Decentralized and Optimal Resource Cooperation in Geo-distributed Mobile Cloud Computing
Mobile cloud computing is a key enabling technology in the era of Internet-of-Things. Geo-distributed mobile cloud computing (GMCC) is a new scenario that adds geography consideration in mobile cloud computing. In GMCC, users are able to access cloud resource that are geographically close to their mobile devices. This is expected to reduce communications delay and service providers’ cost compared to the traditional centralized approach. In this paper, we focus on resource sharing through cooperation among service providers in geo-distributed mobile cloud computing. Then, we propose two different strategies for efficient resource cooperation in geographically distributed data centers. Further, we present a coalition game theoretical approach to deal with the competition and cooperation among service providers. Utility functions have been specifically considered to incorporate the cost related to virtual machine migration and resource utilization. Illustrative results indicate that our proposed schemes are able to efficiently utilize limited resource with Quality-of-Service (QoS) consideration. Index Terms—Mobile cloud computing, resource management, cooperation, game theory EXISTING SYSTEM: Geo-distributed mobile cloud computing (GMCC) is an emerging paradigm that integrate location information in mobile cloud computing . In GMCC, users are able to access cloud storage and computation resource that are geographically close to their mobile devices , . However, vehicles’ high mobility poses a significant challenge for maintaining a stable network topology as well as providing reliable resource. In addition, various applications have different resource requirements . Running mobile applications in GMCC needs to applications which are not easily performed on a resourceconstrained mobile device , . GMCC is in particular beneficial for high-mobility vehicle network where vehicles commonly have position information at any time. In case of fast moving vehicles, cost-efficient resource allocation scheme is very important for location based service, navigation service, and accident alert . Therefore, it makes resource allocation more complicated and crucial. PROPOSED SYSTEM: In this section, we first describe the virtualized framework of GMCC network and give the definition of 2-layer network graph. Then, we build the economic model. Finally, we discuss VM resource allocation. A. Wireless Network and Data Center We consider M geographically distributed clouds in different regions. The resource in each cloud can be rented by N different SPs. One SP can rent long-term reserving resource from more than one region. SP k in region l is denoted by SPl k (k = 1, 2, _ _ _ ,N, l = 1, 2, _ _ _ ,M). The long-term reserving resource is fixed asset of SPl k, and denoted by the maximum capacity of each kind of resource: the CPU resource (maxCl k), the memory resource (maxMl k) and the bandwidth resource (maxBl k). The bandwidth resource in mobile network is radio resource for wireless access. The occupied resource should satisfied the constrains. SP can extend the capability by renting short-term resource from other SPs as an on-demand basis method. The resource requirement from user j can be denoted as Rj = (k, l,Cj ,Mj ,Bj , Tj), which includes the information of SP k in region l, the required CPU resource Cj , the required memory resource Mj , the required bandwidth Bj , and the maximum latency time Tj . Every application has a specific maximum latency Tj which should not be exceeded to provide a satisfactory service. The model in this paper is proposed to take advantages of both long-term reserving resource and short-term demanding resource. Resource cooperation provides a approach for enhancing service capability and making full use of cloud resource. The directed 2-layer graph gi = fV,Ei,wg (i = 1, 2) is employed to describe the resource distribution and relationship of SPs, as shown in Fig. 3. The set of vertex V denotes all SPs in the graph . We encode SPl k from S1 to S12 in the graph to form a matrix and distinguish them. SPs in the graph are connected through wired communication. The connected edge Ei is proposed for the capability to form an coalition. Weight w on each edge denotes the difference of resource utilization. For example, wi;j is the difference of resource utilization of Si and Sj . If Si rent resource from Sj and wi;j > 0, the utilization of Si and Sj will come to balance. Therefore, weight can be a director on balancing the resource allocation in network. The direction of network may change over time. A 2-layer graph framework is proposed for different resource sharing. The first layer represents the CPU resource and memory resource which can be shared between different data centers. The second layer is related to bandwidth resource which can only be shared in the same data center. Thus, the connected edge may be different in different layers. CONCLUSION In this paper, we introduced a coalition game based model for resource management and sharing among the GMCC network. As the computing modules of mobile Internet applications can be offloaded to the powerful server in the cloud, the cloud service providers conform with a virtual resource network. It provides CPU, memory and bandwidth resource in order to support the mobile Internet applications. The coalition game in this GMCC network promotes resource cooperation either among the local SPs or remote SPs. It is a win-win strategy for SPs which can both improve the revenue by increasing the utility of resource appropriately, and largely enhance QoS by few cost for renting resource. REFERENCES:  T. Xing, D. Huang, S. Ata, and D. 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