Browse > Article
http://dx.doi.org/10.3745/KTSDE.2013.2.6.383

Dynamic Clustering based Optimization Technique and Quality Assessment Model of Mobile Cloud Computing  

Kim, Dae Young (숭실대학교 컴퓨터학과)
La, Hyun Jung (숭실대학교 모바일 서비스 소프트웨어공학 센터)
Kim, Soo Dong (숭실대학교 컴퓨터학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.2, no.6, 2013 , pp. 383-394 More about this Journal
Abstract
As a way of augmenting constrained resources of mobile devices such as CPU and memory, many works on mobile cloud computing (MCC), where mobile devices utilize remote resources of cloud services or PCs, have been proposed. Typically, in MCC, many nodes with different operating systems and platform and diverse mobile applications or services are located, and a central manager autonomously performs several management tasks to maintain a consistent level of MCC overall quality. However, as there are a larger number of nodes, mobile applications, and services subscribed by the mobile applications and their interactions are extremely increased, a traditional management method of MCC reveals a fundamental problem of degrading its overall performance due to overloaded management tasks to the central manager, i.e. a bottle neck phenomenon. Therefore, in this paper, we propose a clustering-based optimization method to solve performance-related problems on large-scaled MCC and to stabilize its overall quality. With our proposed method, we can ensure to minimize the management overloads and stabilize the quality of MCC in an active and autonomous way.
Keywords
Mobile Cloud Computing; Clustering; Logical Nearness; k-means Algorithm; Optimization;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. Fernando, S.W. Loke, and W. Rahayu, "Mobile Cloud Computing: A Survey," Future Generation Computer Systems, Vol.29, pp.84-106, 2012, doi: 10.1016/j.future.2012.05.023.   DOI   ScienceOn
2 L. Guan, X. Ke, M. Song, and J. Song, "A Survey of Research on Mobile Cloud Computing," Proc. 2011 10th IEEE/ACIS International Conf. on Computer and Information Science (ICIS 2011), pp.387-392, Dec. 2011, doi: 10.1109/ICIS.2011.67.   DOI
3 Y. Natchetoi, V. Kaufman, A. Shapiro, "Service-Oriented Architecture for Mobile Applications," Proc. 1st Int'l Workshop on Software architectures and mobility (SAM 2008), pp.27-32, 2008, doi: 10.1145/1370888.1370896.   DOI
4 Wikipedia, Dec., 2012,
5 M. Gerla and J.T.C. Tsai, "Multicluster, mobile, multimedia radio network, Wireless Networks", Wireless Networks, Vol.1, pp.255-265. 1995, doi: 10.1007/BF01200845.   DOI
6 D. Baker, and A. Ephremides, "The Architectural Organization of a Mobile Radio Network via a Distributed Algorithm," IEEE Transactions on Communications, Vol.29, pp.1694-1701, 1981, doi: 10.1109/TCOM.1981.1094909.   DOI
7 A. Amis, R. Prakash, T. Vuong, and D. Huynh, "Max-Min D-Cluster Formation in Wireless Ad Hoc Networks," Proc. 9th Annual Joint Conf. of the IEEE Computer and Communications Societies(INFOCOM 2000), Vol.1, pp.32-41, 2000, doi: 10.1109/INFCOM.2000.832171.   DOI
8 P. Harrington, Machine Learning in Action, Manning Publication, 2012.
9 I.H. Witten, E. Frank, and M.A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, Morgan Kaufmann, 2011.