access icon free Hierarchical evolutionary game based dynamic cloudlet selection and bandwidth allocation for mobile cloud computing environment

© The Institution of Engineering and Technology
Received 14/03/2018, Accepted 11/07/2018, Revised 08/06/2018, Published 12/09/2018

To bridge the gap between the resource-constrained mobile devices and the resource-demanding applications, mobile cloud computing (MCC) emerges for offloading complex tasks to a cloud server. Based on this concept, cloudlets, which move available resource to the vicinity of the mobile network, enhance further the system accessibility and performance. Moreover, to strengthen the network capacity in traffic intensive area, dense small cell network (DSCN) is proposed as one of the promising solutions. In this study, the operation of cloudlets and DSCN is collaboratively studied in order to further improve the system performance. On the one hand, users can select a cloudlet and dynamically adapt the connection according to the performance and the cost, which is referred to as a user-essential dynamic cloudlet selection problem. On the other hand, a cloudlet needs to set the optimal selling price and the size of resource for the users, which is considered as a cloudlet resource allocation problem. To jointly address the problems of dynamic cloudlet selection and resource allocation, the authors propose a hierarchical evolutionary game to maximise the utilities. Simulation studies are carried out to demonstrate the effectiveness of the proposed algorithms, which, indeed, improve the entire system performance significantly.

Inspec keywords: cloud computing; mobile computing; telecommunication traffic; bandwidth allocation; game theory; computer centres; evolutionary computation; resource allocation

Other keywords: mobile cloud computing environment; system performance; mobile network; resource-constrained mobile devices; dynamic cloudlet selection problem; traffic intensive area; bandwidth allocation; network capacity; cloud server; resource-demanding applications; system accessibility; hierarchical evolutionary game; cloudlet resource allocation problem; dense small cell network; optimal selling price; DSCN

Subjects: Game theory; Computer networks and techniques; Computer facilities; Mobile radio systems; Internet software; Mobile, ubiquitous and pervasive computing; Game theory; Optimisation techniques; Computer communications; Optimisation techniques

