Cheating-Resilient Bandwidth Distribution in Mobile Cloud Computing

In mobile cloud computing (MCC), optimal utilization of resources (e.g., bandwidth), while maintaining the required level of quality-of-services (QoS), is essential. A user participating in the resource allocation process can provide untruthful information for acquiring undue advantages with respect to the allocated resource amount, and the cost incurred. In this paper, we identify, formulate, and address the problem of such misbehaviour. We formulate the bandwidth distribution as a constrained convex utility maximization problem, and solve it using the proposed cheating-resilient bandwidth distribution (CRAB) scheme. Numerical analysis shows that, in CRAB, the misbehaving user is impelled to behave normally as the misbehaviour increases its own cost while the other users including the cloud service provider (CSP) get benefit in terms of revenue. We investigate the existence of Nash Equilibrium (NE) of the proposed scheme. Both the problem and the solution are extensively analysed theoretically. The maximum and minimum selling prices of bandwidth, and the optimal solution for individual user are computed using the method of Lagrange multiplier.

[1]  Mohammad S. Obaidat,et al.  Quality-Assured Secured Load Sharing in Mobile Cloud Networking Environment , 2019, IEEE Transactions on Cloud Computing.

[2]  Sherali Zeadally,et al.  Performance analysis of Bayesian coalition game-based energy-aware virtual machine migration in vehicular mobile cloud , 2015, IEEE Network.

[3]  Keke Gai,et al.  Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing , 2016, J. Netw. Comput. Appl..

[4]  A. Goldsmith,et al.  Variable-rate variable-power MQAM for fading channels , 1996, Proceedings of Vehicular Technology Conference - VTC.

[5]  Rajashri Khanai,et al.  Addressing mobile Cloud Computing security issues: A survey , 2015, 2015 International Conference on Communications and Signal Processing (ICCSP).

[6]  J. Morris Chang,et al.  QoS-Aware Data Replication for Data-Intensive Applications in Cloud Computing Systems , 2013, IEEE Transactions on Cloud Computing.

[7]  J. Goodman Note on Existence and Uniqueness of Equilibrium Points for Concave N-Person Games , 1965 .

[8]  Keqin Li,et al.  Resource Allocation in Cloud Environment: A Model Based on Double Multi-attribute Auction Mechanism , 2014, 2014 IEEE 6th International Conference on Cloud Computing Technology and Science.

[9]  Takuji Tachibana,et al.  VCG auction-based bandwidth allocation with network coding in wireless networks , 2011 .

[10]  Joel J. P. C. Rodrigues,et al.  Mapping of sensor nodes with servers in a mobile Health-Cloud environment , 2013, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013).

[11]  Cristina Cervello-Pastor,et al.  On the optimal allocation of virtual resources in cloud computing networks , 2013, IEEE Transactions on Computers.

[12]  Xinbing Wang,et al.  Spectrum Sharing in Cognitive Radio Networks—An Auction-Based Approach , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Samee Ullah Khan,et al.  Future Generation Computer Systems ( ) – Future Generation Computer Systems towards Secure Mobile Cloud Computing: a Survey , 2022 .

[14]  Orazio Tomarchio,et al.  A Procurement Auction Market to Trade Residual Cloud Computing Capacity , 2015, IEEE Transactions on Cloud Computing.

[15]  Dario Pompili,et al.  Uncertainty-Aware Autonomic Resource Provisioning for Mobile Cloud Computing , 2015, IEEE Transactions on Parallel and Distributed Systems.

[16]  Chonho Lee,et al.  Auction Approaches for Resource Allocation in Wireless Systems: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[17]  Daniel Grosu,et al.  A Combinatorial Auction-Based Mechanism for Dynamic VM Provisioning and Allocation in Clouds , 2013, IEEE Transactions on Cloud Computing.

[18]  Bin Wang,et al.  Utility-based resource allocation for mixed traffic in wireless networks , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[19]  Dusit Niyato,et al.  An Auction Mechanism for Resource Allocation in Mobile Cloud Computing Systems , 2013, WASA.

[20]  Chonho Lee,et al.  A survey of mobile cloud computing: architecture, applications, and approaches , 2013, Wirel. Commun. Mob. Comput..

[21]  Zhu Han,et al.  A Controlled Coalitional Game for Wireless Connection Sharing and Bandwidth Allocation in Mobile Social Networks , 2010, 2010 IEEE Global Telecommunications Conference GLOBECOM 2010.

[22]  Alexey V. Vinel,et al.  A Novel Resource Reservation Scheme for Mobile PHEVs in V2G Environment Using Game Theoretical Approach , 2015, IEEE Transactions on Vehicular Technology.

[23]  Guy Pujolle,et al.  A dynamic bandwidth allocator for virtual machines in a cloud environment , 2012, 2012 IEEE Consumer Communications and Networking Conference (CCNC).

[24]  V. Kavitha,et al.  A survey on security issues in service delivery models of cloud computing , 2011, J. Netw. Comput. Appl..

[25]  Haiyang Hu,et al.  An Anti-cheating Bidding Approach for Resource Allocation in Cloud Computing Environments ? , 2012 .

[26]  Mohammad S. Obaidat,et al.  QoS-Guaranteed Bandwidth Shifting and Redistribution in Mobile Cloud Environment , 2014, IEEE Transactions on Cloud Computing.

[27]  K. J. Ray Liu,et al.  Spectrum Auction Games for Multimedia Streaming Over Cognitive Radio Networks , 2010, IEEE Transactions on Communications.

[28]  Dusit Niyato,et al.  Dynamic Bandwidth Allocation under Uncertainty in Cognitive Radio Networks , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.