An adaptive offloading framework for Android applications in mobile edge computing


Mobile edge computing (MEC) provides a fresh opportunity to significantly reduce the latency and battery energy consumption of mobile applications. It does so by enabling the offloading of parts of the applications on mobile edges, which are located in close proximity to the mobile devices. Owing to the geographical distribution of mobile edges and the mobility of mobile devices, the runtime environment of MEC is highly complex and dynamic. As a result, it is challenging for application developers to support computation offloading in MEC compared with the traditional approach in mobile cloud computing, where applications use only the cloud for offloading. On the one hand, developers have to make the offloading adaptive to the changing environment, where the offloading should dynamically occur among available computation nodes. On the other hand, developers have to effectively determine the offloading scheme each time the environment changes. To address these challenges, this paper proposes an adaptive framework that supports mobile applications with offloading capabilities in MEC. First, based on our previous study (DPartner), a new design pattern is proposed to enable an application to be dynamically offloaded among mobile devices, mobile edges, and the cloud. Second, an estimation model is designed to automatically determine the offloading scheme. In this model, different parts of the application may be executed on different computation nodes. Finally, an adaptive offloading framework is implemented to support the design pattern and the estimation model. We evaluate our framework on two real-world applications. The results demonstrate that our approach can aid in reducing the response time by 8%–50% and energy consumption by 9%–51% for computation-intensive applications.

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  1. 1

    Berglund M E, Duvall J, Dunne L E. A survey of the historical scope and current trends of wearable technology applications. In: Proceedings of ACM International Symposium on Wearable Computers, Heidelberg, 2016. 40–43

  2. 2

    Yang F C, Li J L, Lei T, et al. Architecture and key technologies for Internet of vehicles: a survey. J Commun Inf Netw, 2017, 2: 1–17

    Article  Google Scholar 

  3. 3

    Li P, Yu X, Peng X Y, et al. Fault-tolerant cooperative control for multiple UAVs based on sliding mode techniques. Sci China Inf Sci, 2017, 60: 070204

    Article  Google Scholar 

  4. 4

    Mei H, Liu X Z. Software techniques for Internet computing: current situation and future trend. Chin Sci Bull, 2010, 55: 3510–3516

    Article  Google Scholar 

  5. 5

    Yang K, Ou S M, Chen H H. On effective offloading services for resource-constrained mobile devices running heavier mobile Internet applications. IEEE Commun Mag, 2008, 46: 56–63

    Article  Google Scholar 

  6. 6

    Paradiso J A, Starner T. Energy scavenging for mobile and wireless electronics. IEEE Pervasive Comput, 2005, 4: 18–27

    Article  Google Scholar 

  7. 7

    Kumar K, Lu Y H. Cloud computing for mobile users. Computer, 2011, 43: 51–56

    Article  Google Scholar 

  8. 8

    Goyal S, Carter J. A lightweight secure cyber foraging infrastructure for resource-constrained devices. In: Proceedings of Mobile Computing Systems and Applications, Windermere, 2004. 186–195

  9. 9

    Balan R, Flinn J, Satyanarayanan M, et al. The case for cyber foraging. In: Proceedings of ACM Sigops European Workshop, Saint-Emilion, 2002. 87–92

  10. 10

    Balan R K, Gergle D, Satyanarayanan M, et al. Simplifying cyber foraging for mobile devices. In: Proceedings of International Conference on Mobile Systems, Applications and Services, San Juan, 2007. 272–285

  11. 11

    Balan R K, Satyanarayanan M, Park S Y, et al. Tactics-based remote execution for mobile computing. In: Proceedings of International Conference on Mobile Systems, Applications, and Services, San Francisco, 2003. 273–286

  12. 12

    Gu X H, Nahrstedt K, Messer A, et al. Adaptive offloading inference for delivering applications in pervasive computing environments. In: Proceedings of IEEE International Conference on Pervasive Computing and Communications, Fort Worth, 2003. 107

  13. 13

    Kumar K, Liu J B, Lu Y H, et al. A survey of computation offloading for mobile systems. Mobile Netw Appl, 2013, 18: 129–140

    Article  Google Scholar 

  14. 14

    Philippsen M, Zenger M. JavaParty — transparent remote objects in Java. Concurrency-Pract Exper, 1997, 9: 1225–1242

    Article  Google Scholar 

  15. 15

    Hunt G C, Scott M L. The coign automatic distributed partitioning system. In: Proceedings of Enterprise Distributed Object Computing Workshop, La Jolla, 1999. 252–262

