An evaluation of cloud-based mobile services with limited capacity: a linear approach

Mobile computing is pervading networks at an increasing speed as mobile devices are used with diverse forms of wireless technologies to access data. This paper evaluates different cloud-supported mobile services subject to limited capacity, as the selection of a service may introduce additional costs, such as those that derive from the additional amount of memory required for processing. In this context, a novel linear model and algorithm in the mobile cloud computing environment are proposed from the service capacity perspective, considering the cost that derives from the unused capacity. The probability of overutilization or underutilization of the selected service is also researched, once a linear growth in the number of users occurs. To further make effective and strategic investment decisions when selecting the appropriate cloud-based mobile service to lease off, the model formulation is based on a cost–benefit appraisal. The proposed quantification approach is evaluated with respect to four different case scenarios, exploiting a web tool that has been developed as a proof of concept and implementing the algorithm to calculate and compare the benefits and costs in the mobile cloud-based service level.

[1]  John M. Cioffi,et al.  Increase in capacity of multiuser OFDM system using dynamic subchannel allocation , 2000, VTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference Proceedings (Cat. No.00CH37026).

[2]  Jie Qiu,et al.  The Method and Tool of Cost Analysis for Cloud Computing , 2009, 2009 IEEE International Conference on Cloud Computing.

[3]  George Mastorakis,et al.  The Technical Debt in Cloud Software Engineering: A Prediction-Based and Quantification Approach (.pdf Document) , 2015 .

[4]  George Mastorakis,et al.  Predicting and quantifying the technical debt in cloud software engineering , 2014, 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).

[5]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[6]  J. Wenny Rahayu,et al.  Mobile cloud computing: A survey , 2013, Future Gener. Comput. Syst..

[7]  Rajkumar Buyya,et al.  A cost-benefit analysis of using cloud computing to extend the capacity of clusters , 2010, Cluster Computing.

[8]  Franck Cappello,et al.  Cost-benefit analysis of Cloud Computing versus desktop grids , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[9]  Pravin Varaiya,et al.  Capacity of fading channels with channel side information , 1997, IEEE Trans. Inf. Theory.

[10]  Baruch Awerbuch,et al.  Global flow control for wide area overlay networks: a cost-benefit approach , 2002, 2002 IEEE Open Architectures and Network Programming Proceedings. OPENARCH 2002 (Cat. No.02EX571).

[11]  Georgios B. Giannakis,et al.  Approaching MIMO channel capacity with reduced-complexity soft sphere decoding , 2004, 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733).

[12]  Moe Z. Win,et al.  Capacity of MIMO systems with antenna selection , 2005 .

[13]  Rajkumar Buyya,et al.  Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters , 2009, HPDC '09.

[14]  Rami Bahsoon,et al.  A decentralized self-adaptation mechanism for service-based applications in the cloud , 2013, IEEE Transactions on Software Engineering.

[15]  Huijun Gao,et al.  ${\cal H}_{\infty}$ Estimation for Uncertain Systems With Limited Communication Capacity , 2007, IEEE Transactions on Automatic Control.

[16]  Michael Dinitz,et al.  Maximizing Capacity in Arbitrary Wireless Networks in the SINR Model: Complexity and Game Theory , 2009, IEEE INFOCOM 2009.