Community Clinic: Economizing Mobile Cloud Service Cost via Cloudlet Group

Abstract

The explosive growth of mobile applications causes the mobile traffic to easily exceed the capacity of the cloud service due to the bandwidth limits of last mile connections to the cloud and legacy backhauls to macrocells' base stations. It degrades mobile applications' quality of service since the mobile devices have to spend more time and thus consume more battery power for data transmissions. It also enforces the cloud provider to put a huge investment to update its infrastructure and the induced cost is inevitably borne by all mobile users. To resolve this issue, in this paper we propose a so-called community clinic solution, which embeds the cloudlet group between the cloud and mobile users, to cut down the cost introduced by the massive deployment of the cloud's data centers and save the battery power consumed by the mobile devices. We firstly show that the mobile devices can consume less energy by choosing the service provided by the cloudlet group. We then model the system with and without the cloudlet group as two types of supply chain and prove that the cloudlet group can increase the cloud's profit without putting additional cost on mobile users. We also propose the real-time group-buying auction for the cloudlet group to promote its service to its nearby mobile users with a lower price and maximize its profit. The community clinic can result in a win-winwin outcome among the cloud, cloudlet group and mobile users. Numerical experiments are further conducted to demonstrate the effectiveness of our scheme.

Can cloudlet coordination support cloud computing infrastructure?

“…These topics were applicable to mobile computing, semantic callbacks, caching metrics, validators, analysis of adaptation, resource revocation, and global estimation from local observations. Moreover, a community clinic solution integrated in the cloudlet group between the mobile users and the cloud was proposed by Zhang et al [18]. The high cost introduced by massive deployment of cloud data centers was reduced with the aid of this proposed system.…”

Modeling Dynamic Resource Allocation in the Edge

“…Edge computing architectures [4], and similar integrated wired/wireless architectures, such as fog computing or cloudlet-based architectures [5], have been proposed to accommodate the emergence of cloud-based services, (multimedia) The basic structure of edge computing infrastructures has three tiers [2]: end device, local cloud (cloudlet) or microdatacenter, and public cloud. Based on this hierarchy, different interactions have been explored [2], [6]- [9].…”

“…The resulting traffic growth, and its increasing variability, put under pressure current networking infrastructures, as noticed by network operators [1], both in the Internet core, in the wireless last mile, or in the access network backhauls [2]. This has encouraged the emergence of edge or fog computing architectures [3], [4], and various integrated infrastructures.…”

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

“…1) Double auction: To motivate resource pooling, cloudlets can form cloudlet groups as illustrated in Fig. 14. The authors in [207] adopted the real-time group-buying auction for the cloudlet group to offer its services, i.e., mobile videos, to nearby mobile users with lower prices while maximizing the profit of the group. The group-buying auction is a type of double auction in which buyers get more discounts from sellers if more buyers participate [208].…”

“…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.…”