An intelligent resource allocation mechanism in the cloud computing environment

In cloud computing, all kinds of idle resources can be pooled to establish a resource pool, and a service combined with different kinds of resources is provided for users through virtualization. Therefore, an effective mechanism is necessary for managing and allocating the resources. In this paper, we propose an intelligent resource allocation mechanism based on double combinatorial auction. A feedback evaluation based reputation system is implemented to avoid malicious behavior, and a price decision mechanism based on a BP (back propagation) neural network is proposed to make decisions scientifically. Since the winner determination is an NP hard problem, group search optimization algorithm is introduced to achieve optimal allocation with the optimization goals being market surplus and total reputation. We also conduct empirical studies to demonstrate the feasibility and effectiveness of the proposed mechanism.

[1]  Zsehong Tsai,et al.  Bid-Proportional Auction for Resource Allocation in Capacity-Constrained Clouds , 2012, 2012 26th International Conference on Advanced Information Networking and Applications Workshops.

[2]  Hecht-Nielsen Theory of the backpropagation neural network , 1989 .

[3]  Q. Henry Wu,et al.  Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior , 2009, IEEE Transactions on Evolutionary Computation.

[4]  Jan Broeckhove,et al.  A Reverse Auction Market for Cloud Resources , 2011, GECON.

[5]  Guangwen Yang,et al.  A Knowledge-based Continuous Double Auction Model for Cloud Market , 2010, 2010 Sixth International Conference on Semantics, Knowledge and Grids.

[6]  John R. Gurd,et al.  Market-based grid resource allocation using a stable continuous double auction , 2007, 2007 8th IEEE/ACM International Conference on Grid Computing.

[7]  Marios D. Dikaiakos,et al.  Cloud Computing: Distributed Internet Computing for IT and Scientific Research , 2009, IEEE Internet Computing.

[8]  Liu Yan Dynamic Resource Pricing Based on Combinatorial Double Auction in Cloud Environment , 2012 .

[9]  Hui Cheng,et al.  IMMIGRANTS-ENHANCED MULTI-POPULATION GENETIC ALGORITHMS FOR DYNAMIC SHORTEST PATH ROUTING PROBLEMS IN MOBILE AD HOC NETWORKS , 2012, Appl. Artif. Intell..

[10]  Chuan Wu,et al.  A Reverse Auction Based Allocation Mechanism in the Cloud Computing Environment , 2013 .

[11]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[12]  Guiran Chang,et al.  Efficient Nash equilibrium based cloud resource allocation by using a continuous double auction , 2010, 2010 International Conference On Computer Design and Applications.

[13]  Robert Hecht-Nielsen,et al.  Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.

[14]  Isao Ono,et al.  Combinatorial Auction-Based Marketplace Mechanism for Cloud Service Reservation , 2012, IEICE Trans. Inf. Syst..

[15]  Andrew B. Whinston,et al.  Solving the combinatorial double auction problem , 2005, Eur. J. Oper. Res..

[16]  Angel A. Juan,et al.  A Multi-lane Double Auction for Economic-Based Service Management in the Cloud , 2011, Intelligent Networking, Collaborative Systems and Applications.

[17]  Radu Prodan,et al.  Double Auction-based Scheduling of Scientific Applications in Distributed Grid and Cloud Environments , 2011, Journal of Grid Computing.

[18]  Hui Cheng,et al.  Multi-robot navigation based QoS routing in self-organizing networks , 2013, Eng. Appl. Artif. Intell..

[19]  Daniel Grosu,et al.  Combinatorial Auction-Based Allocation of Virtual Machine Instances in Clouds , 2010, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.