  16. 16

    Shi W S, Cao J, Zhang Q, et al. Edge computing: vision and challenges. IEEE Internet Things J, 2016, 3: 637–646

    Article  Google Scholar 

  17. 17

    Chiang M, Zhang T. Fog and IoT: an overview of research opportunities. IEEE Internet Things J, 2016, 3: 854–864

    Article  Google Scholar 

  18. 18

    Tran T X, Hajisami A, Pandey P, et al. Collaborative mobile edge computing in 5G networks: new paradigms, scenarios, and challenges. IEEE Commun Mag, 2017, 55: 54–61

    Article  Google Scholar 

  19. 19

    Abbas N, Zhang Y, Taherkordi A, et al. Mobile edge computing: a survey. IEEE Internet Things J, 2018, 5: 450–465

    Article  Google Scholar 

  20. 20

    Wang S G, Xu J L, Zhang N, et al. A survey on service migration in mobile edge computing. IEEE Access, 2018, 6: 23511–23528

    Article  Google Scholar 

  21. 21

    Zhang Y, Huang G, Liu X Z, et al. Refactoring Android Java code for on-demand computation offloading. SIGPLAN Not, 2012, 47: 233

    Article  Google Scholar 

  22. 22

    Chen X, Chen S H, Zeng X, et al. Framework for context-aware computation offloading in mobile cloud computing. J Cloud Comp, 2017, 6: 1

    Article  Google Scholar 

  23. 23

    Chen X, Jiao L, Li W Z, et al. Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans Netw, 2016, 24: 2795–2808

    Article  Google Scholar 

  24. 24

    Chen X. Decentralized computation offloading game for mobile cloud computing. IEEE Trans Parallel Distrib Syst, 2015, 26: 974–983

    Article  Google Scholar 

  25. 25

    Lei T, Wang S G, Li J L, et al. AOM: adaptive mobile data traffic offloading for M2M networks. Pers Ubiquit Comput, 2016, 20: 863–873

    Article  Google Scholar 

  26. 26

    Wang S G, Lei T, Zhang L Y, et al. Offloading mobile data traffic for QoS-aware service provision in vehicular cyber-physical systems. Future Gener Comput Syst, 2016, 61: 118–127

    Article  Google Scholar 

  27. 27

    Kemp R, Palmer N, Kielmann T, et al. Cuckoo: a computation offloading framework for smartphones. In: Proceedings of International Conference on Mobile Computing, Applications, and Services, Santa Clara, 2010. 59–79

  28. 28

    Cuervo E, Balasubramanian A, Cho D K, et al. Maui: making smartphones last longer with code offload. In: Proceedings of International Conference on Mobile Systems, Applications, and Services, San Francisco, 2010. 49–62

  29. 29

    Kosta S, Aucinas A, Hui P, et al. Thinkair: dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In: Proceedings IEEE INFOCOM, Orlando, 2012. 945–953

  30. 30

    Chun B G, Ihm S, Maniatis P, et al. Clonecloud: elastic execution between mobile device and cloud. In: Proceedings of Conference on Computer Systems, Salzburg, 2011. 301–314

  31. 31

    Zhou B W, Dastjerdi A V, Calheiros R N, et al. A context sensitive offloading scheme for mobile cloud computing service. In: Proceedings of IEEE International Conference on Cloud Computing, Washington, 2015. 869–876

  32. 32

    Cheng Z X, Li P, Wang J B, et al. Just-in-time code offloading for wearable computing. IEEE Trans Emerg Top Comput, 2015, 3: 74–83

    Article  Google Scholar 

  33. 33

    Jin X M, Liu Y N, Fan W H, et al. Multisite computation offloading in dynamic mobile cloud environments. Sci China Inf Sci, 2017, 60: 089301

    Article  Google Scholar 

  34. 34

    Huang G, Cai H Q, Swiech M, et al. DelayDroid: an instrumented approach to reducing tail-time energy of Android apps. Sci China Inf Sci, 2017, 60: 012106

    Article  Google Scholar 

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This work was supported by National Key R&D Program of China (Grant No. 2017YFB1002000), National Natural Science Foundation of China (Grant No. 61725201), and Talent Program of Fujian Province for Distinguished Young Scholars in Higher Education. Yun MA’s work was supported by China Postdoctoral Science Foundation.

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Correspondence to Gang Huang.

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Chen, X., Chen, S., Ma, Y. et al. An adaptive offloading framework for Android applications in mobile edge computing. Sci. China Inf. Sci. 62, 82102 (2019).

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  • computation offloading
  • software adaptation
  • mobile edge computing
  • application refactoring
  • Android