, Volume 102, Issue 1, pp 105–139 | Cite as

Transient fault aware application partitioning computational offloading algorithm in microservices based mobile cloudlet networks

  • Abdullah LakhanEmail author
  • Xiaoping Li


Mobile Cloudlet Computing paradigm (MCC) allows execution of resource-intensive mobile applications using computation cloud resources by exploiting computational offloading method for resource-constrained mobile devices. Whereas, computational offloading needs the mobile application to be partitioned during the execution in the MCC so that total execution cost is minimized. In the MCC, at the run-time network contexts (i.e., network bandwidth, signal strength, latency, etc.) are intermittently changed, and transient failures (due to temporary network connection failure, services busy, database disk out of storage) often occur for a short period of time. Therefore, transient failure aware partitioning of the mobile application at run-time is a challenging task. Since, existing MCC offers computational monolithic services by exploiting heavyweight virtual machines, which incurs with long VM startup time and high overhead, and these cannot meet the requirements of fine-grained microservices applications (e.g., E-healthcare, E-business, 3D-Game, and Augmented Reality). To cope up with prior issues, we propose microservices based mobile cloud platform by exploiting containerization which replaces heavyweight virtual machines, and we propose the application partitioning task assignment (APTA) algorithm which determines application partitioning at run-time and adopts the fault aware (FA) policy to execute microservices applications robustly without interruption in the MCC. Simulation results validate that the proposed microservices mobile cloud platform not only shrinks the setup time of run-time platform but also reduce the energy consumption of nodes and improve the application response time by exploiting APTA and FA to the existing VM based MCC and application partitioning strategies.


Offloadi ng Mobile cloudlet computing Min-cut Microservices Application partitioning APTA FA Representational state transfer (REST) Application programming interface (API) 

