Bowen Zhou
ABSTRACT
Recent developments of the enhanced hardware on mobile devices such as quad-core CPUs and various sensors have made it possible to build a powerful heterogeneous mobile cloud offloading service that consists of a mobile ad-hoc cloud, nearby servers and public cloud services. However, the availability and mobility management of mobile devices in the network can significantly hinder the performance of mobile cloud systems due to the frequent system faults caused by dynamic changes, and prevent applications from offloading to mobile ad-hoc networks. In order to improve the mobile cloud service reliability, we propose a group based fault tolerant mechanism GFT-mCloud that classifies mobile devices into groups based on its processing capacity, mobility, and reliability. Different fault tolerance techniques are then devised adaptively based on the task offloading schedules and the specific group of machines it's offloaded. GFT-mCloud is designed as a standalone module that can work with existing mobile cloud code offloading systems. Extensive experiments have been conducted to evaluate the proposed mechanism. The results show that our fault tolerant mechanism is able to outperform conventional fault tolerant algorithms in the mobile cloud offloading environment.
- Sergio Barbarossa, Stefania Sardellitti, and Paolo Di Lorenzo. 2013. Joint allocation of computation and communication resources in multiuser mobile cloud computing. In Proceedings of IEEE 14th Workshop on Signal Processing Advances in Wireless Communications. IEEE, 26--30.Google Scholar
Cross Ref
- Chien-An Chen, Myounggyu Won, Radu Stoleru, and Geoffrey G. Xie. 2015. Energy-efficient fault-tolerant data storage and processing in mobile cloud. IEEE Transactions on Cloud Computing 3, 1 (Jan 2015), 28--41.Google ScholarDigital Library
- SungJin Choi, MaengSoon Baik, JoonMin Gil, SoonYoung Jung, and ChongSun Hwang. 2006. Adaptive group scheduling mechanism using mobile agents in peer-to-peer grid computing environment. Applied Intelligence 25, 2 (2006), 199--221. Google ScholarDigital Library
- Eduardo Cuervo, Aruna Balasubramanian, Dae-ki Cho, Alec Wolman, Stefan Saroiu, Ranveer Chandra, and Paramvir Bahl. 2010. MAUI:: Making smartphones last longer with code offload. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services. ACM, New York, NY, USA, 14. Google ScholarDigital Library
- Menno Dobber, Rob van der Mei, and Ger Koole. 2009. Dynamic load balancing and job replication in a global-scale grid environment: A comparison. IEEE Transactions on Parallel and Distributed Systems 20, 2 (Feb 2009), 207--218. Google ScholarDigital Library
- Richard O Duda, Peter E Hart, David G Stork, and others. 1973. Pattern classification. Vol. 2. Wiley New York.Google ScholarDigital Library
- Jr. James E. Kelley. 1961. Critical-path planning and scheduling: Mathematical basis. Operations Research 9, 3 (1961), 296--320. Google ScholarDigital Library
- Wolfgang Kiess and Martin Mauve. 2007. A survey on real-world implementations of mobile ad-hoc networks. Ad Hoc Networks 5, 3 (2007), 324 -- 339. Google ScholarDigital Library
- Sokol Kosta, Andrius Aucinas, Pan Hui, Richard Mortier, and Xinwen Zhang. 2012. TinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In Proceedings of the 31st IEEE International Conference on Computer Communications.Google Scholar
- David Kotz, Tristan Henderson, Ilya Abyzov, and Jihwang Yeo. 2009. CRAWDAD dataset dartmouth/campus (v. 2009--09-09). Downloaded from http://crawdad.org/dartmouth/campus/20090909/syslog. (Sept. 2009).Google Scholar
- Leslie Lamport, Robert Shostak, and Marshall Pease. 1982. The Byzantine Generals Problem. ACM Transactions on Programming Languages and Systems 4, 3 (July 1982), 382--401. Google ScholarDigital Library
- Antonios Litke, Dimitrios Skoutas, Konstantinos Tserpes, and Theodora Varvarigou. 2007. Efficient task replication and management for adaptive fault tolerance in Mobile Grid environments. Future Generation Computer Systems 23, 2 (2007), 163 -- 178. Google ScholarDigital Library
- James MacQueen and others. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 1. Oakland, CA, USA., 281--297.Google Scholar
