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A cost-effective scheme supporting adaptive service migration in cloud data center

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Abstract

Cloud computing as an emerging technology promises to provide reliable and available services on demand. However, offering services for mobile requirements without dynamic and adaptive migration may hurt the performance of deployed services. In this paper, we propose MAMOC, a cost-effective approach for selecting the server and migrating services to attain enhanced QoS more economically. The goal of MAMOC is to minimize the total operating cost while guaranteeing the constraints of resource demands, storage capacity, access latency and economies, including selling price and reputation grade. First, we devise an objective optimal model with multi-constraints, describing the relationship among operating cost and the above constraints. Second, a normalized method is adopted to calculate the operating cost for each candidate VM. Then we give a detailed presentation on the online algorithm MAMOC, which determines the optimal server. To evaluate the performance of our proposal, we conducted extensive simulations on three typical network topologies and a realistic data center network. Results show that MAMOC is scalable and robust with the larger scales of requests and VMs in cloud environment. Moreover, MAMOC decreases the competitive ratio by identifying the optimal migration paths, while ensuring the constraints of SLA as satisfying as possible.

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Authors and Affiliations

  1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Bing Yu, Yanni Han, Xu Zhou & Zhen Xu

  2. International School of Software, Beijing Institute of Technology, Beijing, 100081, China

    Hanning Yuan

Authors
  1. Bing Yu
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  2. Yanni Han
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  3. Hanning Yuan
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  4. Xu Zhou
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  5. Zhen Xu
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Corresponding author

Correspondence to Yanni Han.

Additional information

Bing Yu received her BS in information and computing science form Beijing University of Posts and Telecommunications, China in 2012. She is currently pursuing her PhD in the State Key Laboratory of Information Security at the Institute of Information Engineering, Chinese Academy of Sciences, China. Her major research interests are in the areas of service migration and resource management.

Yanni Han is an assistant researcher in the State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, China. She obtained her PhD in 2010 from BeiHang University, China. Her current research interests include resource management, network virtualization and future internet.

Hanning Yuan is an associate professor in Beijing Institute of Technology, China. Her research interests include ecommerce and data mining.

Xu Zhou received his BS and MS from the Department of Mathematics, Sichuan University, and received his PhD from University of Electronic Science and Technology of China (UESTC), China in 2005. Currently, he is an associate professor in the Institute of Information Engineering, Chinese Academy of Sciences, China. His research interests include content network and future internet architecture.

Zhen Xu is a professor in the State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences (CAS), China. He received his PhD in 2005 from the Institute of Software, CAS. His research interests include database security, trusted computing and network security.

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Yu, B., Han, Y., Yuan, H. et al. A cost-effective scheme supporting adaptive service migration in cloud data center. Front. Comput. Sci. 9, 875–886 (2015). https://doi.org/10.1007/s11704-015-4592-1

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  • DOI: https://doi.org/10.1007/s11704-015-4592-1

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