Mathematics Subject Classification

68Wxx 68W15 11Y16 68R10 68W20 65Kxx 65Gxx 68Rxx 68Q25 



  1. 1.
    Cao H, Cai J (2018) Distributed multiuser computation offloading for cloudlet-based mobile cloud computing: a game-theoretic machine learning approach. IEEE Trans Veh Technol 67(1):752–764MathSciNetCrossRefGoogle Scholar
  2. 2.
    Akherfi K, Gerndt M, Harroud H (2018) Mobile cloud computing for computation offloading: issues and challenges. Appl Comput Inform 14(1):1–16CrossRefGoogle Scholar
  3. 3.
    Czaja L (2018) Remote procedure call. In: Czaja L (ed) Introduction to distributed computer systems. Springer, Berlin, pp 141–155CrossRefGoogle Scholar
  4. 4.
    Wu H, Knottenbelt W, Wolter K, Sun Y (2016) An optimal offloading partitioning algorithm in mobile cloud computing. In: International conference on quantitative evaluation of systems. Springer, pp 311–328Google Scholar
  5. 5.
    Yang L, Cao J, Tang S, Han D, Suri N (2016) Run time application repartitioning in dynamic mobile cloud environments. IEEE Trans Cloud Comput 4(3):336–348CrossRefGoogle Scholar
  6. 6.
    Kosta S, Aucinas A, Hui P, Mortier R, Zhang X (2012) Thinkair: dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In: Infocom, 2012 proceedings IEEE. IEEE, pp 945–953Google Scholar
  7. 7.
    Kemp R, Palmer N, Kielmann T, Bal H (2010) Cuckoo: a computation offloading framework for smartphones. In: International conference on mobile computing, applications, and services. Springer, pp 59–79Google Scholar
  8. 8.
    Qian H, Andresen D (2014) Jade: an efficient energy-aware computation offloading system with heterogeneous network interface bonding for ad-hoc networked mobile devices. In: 2014 15th IEEE/ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD). IEEE, pp 1–8Google Scholar
  9. 9.
    Osman S, Subhraveti D, Su G, Nieh J (2002) The design and implementation of zap: a system for migrating computing environments. ACM SIGOPS Oper Syst Rev 36(SI):361–376CrossRefGoogle Scholar
  10. 10.
    Adufu T, Choi J, Kim Y (2015) Is container-based technology a winner for high performance scientific applications? In: Network operations and management symposium (APNOMS), 2015 17th Asia-Pacific. IEEE, pp 507–510Google Scholar
  11. 11.
    Wu S, Niu C, Rao J, Jin H, Dai X (2017) Container-based cloud platform for mobile computation offloading. In: Parallel and distributed processing symposium (IPDPS), 2017 IEEE international. IEEE, pp 123–132Google Scholar
  12. 12.
    Bonacorsi D, Eulisse G, Boccali T, Mazzoni E (2016) Containerization of CMS applications with docker. In: PoS, p 007Google Scholar
  13. 13.
    Yang L, Cao J, Yuan Y, Li T, Han A, Chan A (2013) A framework for partitioning and execution of data stream applications in mobile cloud computing. ACM SIGMETRICS Perform Eval Rev 40(4):23–32CrossRefGoogle Scholar
  14. 14.
    Chun B-G, Ihm S, Maniatis P, Naik M, Patti A (2011) Clonecloud: elastic execution between mobile device and cloud. In: Proceedings of the sixth conference on Computer systems. ACM, pp 301–314Google Scholar
  15. 15.
    Cuervo E, Balasubramanian A, Cho D, 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
  16. 16.
    Newton R, Toledo S, Girod L, Balakrishnan H, Madden S (2009) Wishbone: profile-based partitioning for sensornet applications. NSDI 9:395–408Google Scholar
  17. 17.
    Smit M, Shtern M, Simmons B, Litoiu M (2012) Partitioning applications for hybrid and federated clouds. In Proceedings of the 2012 conference of the center for advanced studies on collaborative research. IBM Corp, pp 27–41Google Scholar
  18. 18.
    Abebe E, Ryan C (2011) A hybrid granularity graph for improving adaptive application partitioning efficacy in mobile computing environments. In: 2011 10th IEEE international symposium on network computing and applications (NCA). IEEE, pp 59–66Google Scholar
  19. 19.
    Verbelen T, Stevens T, De Turck F, Dhoedt B (2013) Graph partitioning algorithms for optimizing software deployment in mobile cloud computing. Future Gener Comput Syst 29(2):451–459CrossRefGoogle Scholar
  20. 20.
    Wang C, Li Z (2004) Parametric analysis for adaptive computation offloading. In: ACM SIGPLAN notices, vol 39. ACM, pp 119–130Google Scholar
  21. 21.
    Kumar K, Yung-Hsiang L (2010) Cloud computing for mobile users: can offloading computation save energy? Computer 43(4):51–56CrossRefGoogle Scholar
  22. 22.
    Chen X (2015) Decentralized computation offloading game for mobile cloud computing. IEEE Trans Parallel Distrib Syst 26(4):974–983CrossRefGoogle Scholar
  23. 23.
    Chen X, Jiao L, Li W, Xiaoming F (2016) Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans Netw 5:2795–2808CrossRefGoogle Scholar