- Alberto Medina, Anukool Lakhina, Ibrahim Matta, and John Byers. 2001. BRITE: An approach to universal topology generation. In Proceedings of the Ninth International Symposium in Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS '01). IEEE Computer Society, Washington, DC, USA, 346--. Google ScholarDigital Library
- Nichamon Naksinehaboon, Yudan Liu, Chokchai Leangsuksun, Raja Nassar, Mihaela Paun, and Stephen L. Scot. 2008. Reliability-aware approach: An incremental checkpoint/restart model in hpc environments. In Proceedings of 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid. 783--788. Google ScholarDigital Library
- JiSu Park, HeonChang Yu, Hyongsoon Kim, and Eunyoung Lee. 2016. Dynamic group-based fault tolerance technique for reliable resource management in mobile cloud computing. Concurrency and Computation: Practice and Experience 28, 10 (2016), 2756--2769. Google ScholarDigital Library
- J.R. Quinlan. 1986. Induction of Decision Trees. Machine Learning 1, 1 (1986), 81--106. Google ScholarCross Ref
- M Reza Rahimi, Nalini Venkatasubramanian, and Athanasios V Vasilakos. 2013. MuSIC: Mobility-aware optimal service allocation in mobile cloud computing. In Proceedings of the 6th IEEE International Conference on Cloud Computing. IEEE, 75--82. Google ScholarDigital Library
- Horst Rinne. 2008. The Weibull distribution: a handbook. CRC Press.Google Scholar
- Mahadev Satyanarayanan, P. Bahl, R Caceres, and N. Davies. 2009. The case for VM-based cloudlets in mobile computing. Pervasive Computing, IEEE 8, 4 (Oct 2009), 14--23. Google ScholarDigital Library
- Haluk Topcuoglu, Salim Hariri, and Min-You Wu. 2002. Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems 13, 3 (Mar 2002), 260--274. Google ScholarDigital Library
- Michael Treaster. 2005. A survey of fault-tolerance and fault-recovery techniques in parallel systems. arXiv.org abs/cs/0501002 (2005).Google Scholar
- Wikipedia. 2017. Instructions per Second. Technical Report.Google Scholar
- Ian H Witten, Eibe Frank, Mark A Hall, and Christopher J Pal. 2016. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann. Google ScholarDigital Library
- Liayo Xiang, Shiwen Ye, Yuan Feng, Baochun Li, and Bo Li. 2014. Ready, Set, Go: Coalesced offloading from mobile devices to the cloud. In Proceedings of 2014 IEEE Conference on Computer Communications. 2373--2381.Google ScholarCross Ref
- John W. Young. 1974. A first order approximation to the optimum checkpoint interval. ACM Communications 17, 9 (Sept. 1974), 530--531. Google ScholarDigital Library
- Bowen Zhou, Amir Vahid Dastjerdi, Rodrigo Calheiros, Satish Srirama, and Rajkumar Buyya. 2015. mCloud: A context-aware offloading framework for heterogeneous mobile cloud. IEEE Transactions on Services Computing (2015).Google Scholar
Index Terms
- A Group-based Fault Tolerant Mechanism for Heterogeneous Mobile Clouds
Recommendations
An Online Algorithm for Task Offloading in Heterogeneous Mobile Clouds
Special Issue on Internetware and Devops and Regular PapersMobile cloud computing is emerging as a promising approach to enrich user experiences at the mobile device end. Computation offloading in a heterogeneous mobile cloud environment has recently drawn increasing attention in research. The computation ...
An auction-based incentive mechanism for heterogeneous mobile clouds
Highlights- Heterogeneous mobile cloud forms a shared resource network for task offloading.
AbstractTraditional mobile cloud computing (MCC) adopts a onefold mobile device and public cloud paradigm. As the mobile device capabilities continue to develop, their opportunistic utilization in MCC has recently gained popularity. The new ...
Adaptive code offloading for mobile cloud applications: exploiting fuzzy sets and evidence-based learning
MCS '13: Proceeding of the fourth ACM workshop on Mobile cloud computing and servicesMobile cloud computing is arising as a prominent domain that is seeking to bring the massive advantages of the cloud to the resource constrained smartphones, by following a delegation or offloading criteria. In a delegation model, a mobile device ...
Comments