Adaptive fault-tolerant scheduling strategies for mobile cloud computing

  • JongHyuk Lee
  • JoonMin GilEmail author


Mobile cloud computing is a form of cloud computing that incorporates mobile devices such as smartphones and tablet PCs into the cloud infrastructure. As mobile devices are resource-constrained in nature, new scheduling strategies are required when using them as resource providers. Based on our previous group-based scheduling algorithm, we present fault-tolerant scheduling algorithms considering checkpoint and replication mechanisms to actively cope with faults. We carried out the performance evaluation with simulation to demonstrate that our algorithm is more efficient than the existing one lacking fault tolerance in terms of accuracy rate, resource consumption, and average execution time. In particular, the average execution time was reduced by about 60%, resulting in the reduction of resource consumption.


Adaptive scheduling Fault tolerance Replication Checkpoint Mobile cloud computing 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2055463).


  1. 1.
    Abd SK, Al-Haddad S, Hashim F, Abdullah AB, Yussof S (2017) Energy-aware fault tolerant task offloading of mobile cloud computing. In: 2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud). IEEE, pp 161–164Google Scholar
  2. 2.
    Casanova H, Giersch A, Legrand A, Quinson M, Suter F (2014) Versatile, scalable, and accurate simulation of distributed applications and platforms. J Parallel Distrib Comput 74(10):2899–2917Google Scholar
  3. 3.
    Chantrapornchai C, Nusawat P (2016) Two machine learning models for mobile phone battery discharge rate prediction based on usage patterns. J Inf Process Syst 12(3):436–454Google Scholar
  4. 4.
    Chen CA, Won M, Stoleru R, Xie GG (2015) Energy-efficient fault-tolerant data storage and processing in mobile cloud. IEEE Trans Cloud Comput 3(1):28–41Google Scholar
  5. 5.
    Chen G, Kang BT, Kandemir M, Vijaykrishnan N, Irwin MJ, Chandramouli R (2004) Studying energy trade offs in offloading computation/compilation in java-enabled mobile devices. IEEE Trans Parallel Distrib Syst 15(9):795–809Google Scholar
  6. 6.
    Choi S, Chung K, Yu H (2014) Fault tolerance and QoS scheduling using CAN in mobile social cloud computing. Cluster Comput 17(3):911–926Google Scholar
  7. 7.
    Chun BG, Maniatis P (2010) Dynamically partitioning applications between weak devices and clouds. In: Proceedings of the 1st ACM Workshop on Mobile Cloud Computing & Services: Social Networks and Beyond. ACM, p 7Google Scholar
  8. 8.
    Chunlin L, Xin Y, Yang Z, Youlong L (2017) Multiple context based service scheduling for balancing cost and benefits of mobile users and cloud datacenter supplier in mobile cloud. Comput Netw 122:138–152Google Scholar
  9. 9.
    Cuervo E, Balasubramanian A, Cho Dk, Wolman A, Saroiu S, Chandra R, Bahl P (2010) MAUI: making smartphones last longer with code offload. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services. ACM, pp 49–62Google Scholar
  10. 10.
    Deng S, Huang L, Taheri J, Zomaya AY (2015) Computation offloading for service workflow in mobile cloud computing. IEEE Trans Parallel Distrib Syst 26(12):3317–3329Google Scholar
  11. 11.
    Dinh HT, Lee C, Niyato D, Wang P (2013) A survey of mobile cloud computing: architecture, applications, and approaches. Wirel Commun Mobile Comput 13(18):1587–1611Google Scholar
  12. 12.
    Fayçal-Khelfi M et al (2016) Using mobile data collectors to enhance energy efficiency and reliability in delay tolerant wireless sensor networks. J Inf Process Syst 12(2):275–294Google Scholar
  13. 13.
    Gelenbe E (1979) On the optimum checkpoint interval. J ACM (JACM) 26(2):259–270MathSciNetzbMATHGoogle Scholar
  14. 14.
    George J, Chen CA, Stoleru R, Xie G (2016) Hadoop mapreduce for mobile clouds. IEEE Trans Cloud Comput.
  15. 15.
    Goyal M, Saini P (2016) A fault-tolerant energy-efficient computational offloading approach with minimal energy and response time in mobile cloud computing. In: 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, pp 44–49Google Scholar
  16. 16.
    Hao F, Pei Z, Park DS, Phonexay V, Seo HS (2018) Mobile cloud services recommendation: a soft set-based approach. J Ambient Intell Humaniz Comput 9(4):1235–1243Google Scholar
  17. 17.
