Optimal Cognitive Scheduling and Cloud Offloading Using Multi-Radios

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


In this chapter, we move towards the generalization of the problems considered in Chaps.  3 and  4. This extension is achieved in three ways: (1) by allowing for a natural scheduling order and more general dependencies between the components of the application, (2) using all viable RAT interfaces for cloud offloading, and (3) taking a time-adaptive approach that is cognizant of and responsive to the changes in the wireless network conditions over time. We coin the term cognitive scheduling and cloud offloading (CSCO) for this class of approaches. A mathematical model for the cost function is developed and methods to solve this optimization problem are discussed.


  1. 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
  2. 22.
    D. Kovachev, T. Yu, R. Klamma, Adaptive computation offloading from mobile devices into the cloud, in IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) (2012), pp. 784–791Google Scholar
  3. 28.
    X. Lin, Y. Wang, Q. Xie, M. Pedram, Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment. IEEE Trans. Serv. Comput. 8(2), 175–186 (2015)CrossRefGoogle Scholar
  4. 38.
    M.J. Neely, Stochastic Network Optimization with Application to Communication and Queueing Systems (Morgan and Claypool Publishers, San Rafael, 2010)CrossRefGoogle Scholar
  5. 40.
    S. Ou, K. Yang, J. Zhang, An effective offloading middleware for pervasive services on mobile devices. Pervasive Mob. Comput. 3(4), 362–385 (2007)CrossRefGoogle Scholar
  6. 41.
    J.K. Ousterhout, Scripting: higher level programming for the 21st century. IEEE Comput. Mag. 31, 23–30 (1998)CrossRefGoogle Scholar
  7. 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
  8. 48.
    H. Topcuoglu, S. Hariri, M.-Y. Wu, Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)CrossRefGoogle Scholar
  9. 53.
    K. Zhu, E. Hossain, D. Niyato, Pricing, spectrum sharing, and service selection in two-tier small cell networks: a hierarchical dynamic game approach. IEEE Trans. Mob. Comput. 13(8), 1843–1856 (2014)CrossRefGoogle 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

Personalised recommendations