Reduction of Energy Consumption in Mobile Cloud Computing by ‎Classification of Demands and Executing in Different Data Centers

Document Type : Research Article


1 Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Iran Telecommunication Research Center, Tehran, Iran


 In recent years, mobile networks have faced with the increase of traffic demand. By emerging mobile applications and cloud computing, Mobile Cloud Computing (MCC) has been introduced. In this research, we focus on the 4th and 5th generation of mobile networks. Data Centers (DCs) are connected to each other by high-speed links in order to minimize delay and energy consumption. By considering a model of the geographical distribution of DCs which uses a wideband optical network, renewable energy and sharing resources for new generations of mobile networks, the real effect of issues on the consumed energy, cost, and profit in the mobile cloud computing are investigated. We derived a penalty function for cost and then by using Lyapunov optimization theorem; we designed an algorithm to minimize the average cost of energy consumption based on the online information in MCC. The time average cost is at most O(1/V) above the optimum target, while the average queue size is O(V). The parameter V can be tuned to make the time average cost as close to (or below) the optimum as desired. We designed three scenarios and two classes of applications to set up our simulation environment. The provided results illustrate the efficiency of our proposed scheme and validate the mathematical model.


Main Subjects

[1] K. Katsalis, N. Nikaein, E. Schiller, A. Ksentini, T. Braun, Network slices toward 5G communications: Slicing the LTE network, IEEE Communications Magazine, 55(8) (2017) 146-154.
[2] M. Hogan, F. Liu, A. Sokol, J. Tong, Nist cloud computing standards roadmap, NIST Special Publication, 35 (2011) 6-11.
[3] S. Ren, M. van der Schaar, Dynamic scheduling and pricing in wireless cloud computing, IEEE Transactions on Mobile Computing, 13(10) (2014) 2283-2292.
[4] I.B. Software, Workload automation: Helping Cloud Computing Take Flight, in, http://documents.bmc. com/products/documents/62/56/286256/286256. pdf,04.02.2017
[5] A. Hameed, A. Khoshkbarforoushha, R. Ranjan, P.P. Jayaraman, J. Kolodziej, P. Balaji, S. Zeadally, Q.M. Malluhi, N. Tziritas, A. Vishnu, A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems, Computing, 98(7) (2016) 751- 774.
[6] B.A. Bjerke, LTE-advanced and the evolution of LTE deployments, IEEE Wireless Communications, 18(5) (2011).
[7] I.F. Akyildiz, D.M. Gutierrez-Estevez, E.C. Reyes, The evolution to 4G cellular systems: LTE-Advanced, Physical communication, 3(4) (2010) 217-244.
[8] R. Ferzli, I. Khalife, Mobile cloud computing educational tool for image/video processing algorithms, in: Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop (DSP/SPE), 2011 IEEE, IEEE, 2011, pp. 529-533.
[9]Y. Wen, W. Zhang, H. Luo, Energy-optimal mobile application execution: Taming resource-poor mobile devices with cloud clones, in: INFOCOM, 2012 Proceedings IEEE, IEEE, 2012, pp. 2716-2720.
[10] P.J. Havinga, G.J. Smit, Energy.efficient wireless networking for multimedia applications, Wireless communications and mobile computing, 1(2) (2001) 165-184.
[11] Z. Liu, M. Lin, A. Wierman, S.H. Low, L.L. Andrew, Greening geographical load balancing, in: Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems, ACM, 2011, pp. 233-244.
[12] B. Guenter, N. Jain, C. Williams, Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning, in: INFOCOM, 2011 Proceedings IEEE, IEEE, 2011, pp. 1332-1340.
[13] S.W. Paper, The Seven Standards of Cloud Computing Service Delivery, in, pdf/datasheets/SevenStandards.pdf,07.02.2017.
[14] I.T.C.C. Standards, Cloud Computing Standards: Overview and ITU-T positioning, in, https://www.itu. int/dms_pub/itu-t/oth/06/5B/T065B00001C0043PDFE. pdf,10.09.2016.
[15] M. Lin, Z. Liu, A. Wierman, L.L. Andrew, Online algorithms for geographical load balancing, in: Green Computing Conference (IGCC), 2012 International, IEEE, 2012, pp. 1-10.
