Journal of Computer Science and Technology ›› 2017, Vol. 32 ›› Issue (6): 1231-1249.doi: 10.1007/s11390-017-1797-9

• Regular Paper • Previous Articles     Next Articles

Effective Query Grouping Strategy in Clouds

Qin Liu1,2, Member, CCF, Yuhong Guo3, Jie Wu4, Fellow, IEEE, Guojun Wang5,*, Distinguished Member, CCF, Member, ACM, IEEE   

  1. 1 College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China;
    2 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications Beijing 100876, China;
    3 School of Computer Science, Carleton University, Ottawa, ON K155 B6, Canada;
    4 Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, U.S.A.;
    5 School of Computer Science and Educational Software, Guangzhou University, Guangzhou 510006, China
  • Received:2016-05-18 Revised:2017-01-12 Online:2017-11-05 Published:2017-11-05
  • Contact: Guojun Wang
  • About author:Qin Liu received her in 2004 from Hunan Normal University,Changsha,and her in 2007 and in 2012 both from Central South University,Changsha,all in computer science.She was a visiting student at Temple University,Philadelphia.Her research interests include security and privacy issues in cloud computing.
  • Supported by:

    This research was supported in part by the National Science Foundation of USA under Grant Nos. CNS-1449860, CNS-1461932, CNS-460971, CNS-1439672, CNS-1301774, and ECCS-1231461, the National Natural Science Foundation of China under Grant Nos. 61632009, 61472451, 61402161, 61472131, 61272151, and 61272546, the Hunan Provincial Natural Science Foundation of China under Grant No. 2015JJ3046, and the Open Foundation of State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) under Grant No. SKLNST-2016-2-20.

As the demand for the development of cloud computing grows, more and more organizations have outsourced their data and query services to the cloud for cost-saving and flexibility. Suppose an organization that has a great number of users querying the cloud-deployed multiple proxy servers to achieve cost efficiency and load balancing. Given n queries, each of which is expressed as several keywords, and k proxy servers, the problem to be solved is how to classify n queries into k groups, in order to minimize the difference between each group and the number of distinct keywords in all groups. Since this problem is NP-hard, it is solved in mathematic and heuristic ways. Mathematic grouping uses a local optimization method, and heuristic grouping is based on k-means. Specifically, two extensions are provided:the first one focuses on robustness, i.e., each user obtains search results even if some proxy servers fail; the second one focuses on benefit, i.e., each user can retrieve as many files as possible that may be of interest without increasing the sum. Extensive evaluations have been conducted on both a synthetic dataset and real query traces to verify the effectiveness of our strategies.

Key words: cloud computing; cost efficiency; load balancing; robustness; benefit;

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