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Introduction

  • Seyed Eman Mahmoodi
  • Koduvayur Subbalakshmi
  • R. N. Uma
Chapter
Part of the Signals and Communication Technology book series (SCT)

Abstract

Recent advances in smartphone technologies and wireless communications have created a strong uptick in the use of smart applications over web-enabled, resource-constrained end devices. In order to support such computation and data intensive applications over wireless devices, two problems must be addressed: (1) the resource constraints on the device (e.g., battery power, memory, etc.) and (2) network constraints (e.g., capacity/spectrum and latency limitations). Multiple radio spectrum access technologies (multi-RAT) is becoming one of the ways to address some of the network related problems (e.g., capacity). The heterogeneous network (HetNets) paradigm, enabling multi-RATs is expected to become a mainstay of future wireless networks. Simultaneous access to multiple RATs or spectrum bands can be implemented at the transport layer, network layer, or PHY/MAC layer of wireless devices. The growth of mobile virtual network operators (MVNO) will also facilitate such multi-RAT opportunistic spectrum access. Google’s recent deal with Sprint and T-Mobile is an example in this direction.

At the device level, cloud offloading has emerged as an indispensable part of the solution to the device level constraints. However, offloading computations to a remote cloud can also place an additional burden on the already overburdened wireless backbone. The term “cloud offloading” can mean data flow offloading or offloading computationally intense tasks to the cloud along with the data resulting from the computations. Here we mean the latter. In this chapter, we discuss the factors affecting computation offloading and discuss the organization of the book.

References

  1. 2.
    S. Barbarossa, S. Sardellitti, P. Di Lorenzo, Computation offloading for mobile cloud computing based on wide cross-layer optimization, in Future Network and Mobile Summit (FutureNetworkSummit), July 2013, pp. 1–10Google Scholar
  2. 4.
    C. Buschmann, D. Pfisterer, S. Fischer, S.P. Fekete, A. Kröller, Spyglass: a wireless sensor network visualizer. ACM Sigbed Rev. 2(1), 1–6 (2005)CrossRefGoogle Scholar
  3. 5.
    W. Cai, V.C. Leung, M. Chen, Next generation mobile cloud gaming, in IEEE International Symposium on Service Oriented System Engineering (SOSE) (2013), pp. 551–560Google Scholar
  4. 6.
    X. Chen, J. Wu, Y. Cai, H. Zhang, T. Chen, Energy-efficiency oriented traffic offloading in wireless networks: a brief survey and a learning approach for heterogeneous cellular networks. IEEE J. Sel. Areas Commun. 33(4), 627–640 (2015)CrossRefGoogle Scholar
  5. 10.
    E. Cuervo, A. Balasubramanian, D.-K. Cho, A. Wolman, S. Saroiu, R. Chandra, P. Bahl, MAUI: making smartphones last longer with code offload, in Proceedings of the International Conference on Mobile Systems, Applications, and Services, MobiSys (ACM, New York, 2010), pp. 49–62Google Scholar
  6. 11.
    T. Dao, I. Singh, H.V. Madhyastha, S.V. Krishnamurthy, G. Cao, P. Mohapatra, TIDE: a user-centric tool for identifying energy hungry applications on smartphones, in IEEE International Conference on Distributed Computing Systems (ICDCS), June 2015, pp. 123–132Google Scholar
  7. 12.
    S. Deng, L. Huang, J. Taheri, A. Zomaya, Computation offloading for service workflow in mobile cloud computing. IEEE Trans. Parallel Distrib. Syst. PP(99), 1–1 (2014)Google Scholar
  8. 13.
    W. Dong, S. Rallapalli, R. Jana, L. Qiu, K. K. Ramakrishnan, L. Razoumov, Y. Zhang, T.W. Cho, iDEAL: incentivized dynamic cellular offloading via auctions. IEEE/ACM Trans. Netw. 22(4), 1271–1284 (2014)CrossRefGoogle Scholar
  9. 16.
    X. Gu, K. Nahrstedt, A. Messer, I. Greenberg, D. Milojicic, Adaptive offloading for pervasive computing. IEEE Pervasive Comput. 3(3), 66–73 (2004)CrossRefGoogle Scholar
  10. 17.
    C. Gui, P. Mohapatra, Power conservation and quality of surveillance in target tracking sensor networks, in Proceedings of the 10th Annual International Conference on Mobile Computing and Networking, MobiCom’04 (2004), pp. 129–143Google Scholar
  11. 18.
    K. Hong, S. Sengupta, R. Chandramouli, Spiderradio: a cognitive radio implementation using IEEE 802.11 components. IEEE Trans. Mob. Comput. 12(11), 2105–2118 (2013)CrossRefGoogle Scholar
  12. 19.
    D. Huang, P. Wang, D. Niyato, A dynamic offloading algorithm for mobile computing. IEEE Trans. Wirel. Commun. 11(6), 1991–1995 (2012)CrossRefGoogle Scholar
  13. 20.
    D. Kaspar, Multipath aggregation of heterogeneous access networks. SIGMultimedia Rec. 4(1), 27–28 (2012)MathSciNetCrossRefGoogle Scholar
  14. 21.
    S. Kosta, A. Aucinas, P. Hui, R. Mortier, X. Zhang, Thinkair: dynamic resource allocation and parallel execution in the cloud for mobile code offloading, in IEEE Proceedings of INFOCOM (2012), pp. 945–953Google Scholar
  15. 24.
    K. Kumar, Y.H. Lu, Cloud computing for mobile users: can offloading computation save energy? Computer 43, 51–56 (2010)CrossRefGoogle Scholar
  16. 25.
    Y. Li, M. Qian, D. Jin, P. Hui, Z. Wang, S. Chen, Multiple mobile data offloading through disruption tolerant networks. IEEE Trans. Mob. Comput. 13(7), 1579–1596 (2014)CrossRefGoogle Scholar
  17. 26.
    S. Li, E. Ekici, N. Shroff, Throughput-optimal queue length based CSMA/CA algorithm for cognitive radio networks. IEEE Trans. Mob. Comput. 14(5), 1098–1108 (2015)CrossRefGoogle Scholar
  18. 32.
    X. Ma, Y. Zhao, L. Zhang, H. Wang, L. Peng, When mobile terminals meet the cloud: computation offloading as the bridge. IEEE Mag. Netw. 27(5), 28–33 (2013)CrossRefGoogle Scholar
  19. 34.
    S.E. Mahmoodi, K.P.S. Subbalakshmi, A time-adaptive heuristic for cognitive cloud offloading in multi-rat enabled wireless devices. IEEE Trans. Cogn. Commun. Netw. 2(2), 194–207 (2016)CrossRefGoogle Scholar
  20. 43.
    M. Patel, B. Naughton, C. Chan, N. Sprecher, S. Abeta, A. Neal, et al., Mobile-edge computing introductory technical white paper, in White Paper, Mobile-Edge Computing (MEC) Industry Initiative (2014)Google Scholar
  21. 45.
    P. Shu, F. Liu, H. Jin, M. Chen, F. Wen, Y. Qu, eTime: energy-efficient transmission between cloud and mobile devices, in IEEE Conference on Computer Communications (INFOCOM), April 2013, pp. 195–199Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Seyed Eman Mahmoodi
    • 1
  • Koduvayur Subbalakshmi
    • 2
  • R. N. Uma
    • 3
  1. 1.Department of Research and InnovationInteractions CorporationNew YorkUSA
  2. 2.Department of Electrical and Computer EngineeringStevens Institute of TechnologyHobokenUSA
  3. 3.Department of Mathematics and PhysicsNorth Carolina Central UniversityDurhamUSA

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