Skip to main content

Advertisement

Springer Nature Link
Log in

Adaptive fault-tolerant scheduling strategies for mobile cloud computing

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  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 161–164

  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):2899–2917

    Article  Google Scholar 

  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):436–454

    Google Scholar 

  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):28–41

    Article  Google Scholar 

  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):795–809

    Article  Google Scholar 

  6. Choi S, Chung K, Yu H (2014) Fault tolerance and QoS scheduling using CAN in mobile social cloud computing. Cluster Comput 17(3):911–926

    Article  Google Scholar 

  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 7

  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 122:138–152

    Article  Google Scholar 

  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 49–62

  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):3317–3329

    Article  Google Scholar 

  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):1587–1611

    Article  Google Scholar 

  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):275–294

    Google Scholar 

  13. Gelenbe E (1979) On the optimum checkpoint interval. J ACM (JACM) 26(2):259–270

    Article  MathSciNet  MATH  Google Scholar 

  14. George J, Chen CA, Stoleru R, Xie G (2016) Hadoop mapreduce for mobile clouds. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2016.2603474

  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 44–49

  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):1235–1243

    Article  Google Scholar 

  17. Henderson T, Kotz D (2007) Crawdad trace \(dartmouth/campus/syslog/05\_06\) (v. 2007-02-08)

  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 1315–1320

  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 159–168

  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):37–44

    Article  Google Scholar 

  21. Kim D (2017) Cloud computing to improve javascript processing efficiency of mobile applications. J Inf Process Syst 13(4):731–751

    Google Scholar 

  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):11

    Article  Google Scholar 

  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):847–857

    Article  MathSciNet  Google Scholar 

  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 485–492

    Chapter  Google Scholar 

  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):409–432

    Article  Google Scholar 

  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 394–403

  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 117–126

  28. Ling Y, Mi J, Lin X (2001) A variational calculus approach to optimal checkpoint placement. IEEE Trans Comput 50(7):699–708

    Article  Google Scholar 

  29. Mell PM, Grance T (2011) The NIST definition of cloud computing. NIST Special Publication, Report Number 800–145. https://doi.org/10.6028/NIST.SP.800-145

  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):20

    Article  Google Scholar 

  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):28

    Article  Google Scholar 

  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 67–72

  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 1821–1826

  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 33–42

  35. Rinne H (2008) The Weibull distribution: a handbook. Chapman and Hall/CRC, Boca Raton

    Book  MATH  Google Scholar 

  36. Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23

    Article  Google Scholar 

  37. Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5):637–646

    Article  Google Scholar 

  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):3256–3268

    Article  Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

  1. Department of Big Data Engineering, Daegu Catholic University, Gyeongsan, Republic of Korea

    JongHyuk Lee

  2. School of Information Technology Engineering, Daegu Catholic University, Gyeongsan, Republic of Korea

    JoonMin Gil

Authors
  1. JongHyuk Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. JoonMin Gil
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to JoonMin Gil.

Additional information

This paper is an extended version of conference papers that appeared as [24, 26].

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, J., Gil, J. Adaptive fault-tolerant scheduling strategies for mobile cloud computing. J Supercomput 75, 4472–4488 (2019). https://doi.org/10.1007/s11227-019-02745-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-02745-5

Keywords