 Published:2019-01-12
  • Contact: Jie Wu
  • About author:Shuai-Bing Lu is currently a Ph.D. candidate in computer science and technology of Jilin University, Changchun. She is supported by the China Scholarship Council as a visiting scholar supervised by Prof. Jie Wu in the Department of Computer and Information Sciences at Temple University (2016-2018), Philadelphia. She is a student member of IEEE. Her current research focuses on distributed computing, cloud computing and fog computing.
  • Supported by:
    This research was supported in part by the National Science Foundation (NSF) of United States under Grant Nos. CNS 1757533, CNS 1629746, CNS 1564128, CNS 1449860, CNS 1461932, CNS 1460971, ⅡP 1439672, and CSC 20163100.

With the growing popularity of cloud-based data center networks (DCNs), task resource allocation has become more and more important to the efficient use of resource in DCNs. This paper considers provisioning the maximum admissible load (MAL) of virtual machines (VMs) in physical machines (PMs) with underlying tree-structured DCNs using the hose model for communication. The limitation of static load distribution is that it assigns tasks to nodes in a once-and-for-all manner, and thus requires a priori knowledge of program behavior. To avoid load redistribution during runtime when the load grows, we introduce maximum elasticity scheduling, which has the maximum growth potential subject to the node and link capacities. This paper aims to find the schedule with the maximum elasticity across nodes and links. We first propose a distributed linear solution based on message passing, and we discuss several properties and extensions of the model. Based on the assumptions and conclusions, we extend it to the multiple paths case with a fat tree DCN, and discuss the optimal solution for computing the MAL with both computation and communication constraints. After that, we present the provision scheme with the maximum elasticity for the VMs, which comes with provable optimality guarantee for a fixed flow scheduling strategy in a fat tree DCN. We conduct the evaluations on our testbed and present various simulation results by comparing the proposed maximum elastic scheduling schemes with other methods. Extensive simulations validate the effectiveness of the proposed policies, and the results are shown from different perspectives to provide solutions based on our research.

Key words: data center network (DCN); cloud; distributed algorithm; elasticity; hose model; optimization;

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