References

    1. 1)
      • 16. Xu, H., Li, B.: ‘Dynamic cloud pricing for revenue maximization’, IEEE Trans. Cloud Comput., 2013, 1, (2), pp. 158–171.
    2. 2)
      • 11. Tawalbeh, L., Jararweh, Y., Dosari, F.: ‘Large scale cloudlets deployment for efficient mobile cloud computing’, J. Netw., 2015, 10, (1), pp. 70–77.
    3. 3)
      • 8. Sun, X., Ansari, N.: ‘Green cloudlet network: A distributed green mobile cloud network’, IEEE Netw., 2017, 31, (1), pp. 64–70.
    4. 4)
      • 3. Satyanarayanan, M., Lewis, G., Morris, E., et al: ‘The role of cloudlets in hostile environments’, IEEE Pervasive Comput., 2013, 12, (4), pp. 40–49.
    5. 5)
      • 26. Vakilinia, S., Qiu, D., Ali, M.M.: ‘Optimal multi-dimensional dynamic resource allocation in mobile cloud computing’, EURASIP J. Wirel. Commun. Netw., 2014, 2014, (1), pp. 1–14.
    6. 6)
      • 29. Slotine, J.J.E, Li, W.: ‘Applied nonlinear control’ (Prentice Hall, Englewood Cliffs, NJ, 1991).
    7. 7)
      • 30. Niyato, D., Hossain, E., Han, Z.: ‘Dynamics of multiple-seller and multiple-buyer spectrum trading in cognitive radio networks: A game-theoretic modeling approach’, IEEE Trans. Mob. Comput., 2009, 8, (8), pp. 1009–1022.
    8. 8)
      • 14. Ren, S., van der Schaar, M.: ‘Joint design of dynamic scheduling and pricing in wireless cloud computing’. Proc. IEEE INFOCOM, Turin, Italy, 2013, pp. 185–189.
    9. 9)
      • 17. Yin, Z., Yu, F.R., Bu, S.: ‘Joint cloud computing and wireless networks operations: a game theoretic approach’. Proc. IEEE Global Communications Conf. (GLOBECOM), Austin, TX, USA, 2014, pp. 4977–4982.
    10. 10)
      • 15. Menache, I., Ozdaglar, A., Shimkin, N.: ‘Socially optimal pricing of cloud computing resources’. Proc. Int. Conf. Performance Evaluation Methodologies and Tools. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Cachan, France, 2011, pp. 322–331.
    11. 11)
      • 27. ITU: ‘Guidelines for evaluation of radio transmission technologies for IMT-A 2000’ (ITU Recommendation M.1225, 1997), http://www.itu.int/rec/R-REC-M.1225-0-199702-I/en.
    12. 12)
      • 20. Chai, B., Chen, J., Yang, Z., et al: ‘Demand response management with multiple utility companies: A two-level game approach’, IEEE Trans. Smart Grid, 2014, 5, (2), pp. 722–731.
    13. 13)
      • 25. Zhang, Y., Niyato, D., Wang, P.: ‘An auction mechanism for resource allocation in mobile cloud computing systems’. Proc. Int. Conf. Wireless Algorithms, Systems, and Applications, Springer, Berlin, Heidelberg, 2013, pp. 76–87.
    14. 14)
      • 2. Dinh, H.T., Lee, C., Niyato, D., et al: ‘A survey of mobile cloud computing: architecture, applications, and approaches’, Wirel. Commun. Mob. Comput., 2013, 13, (18), pp. 1587–1611.
    15. 15)
      • 28. Hofbauer, J., Sigmund, K.: ‘Evolutionary game dynamics’, Bull. Am. Math. Soc., 2003, 40, (4), pp. 479–519.
    16. 16)
      • 6. Tang, L., Chen, X., He, S.: ‘When social network meets mobile cloud: A social group utility approach for optimizing computation offloading in cloudlet’, IEEE Access, 2016, 4, (99), pp. 5868–5879.
    17. 17)
      • 24. Misra, S., Das, S., Khatua, M., et al: ‘QoS-guaranteed bandwidth shifting and redistribution in mobile cloud environment’, IEEE Trans. Cloud Comput., 2014, 2, (2), pp. 181–193.
    18. 18)
      • 23. Wang, Y., Meng, S., Chen, Y., et al: ‘Multi-leader multi-follower stackelberg game based dynamic resource allocation for mobile cloud computing environment’, Wirel. Pers. Commun., 2017, 93, (2), pp. 461–480.
    19. 19)
      • 21. Niyato, D., Hossain, E.: ‘Dynamics of network selection in heterogeneous wireless networks: an evolutionary game approach’, IEEE Trans. Veh. Technol., 2009, 58, (4), pp. 2008–2017.
    20. 20)
      • 1. Cisco: ‘visual networking index: global mobile data traffic forecast update, 2014-2019’ (Cisco Public Information, 2014) http://www.cisco.com.
    21. 21)
      • 4. Luan, Z., Qu, H., Zhao, J., et al: ‘Correntropy induced joint power and admission control algorithm for dense small cell network’, IET Commun., 2016, 10, (16), pp. 2154–2161.
    22. 22)
      • 31. Han, Z., Niyato, D., Saad, W., et al: ‘Game theory in wireless and communication networks: theory, models, and applications’ (Cambridge University Press, Cambridge, UK, 2012).
    23. 23)
      • 22. Tang, X., Ren, P., Han, Z.: ‘Hierarchical competition as equilibrium program with equilibrium constraints towards security-enhanced wireless networks’, IEEE J. Sel. Areas Commun., 2018, PP, (99), pp. 1–1.
    24. 24)
      • 18. Tang, L., Chen, H.: ‘Joint pricing and capacity planning in the iaas cloud market’, IEEE Trans. Cloud Comput., 2017, 5, (1), pp. 57–70.
    25. 25)
      • 12. Gai, K., Qiu, M., Zhao, H., et al: ‘Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing’, J. Netw. Comput. Appl., 2016, 59, (C), pp. 46–54.
    26. 26)
      • 5. Zhang, Y., Niyato, D., Wang, P.: ‘Offloading in mobile cloudlet systems with intermittent connectivity’, IEEE Trans. Mob. Comput., 2015, 14, (12), pp. 2516–2529.
    27. 27)
      • 9. Hoang, D.T., Niyato, D., Wang, P.: ‘Optimal admission control policy for mobile cloud computing hotspot with cloudlet’. Proc. IEEE Wireless Communications and Networking Conf. (WCNC), Shanghai, China, 2012, pp. 3145–3149.
    28. 28)
      • 13. Chi, F., Wang, X., Cai, W., et al: ‘Ad-hoc cloudlet based cooperative cloud gaming’, IEEE Trans. Cloud Comput., 2017, PP, (99), pp. 1–1.
    29. 29)
      • 10. Yu, Y., Zhang, J., Letaief, K.B.: ‘Joint subcarrier and CPU time allocation for mobile edge computing’. Proc. IEEE Global Communications Conf. (GLOBECOM), Washington, DC, USA, 2016, pp. 1–6.
    30. 30)
      • 7. Rimal, B.P., Van, D.P., Maier, M.: ‘Cloudlet enhanced fiber-wireless access networks for mobile-edge computing’, IEEE Trans. Wirel. Commun., 2017, 16, (6), pp. 3601–3618.
    31. 31)
      • 19. Meng, S., Wang, Y., Miao, Z., et al: ‘Joint optimization of wireless bandwidth and computing resource in cloudlet-based mobile cloud computing environment’, Peer-to-Peer Net. Appl., 2017, 11, (3), pp. 1–11.
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