Adaptive Computing Resource Allocation for Mobile Cloud Computing

Hongbin Liang, Tianyi Xing, Lin X. Cai, Dijiang Huang, Daiyuan Peng and Yan Liu

International Journal of Distributed Sensor Networks, 2013, vol. 9, issue 4, 181426

Abstract: Mobile cloud computing (MCC) enables mobile devices to outsource their computing, storage and other tasks onto the cloud to achieve more capacities and higher performance. One of the most critical research issues is how the cloud can efficiently handle the possible overwhelming requests from mobile users when the cloud resource is limited. In this paper, a novel MCC adaptive resource allocation model is proposed to achieve the optimal resource allocation in terms of the maximal overall system reward by considering both cloud and mobile devices. To achieve this goal, we model the adaptive resource allocation as a semi-Markov decision process (SMDP) to capture the dynamic arrivals and departures of resource requests. Extensive simulations are conducted to demonstrate that our proposed model can achieve higher system reward and lower service blocking probability compared to traditional approaches based on greedy resource allocation algorithm. Performance comparisons with various MCC resource allocation schemes are also provided.

Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1155/2013/181426 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:9:y:2013:i:4:p:181426

DOI: 10.1155/2013/181426

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().