Personal and Ubiquitous Computing

, Volume 20, Issue 3, pp 295–309 | Cite as

A survey on decision making for task migration in mobile cloud environments

  • Weishan Zhang
  • Shouchao Tan
  • Feng Xia
  • Xiufeng Chen
  • Zhongwei Li
  • Qinghua Lu
  • Su Yang
Original Article

Abstract

The key idea of MCC is using powerful back-end computing nodes to enhance capabilities of small mobile devices and provide better user experiences. An effective and widely used approach to realize this is task migrations. Decision making is an important aspect of migrations which affects the feasibility and effectiveness of task migrations. There have been a number of research efforts to MCC which help make decisions for task migrations. In this paper, we present a comprehensive survey on decision making for task migrations in MCC, including decision factors and algorithms. We observe that there are still some challenges such as comprehensive context awareness, unified migration standards, large-scale experiments, more involvement of latest achievements from artificial intelligence, and flexible decision-making mechanisms. The paper highlights these issues and challenges to attract more efforts to work on MCC.

Keywords

Cloud computing Task migration Decision making Mobile cloud Context awareness 

References

  1. 1.
    Hung PP, Bui TA, Morales MAG et al (2014) Optimal collaboration of thinthick clients and resource allocation in cloud computing. Pers Ubiquitous Comput 18(3):563–572CrossRefGoogle Scholar
  2. 2.
    Kristensen MD (2010) Empowering mobile devices through cyber foraging. Ph. D. Dissertation, Aarhus UniversityGoogle Scholar
  3. 3.
    Dinh HT, Lee C, Niyato D et al (2013) A survey of mobile cloud computing: architecture, applications, and approaches. Wirel Commun Mobile Comput 13(18):1587–1611CrossRefGoogle Scholar
  4. 4.
    Dou A, Kalogeraki V, Gunopulos D et al (2010) Misco: a mapreduce framework for mobile systems. In: Proceedings of the 3rd international conference on pervasive technologies related to assistive environments. ACM, p 32Google Scholar
  5. 5.
    Chun BG, Ihm S, Maniatis P et al (2011) Clonecloud: elastic execution between mobile device and cloud. In: Proceedings of the sixth conference on computer systems. ACM, pp 301–314Google Scholar
  6. 6.
    Marinelli EE (2009) Hyrax: cloud computing on mobile devices using MapReduce. Carnegie-Mellon University, Pittsburgh school of computer scienceGoogle Scholar
  7. 7.
    March V, Gu Y, Leonardi E et al (2011) Cloud: towards a new paradigm of rich mobile applications. Proc Comput Sci 5:618–624CrossRefGoogle Scholar
  8. 8.
    Lu Y, Li S, Shen H (2011) Virtualized screen: a third element for cloud–mobile convergence. MultiMed IEEE 18(2):4–11CrossRefGoogle Scholar
  9. 9.
    Lomotey RK, Deters R (2014) Using a cloud-centric middleware to enable mobile hosting of Web services: mHealth use case. Pers Ubiquitous Comput 18(5):1085–1098CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Kakadia D, Saripalli P, Varma V (2013) MECCA: mobile, efficient cloud computing workload adoption framework using scheduler customization and workload migration decisions. In: Proceedings of the first international workshop on Mobile cloud computing & networking. ACM, pp 41–46Google Scholar
  12. 12.
    Gu X, Nahrstedt K, Messer A et al (2004) Adaptive offloading for pervasive computing. Pervasive Comput IEEE 3(3):66–73CrossRefGoogle Scholar
  13. 13.
    Abolfazli S, Sanaei Z, Ahmed E et al (2014) Cloud-based augmentation for mobile devices: motivation, taxonomies, and open challenges. Commun Surv Tutor IEEE 16(1):337–368CrossRefGoogle Scholar
  14. 14.
  15. 15.
    AEPONA (2010) Mobile cloud computing solution brief. White PaperGoogle Scholar
  16. 16.
    Liu L, Moulic R, Shea D (2010) Cloud service portal for mobile device management. In: 2010 IEEE 7th international conference on e-Business engineering (ICEBE). IEEE, pp 474–478Google Scholar
  17. 17.
