Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61289
Title: Cost aware service placement and load dispatching in mobile cloud systems
Authors: Yang, L 
Cao, J 
Liang, G
Han, X
Keywords: Load dispatching
Mobile cloud computing
Service placement
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on computers, 2016, v. 65, no. 5, 7110527, p. 1440-1452 How to cite?
Journal: IEEE transactions on computers 
Abstract: With proliferation of smart phones and an increasing number of services provisioned by clouds, it is commonplace for users to request cloud services from their mobile devices. Accessing services directly from the Internet data centers inherently incurs high latency due to long RTTs and possible congestions in WAN. To lower the latency, some researchers propose to 'cache' the services at edge clouds or smart routers in the access network which are closer to end users than the Internet cloud. Although 'caching' is a promising technique, placing the services and dispatching users' requests in a way that can minimize the users' access delay and service providers' cost has not been addressed so far. In this paper, we study the joint optimization of service placement and load dispatching in the mobile cloud systems. We show this problem is unique to both the traditional caching problem in mobile networks and the content distribution problem in content distribution networks. We develop a set of efficient algorithms for service providers to achieve various trade-offs among the average latency of mobile users' requests, and the cost of service providers. Our solution utilizes user's mobility pattern and services access pattern to predict the distribution of user's future requests, and then adapt the service placement and load dispatching online based on the prediction. We conduct extensive trace driven simulations. Results show our solution not only achieves much lower latency than directly accessing service from remote clouds, but also outperforms other classical benchmark algorithms in term of the latency, cost and algorithm running time.
URI: http://hdl.handle.net/10397/61289
ISSN: 0018-9340
EISSN: 1557-9956
DOI: 10.1109/TC.2015.2435781
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