Reliable resource allocation and fault tolerance in mobile cloud computing
الموضوعات :Zahra Najafabadi Samani 1 , Mohammad Reza Khayyam Bashi 2
1 - University of Isfahan
2 - University of Isfahan
الکلمات المفتاحية: Mobile Cloud Computing, , Fault tolerance, , Reliability, , Replication, , Checkpointing, ,
ملخص المقالة :
By switching the computational load from mobile devices to the cloud, Mobile Cloud Computing (MCC) allows mobile devices to offer a wider range of functionalities. There are several issues in using mobile devices as resource providers, including unstable wireless connections, limited energy capacity, and frequent location changes. Fault tolerance and reliable resource allocation are among the challenges encountered by mobile service providers in MCC. In this paper, a new reliable resource allocation and fault tolerance mechanism is proposed in order to apply a fully distributed resource allocation algorithm without exploiting any central component. The objective is to improve the reliability of mobile resources. The proposed approach involves two steps: (1) Predicting device status by gathering contextual information and applying TOPSIS to prevent faults caused by volatility of mobile devices, and (2) Adapting replication and checkpointing methods to fault tolerance. A context-aware reliable offloading middleware is developed to collect contextual information and manage the offloading process. To evaluate the proposed method, several experiments are run in a real environment. The results indicate improvements in success rates, completion time, and energy consumption for tasks with high computational load
[1] N. Fernando, S. W. Loke, and W. Rahayu, “Mobile cloud computing: A survey,” Future Generation Computer Systems, vol. 29, no. 1, pp. 84-106, 2013.
[2] M.Othman, S. A. Madani, and S. U. Khan, “A survey of mobile cloud computing application models,” IEEE Communications Surveys & Tutorials, vol. 16, no. 11, pp. 393-413, 2014.
[3] G. F. Huerta Cánepa, “A context-aware application offloading scheme for a mobile peer to peer environment,” Ph.D. dissertation, Department of Information and Communication Engineering, KAIST, South Korea, 2012.
[4] M. Conti, et al. “Looking ahead in pervasive computing: Challenges and opportunities in the era of cyber–physical convergence,” Pervasive and Mobile Computing, vol. 8, no. 1, pp. 2-21, 2012.
[5] V. Cardellini, V. De NitoPersoné, V. Di Valerio, F. Facchinei, V. Grassi, F. Lo Presti and V. Piccialli, “A game-theoretic approach to computation offloading in mobile cloud computing,” Technical Report, 2013.
[6] S. G. Falavarjani, M. Nematbakhsh, and B. S. Ghahfarokhi, "Context-aware multi-objective resource allocation in mobile cloud," Computers & Electrical Engineering,¬vol. 44, pp. 218-240, 2015.
[7] C.Shi, et al. “Serendipity: enabling remote computing among intermittently connected mobile devices,” In Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing. ACM, 2012, pp. 145-154.
[8] B. Zhou, A. V. Dastjerdi, R. N. Calheiros, S. N. Srirama, and R. Buyya, “A context sensitive offloading scheme for mobile cloud computing service,” In Proceedings of IEEE of 8th International Conference on In Cloud Computing (CLOUD), 2015, pp.869-876.
[9] D. N. Raju, and V. Saritha. "Architecture for fault tolerance in mobile cloud computing using disease resistance approach." International Journal of Communication Networks and Information Security, vol. 8, no. 2, 2016.
[10] B. Zhou and R. Buyya, "A Group based Fault Tolerant Mechanism for Heterogeneous Mobile Clouds," In Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Nov 2017.
[11] J. Park, H. Yu, H. Kim, and E. Lee, “Dynamic group‐based fault tolerance technique for reliable resource management in mobile cloud computing,” Concurrency and Computation: Practice and Experience, John Wiley & Sons, vol. 26, no. 17, Jan. 2014.
[12] J. Park, H. Yu, E. Lee, “Resource allocation techniques based on availability and movement reliability for mobile cloud computing,” Distributed Computing and Internet Technology, Springer Berlin Heidelberg, 2012,pp. 263–264.
[13] J. S. Park and E. Y. Lee, “Entropy-based grouping techniques for resource management in mobile cloud computing,” Ubiquitous Information Technologies and Applications, Springer Netherlands, 2013, pp. 773-780.
[14] P. Stahl, et al, "Dynamic Fault-Tolerance and Mobility Provisioning for Services on Mobile Cloud Platforms." In Proceedings of the 2017 5th International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), IEEE, 2017, pp. 131-138.