    Chun BG, Maniatis P (2009) Augmented smartphone applications through clone cloud execution. HotOS 9:8–11Google Scholar
  18. 18.
    Kallonen T, Porras J (2006) Use of distributed resources in mobile environment. In: International conference on software in telecommunications and computer networks, 2006. SoftCOM 2006. IEEE, pp 281–285Google Scholar
  19. 19.
    Ververidis CN, Polyzos GC (2008) Service discovery for mobile ad hoc networks: a survey of issues and techniques. Commun Surv Tutor IEEE 10(3):30–45CrossRefGoogle Scholar
  20. 20.
    Preuveneers D, Berbers Y (2005) Adaptive context management using a component-based approach. In: Distributed applications and interoperable systems. Springer, BerlinGoogle Scholar
  21. 21.
    Miraoui M, Tadj C, Fattahi J et al (2011) Dynamic context-aware and limited resources-aware service adaptation for pervasive computing. Adv Softw Eng 2011:7CrossRefGoogle Scholar
  22. 22.
    Makris P, Skoutas DN, Skianis C (2013) A survey on context-aware mobile and wireless networking: on networking and computing environments’ integration. Commun Surv Tutor IEEE 15(1):362–386CrossRefGoogle Scholar
  23. 23.
    Yuan B, Herbert J (2014) Context-aware hybrid reasoning framework for pervasive healthcare. Pers Ubiquitous Comput 18(4):865–881CrossRefGoogle Scholar
  24. 24.
    Kumar K, Liu J, Lu YH et al (2013) A survey of computation offloading for mobile systems. Mobile Netw Appl 18(1):129–140CrossRefGoogle Scholar
  25. 25.
    Balan RK, Satyanarayanan M, Park SY et al (2003) Tactics-based remote execution for mobile computing. In: Proceedings of the 1st international conference on Mobile systems, applications and services. ACM, pp 273–286Google Scholar
  26. 26.
    Frey S, Hasselbring W (2011) The cloudmig approach: model-based migration of software systems to cloud-optimized applications. Int J Adv Softw 4(3 and 4):342–353Google Scholar
  27. 27.
    Cuervo E, Balasubramanian A, Cho D et al (2010) MAUI: making smart phones last longer with code offload. In: Proceedings of the 8th international conference on Mobile systems, applications, and services. ACM, pp 49–62Google Scholar
  28. 28.
    Satyanarayanan M, Bahl P, Caceres R et al (2009) The case for vm-based cloudlets in mobile computing. Pervasive Comput IEEE 8(4):14–23CrossRefGoogle Scholar
  29. 29.
    Soyata T, Muraleedharan R, Funai C et al (2012) Cloud-vision: real-time face recognition using a mobile-cloudlet-cloud acceleration architecture. In: 2012 IEEE symposium on computers and communications (ISCC). IEEE, pp 59–66Google Scholar
  30. 30.
  31. 31.
  32. 32.
    Sanaei Z, Abolfazli S, Gani A et al (2012) SAMI: Service-based arbitrated multi-tier infrastructure for Mobile Cloud Computing. In: 2012 1st IEEE international conference on communications in china workshops (ICCC). IEEE, pp 14–19Google Scholar
  33. 33.
    Zhao B, Xu Z, Chi C et al (2010) Mirroring smart phones for good: a feasibility study. In: Mobile and ubiquitous systems: computing, networking, and services. Springer, Heidelberg, pp 26–38Google Scholar
  34. 34.
    Black M, Edgar W (2009) Exploring mobile devices as Grid resources: using an x86 virtual machine to run BOINC on an iPhone. In: 2009 10th IEEE/ACM international conference on grid computing. IEEE, pp 9–16Google Scholar
  35. 35.
    Huerta-Canepa G, Lee D (2010) A virtual cloud computing provider for mobile devices. In: Proceedings of the 1st ACM workshop on mobile cloud computing & services: social networks and beyond. ACM, p 6Google Scholar
  36. 36.
    Newton R, Toledo S, Girod L et al (2009) Wishbone: profile-based partitioning for sensornet applications. NSDI 9:395–408Google Scholar
  37. 37.
    Piao JT, Yan J (2010) A network-aware virtual machine placement and migration approach in cloud computing. In: 2010 9th international conference on grid and cooperative computing (GCC). IEEE, pp 87–92Google Scholar
  38. 38.
    Gorbenko A, Popov V (2012) Task-resource scheduling problem. Int J Autom Comput 9(4):429–441MathSciNetCrossRefGoogle Scholar
  39. 39.
