logo

SCIENCE CHINA Information Sciences, Volume 62, Issue 8: 082102(2019) https://doi.org/10.1007/s11432-018-9749-8

An adaptive offloading framework for Android applications in mobile edge computing

More info
  • ReceivedMar 8, 2018
  • AcceptedDec 19, 2018
  • PublishedJul 12, 2019

Abstract

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.


Acknowledgment

This work was supported by National Key RD 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.


References

[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. Google Scholar

[2] Yang F C, Li J L, Lei T. Architecture and key technologies for Internet of Vehicles: a survey. J Commun Inf Netw, 2017, 2: 1-17 CrossRef Google Scholar

[3] Li P, Yu X, Peng X Y. Fault-tolerant cooperative control for multiple UAVs based on sliding mode techniques. Sci China Inf Sci, 2017, 60: 070204 CrossRef Google Scholar

[4] Mei H, Liu X Z. Software techniques for Internet Computing: Current situation and future trend. Chin Sci Bull, 2010, 55: 3510-3516 CrossRef ADS Google Scholar

[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 CrossRef Google Scholar

[6] Paradiso J A, Starner T. Energy Scavenging for Mobile and Wireless Electronics. IEEE Pervasive Comput, 2005, 4: 18-27 CrossRef Google Scholar

[7] Kumar K and Lu Y H. Cloud computing for mobile users. Computer, 2011, 1-1. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[13] Kumar K, Liu J H, Lu Y H. A Survey of Computation Offloading for Mobile Systems. Mobile Netw Appl, 2013, 18: 129-140 CrossRef Google Scholar

[14] Philippsen M, Zenger M. JavaParty - transparent remote objects in Java. Concurrency-Pract Exper 1997, 9: 1225--1242. Google Scholar

[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. Google Scholar

[16] Shi W S, Cao J, Zhang Q. Edge Computing: Vision and Challenges. IEEE Internet Things J, 2016, 3: 637-646 CrossRef Google Scholar

[17] Chiang M, Zhang T. Fog and IoT: An Overview of Research Opportunities. IEEE Internet Things J, 2016, 3: 854-864 CrossRef Google Scholar

[18] Tran T X, Hajisami A, Pandey P. Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges. IEEE Commun Mag, 2017, 55: 54-61 CrossRef Google Scholar

[19] Abbas N, Zhang Y, Taherkordi A. Mobile Edge Computing: A Survey. IEEE Internet Things J, 2018, 5: 450-465 CrossRef Google Scholar

[20] Wang S G, Xu J L, Zhang N. A Survey on Service Migration in Mobile Edge Computing. IEEE Access, 2018, 6: 23511-23528 CrossRef Google Scholar

[21] Zhang Y, Huang G, Liu X Z. Refactoring android Java code for on-demand computation offloading. SIGPLAN Not, 2012, 47: 233 CrossRef Google Scholar

[22] Chen X, Chen S H, Zeng X. Framework for context-aware computation offloading in mobile cloud computing. J Cloud Comp, 2017, 6: 1 CrossRef Google Scholar

[23] Chen X, Jiao L, Li W Z. Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing. IEEE/ACM Trans Networking, 2016, 24: 2795-2808 CrossRef Google Scholar

[24] Chen X. Decentralized Computation Offloading Game for Mobile Cloud Computing. IEEE Trans Parallel Distrib Syst, 2015, 26: 974-983 CrossRef Google Scholar

[25] Lei T, Wang S G, Li J L. AOM: adaptive mobile data traffic offloading for M2M networks. Pers Ubiquit Comput, 2016, 20: 863-873 CrossRef Google Scholar

[26] Wang S G, Lei T, Zhang L Y. Offloading mobile data traffic for QoS-aware service provision in vehicular cyber-physical systems. Future Generation Comput Syst, 2016, 61: 118-127 CrossRef Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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. Google Scholar

[32] Cheng Z X, Li P, Wang J B. Just-in-Time Code Offloading for Wearable Computing. IEEE Trans Emerg Top Comput, 2015, 3: 74-83 CrossRef Google Scholar

[33] Jin X M, Liu Y N, Fan W H. Multisite computation offloading in dynamic mobile cloud environments. Sci China Inf Sci, 2017, 60: 089301 CrossRef Google Scholar

[34] Huang G, Cai H Q, Swiech M. DelayDroid: an instrumented approach to reducing tail-time energy of Android apps. Sci China Inf Sci, 2017, 60: 012106 CrossRef Google Scholar

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1