[15] S. Choi, K. Chung, and H. Yu, “Fault tolerance and QoS scheduling using CAN in mobile social cloud computing”, Cluster Computing, vol.17, no.3, pp. 911-926, 2014.
[16] E. E. Marinelli, Hyrax: cloud computing on mobile devices using MapReduce. No. CMU-CS-09-164. Carnegie-mellon univ Pittsburgh PA school of computer science, 2009.
[17] C-A. Chen, et al., “Energy-efficient fault-tolerant data storage and processing in mobile cloud,” IEEE Transactions on cloud computing, vol. 3, no. 1, pp. 28-41, 2015.
[18] J. Park, H. Yu, K. Chung, and E. Lee, “Markov Chain based Monitoring Service for Fault Tolerance in Mobile Cloud Computing,” In Proceedings of IEEE Workshops of International Conference on Advanced Information Networking and Applications, 2011, pp.520-525.
[19] P. Patel and V. Prakash, “FTAB: Fault Tolerance Approach by Using HMM with BAUM-WELCH Algorithm in MCC,” In Proceedings of Tenth international conference on Wireless and Optical Communication Network (WOCN), 2013, pp.1-4.
[20] L. Ling, W. Zhulin and Y. Xiuhua, “Mobile Resource Reliability-Based Task Allocation for Mobile Cloud”, In Proceedings of the 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), IEEE, 2015, pp.1746-1750.
[21] J.Park, et al, “Two‐phase grouping‐based resource management for big data processing in mobile cloud computing,” International Journal of Communication Systems, vol. 27, no. 6, pp. 839-851, 2014.
[22] L. McNamara, C. Mascolo, L. Capra, “Media sharing based on colocation prediction in urban transport,” In Proceedings of the 14th ACM international conference on mobile computing and networking, ACM, 2008. pp. 58–69.
[23] P. A, Lee, and T. Anderson, “Dependable computing and fault tolerant systems,” Fault Tolerance: Principles and Practice, Springer Verlag, NewYork, vol. 3, 1990.
[24] Ch. Song, et al. “Limits of predictability in human mobility,” Science 327.5968 , 2010, pp.1018-1021.
[25] J. Nicholson and B. D. Noble. “Breadcrumbs: forecasting mobile connectivity,” In Proceedings of the 14th ACM international conference on Mobile computing and networking. ACM, 2008, pp. 46-57.
[26] Huang, Wei, et al. “Predicting human mobility with activity changes,” International Journal of Geographical Information Science, vol. 29, no. 9, pp. 1569-1587, 2015.
[27] S. Ch. Shah, “Energy efficient and robust allocation of interdependent tasks on mobile ad hoc computational grid,” Concurrency and Computation: Practice and Experience, vol. 27, no. 5, pp. 1226-1254, 2015.
[28] M.Sepahkar and M. R. Khayyambashi, “A novel collaborative approach for location prediction in mobile networks,” Wireless Networks, pp. 1-12, DOI 10.1007/s11276-016-1304-1, 2016.
[29] J. A. Gubner, Probability and random processes for electrical and computer engineers, Cambridge University Press, 2006.
[30] M. Wiesmann, et al. “Understanding replication in databases and distributed systems”, In Proceedings of the 20th International Conference on IEEE, Distributed Computing Systems, 2000, pp. 464-474.
[31] R. Tuli and P. Kumar, “Analysis of recent checkpointing techniques for mobile computing systems,” International Journal of Computer Science & Engineering Survey, vol. 2, no. 3, 2011.
[32] E. Cuervo, et al. “MAUI: making smartphones last longer with code offload,” In: Proceedings of the 8th international conference on mobile systems, applications, and services, MobiSys’10, 2010, pp. 49–62.
[33] M.D. Kristensen, “Scavenger: transparent development of efficient cyber foraging applications,” In: Proceedings of the IEEE international conference on pervasive computing and communications (PerCom); 2010. p. 217–26.
[34] C. L. Hwang, and K. Yoon. Multiple attribute decision making: methods and applications a state-of-the-art survey, Springer Science & Business Media, Vol. 186, 2012.
[35] C. E. Shannon, “A mathematical theory of communication,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 5, no. 1, pp. 3-55, 2001.
[36] FaceDetector, http://developer.android.com/reference/android/media/FaceDetector.html Avalaible Online @ September 2016.
[37] L. Zhang, et al. “Accurate online power estimation and automatic battery behavior based power model generation for smartphones,” In Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis. ACM, 2010, pp. 105.