  24. 24.
    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):3317–3329CrossRefGoogle Scholar
  25. 25.
    Xia F, Ding F, Li J, Kong X, Yang LT, Ma J (2014) Phone2cloud: exploiting computation offloading for energy saving on smartphones in mobile cloud computing. Inf Syst Front 16(1):95–111CrossRefGoogle Scholar
  26. 26.
    Cardellini V, De Nitto Personé V, Di Valerio V, Facchinei F, Grassi V, Presti FL, Piccialli V (2016) A game-theoretic approach to computation offloading in mobile cloud computing. Math Program 157(2):421–449MathSciNetzbMATHCrossRefGoogle Scholar
  27. 27.
    Shi C, Habak K, Pandurangan P, Ammar M, Naik M, Zegura E (2014) Cosmos: computation offloading as a service for mobile devices. In: Proceedings of the 15th ACM international symposium on Mobile ad hoc networking and computing. ACM, pp 287–296Google Scholar
  28. 28.
    Shiraz M, Gani A (2014) A lightweight active service migration framework for computational offloading in mobile cloud computing. J Supercomput 68(2):978–995CrossRefGoogle Scholar
  29. 29.
    Shiraz M, Gani A, Shamim A, Khan S, Ahmad RW (2015) Energy efficient computational offloading framework for mobile cloud computing. J Grid Comput 13(1):1–18CrossRefGoogle Scholar
  30. 30.
    Park JS, Yu HC, Chung KS, Lee EY (2011) Markov chain based monitoring service for fault tolerance in mobile cloud computing. In: 2011 IEEE workshops of international conference on advanced information networking and applications (WAINA). IEEE, pp 520–525Google Scholar
  31. 31.
    Chen C-A, Won M, Stoleru R, Xie GG (2015) Energy-efficient fault-tolerant data storage and processing in mobile cloud. IEEE Trans Cloud Comput 3(1):28–41CrossRefGoogle Scholar
  32. 32.
    Choi SK, Chung KS, Heonchang Y (2014) Fault tolerance and QoS scheduling using can in mobile social cloud computing. Cluster Comput 17(3):911–926CrossRefGoogle Scholar
  33. 33.
    Wang K, Shao Y, Shu L, Zhu C, Zhang Y (2016) Mobile big data fault-tolerant processing for ehealth networks. IEEE Netw 30(1):36–42CrossRefGoogle Scholar
  34. 34.
    Kwon Y-W, Tilevich E (2012) Energy-efficient and fault-tolerant distributed mobile execution. In: 2012 IEEE 32nd international conference on distributed computing systems (ICDCS). IEEE, pp 586–595Google Scholar
  35. 35.
    Park JS, HeonChang Y, Kim H, Lee E (2016) Dynamic group-based fault tolerance technique for reliable resource management in mobile cloud computing. Concurr Comput Pract Exp 28(10):2756–2769CrossRefGoogle Scholar
  36. 36.
    Abd SK, Al-Haddad SAR, Hashim F, Abdullah ABHJ, 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
  37. 37.
    Chen C-A, Won M, Stoleru R, Xie GG (2013) Energy-efficient fault-tolerant data storage and processing in dynamic networks. In: Proceedings of the fourteenth ACM international symposium on mobile ad hoc networking and computing. ACM, pp 281–286Google Scholar
  38. 38.
    Park J, Yu H-C, Lee E-Y (2013) Fault tolerance technique based on monitoring and pattern for reliable resource management in mobile cloud computing. J Internet Technol 14(6):997–1005Google Scholar
  39. 39.
    Deng J, Huang SC-H, Han YS, Deng JH (2010) Fault-tolerant and reliable computation in cloud computing. In: GLOBECOM workshops (GC Wkshps), 2010 IEEE. IEEE, pp 1601–1605Google Scholar
  40. 40.
    Bernstein D (2014) Containers and cloud: from lxc to docker to kubernetes. IEEE Cloud Comput 1(3):81–84CrossRefGoogle Scholar
  41. 41.
    Java client library for Azure Event Hubs. Accessed 31 Dec 2018
  42. 42.
    Madsack A, Heininger J, Davaasambuu N, Voronik V, Käufl M, Weißgraeber R (2018) Multi-language surface realisation as rest API based NLG microservice. In: INLG, pp 480–481Google Scholar
  43. 43.
    Stoer M, Wagner F (2017) A simple min-cut algorithm. J ACM 44(4):585–591MathSciNetzbMATHCrossRefGoogle Scholar
  44. 44.
    Ahmed E, Gani A, Khan MK, Buyya R, Khan SU (2015) Seamless application execution in mobile cloud computing. J Netw Comput Appl 52:154–172CrossRefGoogle Scholar
  45. 45.
    Li X, Zhao J, Ma Y, Wang P, Sun H, Tang Y (2017) A partition model and strategy based on the Stoer–Wagner algorithm for SAAS multi-tenant data. Soft Comput 21(20):6121–6132CrossRefGoogle Scholar
  46. 46.
    Stoer M, Wagner F (1997) A simple min-cut algorithm weighted consumption graph. JACM 44(4):585–591zbMATHCrossRefGoogle Scholar
  47. 47.
    Wagner F, Klimmek R (2016) Simple Hypergraph Min Cut Algorithm for call graph. Institute of Computer Science, Freie University, BerlinGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, and Ministry of EducationSoutheast UniversityNanjingChina

Personalised recommendations