[16] A. Qureshi, R. Weber, H. Balakrishnan, J. Guttag, B. Maggs, Cutting the electric bill for internet-scale systems, in: ACM SIGCOMM computer communication review, ACM, 2009, pp. 123-134.
[17] H. L. Barroso, U, The Data Center as a Computer, Morgan & Claypool 2009.
[18] Y. Guo, Z. Ding, Y. Fang, D. Wu, Cutting down electricity cost in internet data centers by using energy storage, in: Global Telecommunications Conference (GLOBECOM 2011), 2011 IEEE, Ieee, 2011, pp. 1-5.
[19] M. Anastasopoulos, A. Tzanakaki, G. Zervas, B.R. Rofoee, R. Nejabati, D. Simeonidou, Virtualization over converged wireless, optical and IT elements in support of resilient cloud and mobile cloud services, in: European Conference and Exhibition on Optical Communication, Optical Society of America, 2012, pp. P5. 15.
[20] A. Tzanakaki, M.P. Anastasopoulos, G.S. Zervas, B.R. Rofoee, R. Nejabati, D. Simeonidou, Virtualization of heterogeneous wireless-optical network and IT infrastructures in support of cloud and mobile cloud services, IEEE Communications Magazine, 51(8) (2013) 155-161.
[21] E. Skejić, O. Dšindo, D. Demirović, Virtualization of hardware resources as a method of power savings in data center, in: MIPRO, 2010 Proceedings of the 33rd International Convention, IEEE, 2010, pp. 636-640.
[22] S. Irani, S. Shukla, R. Gupta, Online strategies for dynamic power management in systems with multiple power-saving states, ACM Transactions on Embedded Computing Systems (TECS), 2(3) (2003) 325-346.
[23] D.B. Rawat, C. Bajracharya, Software defined networking for reducing energy consumption and carbon emission, in: SoutheastCon, 2016, IEEE, 2016, pp. 1-2.
[24] P. Hande, M. Chiang, R. Calderbank, S. Rangan, Network pricing and rate allocation with content provider participation, in: INFOCOM 2009, IEEE, IEEE, 2009, pp. 990-998.
[25] C. Joe-Wong, S. Sen, S. Ha, M. Chiang, Optimized day-ahead pricing for smart grids with device-specific scheduling flexibility, IEEE Journal on Selected Areas in Communications, 30(6) (2012) 1075-1085.
[26] M. Dayarathna, Y. Wen, R. Fan, Data center energy consumption modeling: A survey, IEEE Communications Surveys & Tutorials, 18(1) (2016) 732-794.
[27] L. Tassiulas, A. Ephremides, Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks, IEEE transactions on automatic control, 37(12) (1992) 1936-1948.
[28] L. Tassiulas, A. Ephremides, Dynamic server allocation to parallel queues with randomly varying connectivity, IEEE Transactions on Information Theory, 39(2) (1993) 466-478.
[29] M.J. Neely, Stochastic network optimization with application to communication and queueing systems, Synthesis Lectures on Communication Networks, 3(1) (2010) 1-211.
[30] M.J. Neely, Energy optimal control for time-varying wireless networks, IEEE transactions on Information Theory, 52(7) (2006) 2915-2934.
[31] M.J. Neely, L. Huang, Dynamic product assembly and inventory control for maximum profit, in: Decision and Control (CDC), 2010 49th IEEE Conference on, IEEE, 2010, pp. 2805-2812.
[32] C.D. Patel, R.K. Sharma, C.E. Bash, M.H. Beitelmal, Energy flow in the information technology stack: Introducing the coefficient of performance of the ensemble, in: ASME 2006 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, 2006, pp. 233-241.
[33] Z. Liu, Y. Chen, C. Bash, A. Wierman, D. Gmach, Z. Wang, M. Marwah, C. Hyser, Renewable and cooling aware workload management for sustainable data centers, in: ACM SIGMETRICS Performance Evaluation Review, ACM, 2012, pp. 175-186.
[34] M. Lin, A. Wierman, L.L. Andrew, E. Thereska, Dynamic right-sizing for power-proportional data centers, IEEE/ACM Transactions on Networking, 21(5) (2013) 1378-1391.
[35] M.J. Neely, Distributed and secure computation of convex programs over a network of connected processors, in: DCDIS Conf., Guelph, Ontario, Citeseer, 2005, pp. 285-307.
[36] California ISO- Todays Outlook in, http://www.caiso. com/Pages/TodaysOutlook.aspx , 04.05.2017.