Auction Mechanisms Toward Efficient Resource Sharing for Cloudlets in Mobile Cloud Computing

Abstract

Mobile cloud computing offers an appealing paradigm to relieve the pressure of soaring data demands and augment energy efficiency for future green networks. Cloudlets can provide available resources to nearby mobile devices with lower access overhead and energy consumption. To stimulate service provisioning by cloudlets and improve resource utilization, a feasible and efficient incentive mechanism is required to charge mobile users and reward cloudlets. Although auction has been considered as a promising form for incentive, it is challenging to design an auction mechanism that holds certain desirable properties for the cloudlet scenario. Truthfulness and system efficiency are two crucial properties in addition to computational efficiency, individual rationality and budget balance. In this paper, we first propose a feasible and truthful incentive mechanism (TIM), to coordinate the resource auction between mobile devices as service users (buyers) and cloudlets as service providers (sellers). Further, TIM is extended to a more efficient design of auction (EDA). TIM guarantees strong truthfulness for both buyers and sellers, while EDA achieves a fairly high system efficiency but only satisfies strong truthfulness for sellers. We also show the difficulties for the buyers to manipulate the resource auction in EDA and the high expected utility with truthful bidding.

Mobile Edge Intelligence and Computing for the Internet of Vehicles

“…2) Incentive Mechanisms: To utilize onboard computation resources of intelligent vehicles to assist passenger devices or other vehicles, effective incentive mechanisms are needed. There have been many studies in incentivizing players to share resources in other domains [117], and recently extensions to intelligent vehicles have been investigated.…”

Resource Management in Cloud Networking Using Economic Analysis and Pricing Models: A Survey

“…However, the system efficiency in terms of the number of final matchings between winning buyers and winning sellers only achieves around 50% of that of the optimal strategy. Using the same model as in [209], the authors in [210] considered the randomness and the uncertainty in the auction to improve the system efficiency. Specifically, the auctioneer sorts sellers randomly as a list.…”

“…Mobile data offloading service Three contract theoretic models for the service trading are used, that are perfect discrimination, linear pricing, and anti adverse selection. In particular for the anti adverse selection, the seller determines the optimal prices and the amounts of bandwidth using the Lagrange multiplier method [285] • Overcome the information asymmetry • Support multiple traffic-payment bundles • Support only one SDN controller [287] • Adapt to both real-time and non-real-time service requests • Be resilient to demand fluctuations • Support only one service provider 2) Collusion in auction: Apart from the false-name bidding cheating, bidders in the reviewed approaches based on auction, i.e., the VCG auction [124], [224], the combinatorial auction [192], and the double auction [190], [207], [209], [210], [229], may collude with each other through coordinating their bids. This suppresses the competition for cloud resource, thus reducing the price that the bidders must pay for the cloud resource.…”

QUICK: QoS-guaranteed efficient cloudlet placement in wireless metropolitan area networks

“…Therefore, if the current cloudlet can serve neighbouring user requests close to the AP it attaches to, user access delay can be reduced; otherwise, the user requests at that AP must be relayed to nearby cloudlets. We assume, all nodes stay staic during the allocation period [19] and the cloudlets can be initially deployed based on the statistical history data. To deal the original resquest at v i can be redirected to other APs.…”