    Lin X, Wang Y, Xie Q et al (2015) Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment. Services Comput IEEE Trans 8(2):175–186CrossRefGoogle Scholar
  40. 40.
    Wang C, Li Z (2004) Parametric analysis for adaptive computation offloading. In: ACM SIGPLAN notices, vol 39, no. 6. ACM, pp 119–130Google Scholar
  41. 41.
    Ou S, Yang K, Liotta A (2006) An adaptive multi-constraint partitioning algorithm for offloading in pervasive systems. In: Fourth annual IEEE international conference on pervasive computing and communications, PerCom 2006, vol 10. IEEE, p 125Google Scholar
  42. 42.
    Ward C, Aravamudan N, Bhattacharya K et al (2010) Workload migration into clouds challenges, experiences, opportunities. In: 2010 IEEE 3rd international conference on cloud computing (CLOUD). IEEE, pp 164–171Google Scholar
  43. 43.
    Buchbinder N, Jain N, Menache I (2011) Online job-migration for reducing the electricity bill in the cloud. In: NETWORKING 2011. Springer, Berlin, pp 172–185Google Scholar
  44. 44.
    Ma RKK, Wang CL (2012) Lightweight application-level task migration for mobile cloud computing. In: 2012 IEEE 26th international conference on advanced information networking and applications (AINA). IEEE, pp 550–557Google Scholar
  45. 45.
    Niu R, Song W, Liu Y (2013) An Energy-efficient multisite offloading algorithm for mobile devices. Int J Distrib Sensor Netw. 2013:9 doi:10.1155/2013/518518 Google Scholar
  46. 46.
    Ksentini A, Taleb T, Chen M (2014) A Markov decision process-based service migration procedure for follow me cloud. In: 2014 IEEE international conference on communications (ICC). IEEE, pp 1350–1354Google Scholar
  47. 47.
    Zhang WS, Chen LC, Liu X et al (2014) An OSGi-based flexible and adaptive pervasive cloud infrastructure. Sci China Inf Sci 57(3):1–11MathSciNetGoogle Scholar
  48. 48.
    Gkatzikis L, Koutsopoulos I (2014) Mobiles on cloud nine: efficient task migration policies for cloud computing systems. In: 2014 IEEE 3rd international conference on cloud networking (CloudNet). IEEE, pp 204–210Google Scholar
  49. 49.
    Qian H, Andresen D (2015) An energy-saving task scheduler for mobile devices. In: 2015 IEEE/ACIS 14th international conference on computer and information science (ICIS). IEEE, pp 423–430Google Scholar
  50. 50.
    Chen X (2015) Decentralized computation offloading game for mobile cloud computing. IEEE Trans Parallel Distrib Syst 26(4):974–983CrossRefGoogle Scholar
  51. 51.
    Gai K, Qiu M, Zhao H et al (2016) Dynamic energy-aware Cloudlet-based mobile cloud computing model for green computing. J Netw Comput Appl 59:46–54CrossRefGoogle Scholar
  52. 52.
    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, 2010, p 7Google Scholar
  53. 53.
    Hung SH, Shih CS, Shieh JP et al (2012) Executing mobile applications on the cloud: framework and issues. Comput Math Appl 63(2):573–587CrossRefGoogle Scholar
  54. 54.
    Grønli TM, Ghinea G, Younas M (2014) Context-aware and automatic configuration of mobile devices in cloud-enabled ubiquitous computing. Pers Ubiquitous Comput 18(4):883–894CrossRefGoogle Scholar
  55. 55.
    Zhang K, Chen X (2014) Large-scale deep belief nets with mapreduce. IEEE Access 2:395–403CrossRefGoogle Scholar
  56. 56.
    Zhang Weishan, Duan Pengcheng, Li Zhongwei, Lu Qinghua, Gong Wenjuan, Yang Su (2015) A deep awareness framework for pervasive video cloud. IEEE Access 3:2227–2237CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Weishan Zhang
    • 1
  • Shouchao Tan
    • 1
  • Feng Xia
    • 2
    • 3
  • Xiufeng Chen
    • 4
  • Zhongwei Li
    • 1
  • Qinghua Lu
    • 1
  • Su Yang
    • 5
  1. 1.School of Computer and Communication EngineeringChina University of PetroleumQingdaoChina
  2. 2.School of SoftwareDalian University of TechnologyDalianChina
  3. 3.Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalianChina
  4. 4.Hisense TransTech, Ltd.QingdaoChina
  5. 5.College of Computer Science and TechnologyFudan UniversityShanghaiChina

Personalised recommendations