    Henderson T, Kotz D (2007) Crawdad trace \(dartmouth/campus/syslog/05\_06\) (v. 2007-02-08)Google Scholar
  18. 18.
    Huchton S, Xie G, Beverly R (2011) Building and evaluating a k-resilient mobile distributed file system resistant to device compromise. In: Military Communications Conference, 2011-MILCOM 2011. IEEE, pp 1315–1320Google Scholar
  19. 19.
    Jackson KR, Ramakrishnan L, Muriki K, Canon S, Cholia S, Shalf J, Wasserman HJ, Wright NJ (2010) Performance analysis of high performance computing applications on the amazon web services cloud. In: 2010 IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, pp 159–168Google Scholar
  20. 20.
    Jeong H, Kim DH, Baddar WJ, Ro YM (2017) Gender classification system based on deep learning in low power embedded board. KIPS Trans Softw Data Eng 6(1):37–44Google Scholar
  21. 21.
    Kim D (2017) Cloud computing to improve javascript processing efficiency of mobile applications. J Inf Process Syst 13(4):731–751Google Scholar
  22. 22.
    Kim HW, Jeong YS (2018) Secure authentication-management human-centric scheme for trusting personal resource information on mobile cloud computing with blockchain. Hum Centric Comput Inf Sci 8(1):11Google Scholar
  23. 23.
    Lee J, Choi S, Gil J, Suh T, Yu H (2014) A scheduling algorithm with dynamic properties in mobile grid. Front Comput Sci 8(5):847–857MathSciNetGoogle Scholar
  24. 24.
    Lee J, Choi S, Suh T, Gil J, Shi W, Yu H (2012) A mobile device group based fault tolerance scheduling algorithm in mobile grid. In: Park J, Jeong YS, Park S, Chen HC (eds) Embedded and multimedia computing technology and service. Springer, Dordrecht, pp 485–492Google Scholar
  25. 25.
    Lee J, Choi S, Suh T, Yu H (2014) Mobility-aware balanced scheduling algorithm in mobile grid based on mobile agent. Knowl Eng Rev 29(4):409–432Google Scholar
  26. 26.
    Lee J, Choi S, Suh T, Yu H, Gil J (2010) Group-based scheduling algorithm for fault tolerance in mobile grid. In: Security-Enriched Urban Computing and Smart Grid, pp 394–403Google Scholar
  27. 27.
    Lee JH, Choi S, Lim J, Suh T, Gil JM, Yu HC (2010) Mobile grid system based on mobile agent. In: FGIT-GDC/CA. Springer, pp 117–126Google Scholar
  28. 28.
    Ling Y, Mi J, Lin X (2001) A variational calculus approach to optimal checkpoint placement. IEEE Trans Comput 50(7):699–708Google Scholar
  29. 29.
    Mell PM, Grance T (2011) The NIST definition of cloud computing. NIST Special Publication, Report Number 800–145.
  30. 30.
    Mesbahi MR, Rahmani AM, Hosseinzadeh M (2018) Reliability and high availability in cloud computing environments: a reference roadmap. Hum Centric Comput Inf Sci 8(1):20Google Scholar
  31. 31.
    Moon Y, Yu H, Gil JM, Lim J (2017) A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments. Hum Centric Comput Inf Sci 7(1):28Google Scholar
  32. 32.
    O'Sullivan MJ, Grigoras D (2016) Context aware mobile cloud services: a user experience oriented middleware for mobile cloud computing. In: 2016 4th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud). IEEE, pp 67–72Google Scholar
  33. 33.
    Ou S, Yang K, Liotta A, Hu L (2007) Performance analysis of offloading systems in mobile wireless environments. In: IEEE International Conference on Communications, 2007. ICC’07. IEEE, pp 1821–1826Google Scholar
  34. 34.
    Ren X, Eigenmann R, Bagchi S (2007) Failure-aware checkpointing in fine-grained cycle sharing systems. In: Proceedings of the 16th International Symposium on High Performance Distributed Computing. ACM, pp 33–42Google Scholar
  35. 35.
    Rinne H (2008) The Weibull distribution: a handbook. Chapman and Hall/CRC, Boca RatonzbMATHGoogle Scholar
  36. 36.
    Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23Google Scholar
  37. 37.
    Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5):637–646Google Scholar
  38. 38.
    Zhou Z, Zhang H, Ye L, Du X (2016) Cuckoo: flexible compute-intensive task offloading in mobile cloud computing. Wirel Commun Mobile Comput 16(18):3256–3268Google Scholar

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Authors and Affiliations

  1. 1.Department of Big Data EngineeringDaegu Catholic UniversityGyeongsanRepublic of Korea
  2. 2.School of Information Technology EngineeringDaegu Catholic UniversityGyeongsanRepublic of Korea

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