Adaptive resource discovery in mobile cloud computing

Mobile cloud computing (MCC) is aimed at integrating mobile devices with cloud computing. It is one of the most important concepts that have emerged in the last few years. Mobile devices, in the traditional agent-client architecture of MCC, only utilize resources in the cloud to enhance their functionalities. However, modern mobile devices have many more resources than before. As a result, researchers have begun to consider the possibility of mobile devices themselves sharing resources. This is called the cooperation-based architecture of MCC. Resource discovery is one of the most important issues that need to be solved to achieve this goal. Most of the existing work on resource discovery has adopted a fixed choice of centralized or flooding strategies. Many improved versions of energy-efficient methods based on both strategies have been proposed by researchers due to the limited battery life of mobile devices. This paper proposes a novel adaptive method of resource discovery from a different point of view to distinguish it from existing work. The proposed method automatically transforms between centralized and flooding strategies to save energy according to different network environments. Theoretical models of both energy consumption and the quality of response information are presented in this paper. A heuristic algorithm was also designed to implement the new adaptive method of resource discovery. The results from simulations demonstrated the effectiveness of the strategy and the efficiency of the proposed heuristic method.

[1]  Nitin H. Vaidya,et al.  Efficient content location in wireless ad hoc networks , 2004, IEEE International Conference on Mobile Data Management, 2004. Proceedings. 2004.

[2]  Ruay-Shiung Chang,et al.  A semantic service discovery approach for ubiquitous computing , 2009, J. Intell. Manuf..

[3]  Ellen W. Zegura,et al.  Computing in cirrus clouds: the challenge of intermittent connectivity , 2012, MCC '12.

[4]  Tao Zhang,et al.  An adaptive low-overhead resource discovery protocol for mobile ad-hoc networks , 2011, Wirel. Networks.

[5]  Françoise Sailhan,et al.  Scalable Service Discovery for MANET , 2005, Third IEEE International Conference on Pervasive Computing and Communications.

[6]  George C. Polyzos,et al.  Service discovery for mobile Ad Hoc networks: a survey of issues and techniques , 2008, IEEE Communications Surveys & Tutorials.

[7]  Ramachandran Venkatesan,et al.  Performance analysis of BitTorrent enabled ad hoc network routing , 2009, IWCMC.

[8]  Vijay Erramilli,et al.  Energy Efficient Offloading of 3G Networks , 2011, 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems.

[9]  Charles E. Perkins,et al.  Service Location Protocol, Version 2 , 1999, RFC.

[10]  Shaobin Cai,et al.  RICFFP: An Efficient Service Discovery Protocol for MANETs , 2004, EUC.

[11]  J. Antonio García-Macías,et al.  Service discovery in mobile ad-hoc networks: better at the network layer? , 2005, 2005 International Conference on Parallel Processing Workshops (ICPPW'05).

[12]  Shin-Dug Kim,et al.  Personalized Service Discovery in Ubiquitous Computing Environments , 2009, IEEE Pervasive Computing.

[13]  Koen Vanthournout,et al.  A taxonomy for resource discovery , 2004, Personal and Ubiquitous Computing.

[14]  Antonio F. Gómez-Skarmeta,et al.  Mobile Digcovery: A Global Service Discovery for the Internet of Things , 2013, 2013 27th International Conference on Advanced Information Networking and Applications Workshops.

[15]  Charles E. Perkins,et al.  Service Location Protocol , 1997, RFC.

[16]  Paolo Santi,et al.  Enabling Efficient Peer-to-Peer Resource Sharing in Wireless Mesh Networks , 2010, IEEE Transactions on Mobile Computing.

[17]  Ramachandran Venkatesan,et al.  Probability Distribution of Multi-Hop Multipath connection in a Random Network , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[18]  Robert Cole,et al.  Computer Communications , 1982, Springer New York.

[19]  Ke Xu,et al.  A Survey of Research on Mobile Cloud Computing , 2011, 2011 10th IEEE/ACIS International Conference on Computer and Information Science.