Optimal Pricing for Service Provision in an IaaS Cloud Market with Delay Sensitive Cloud Users

Fang, Gang; Li, Xianwei

doi:10.1109/iccnea.2017.112

Cited by 2 publications

(1 citation statement)

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“…A pricing mechanism for resource management in a public cloud is proposed in [25] with the goal to maximize the PSP's profit. In [26], Fang and Li studied how to design the optimal price schemes in a monopoly public cloud market where only one CSP exists. In [16], the authors developed an framework to set price of the mobile cloud services, and analyzed the incentives for offloading CSPs to offload the tasks of MUs.…”

Section: Related Workmentioning

confidence: 99%

Optimal Pricing and Service Selection in the Mobile Cloud Architectures

Zhang

et al. 2019

IEEE Access

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With offloading the tasks that mobile users (MUs) running in their mobile devices (MDs) to the data centers of remote public clouds, mobile cloud computing (MCC) can greatly improve the computing capacity and prolong the battery life of MDs. However, the data centers of remote public cloud are generally far from the MUs, thus long delay will be caused due to the transmission from the base station to the public clouds over the Internet. Mobile edge computing (MEC) is recognized as a promising technique to augment the computation capabilities of MDs and shorten the transmission delay. Nevertheless, compared with the traditional MCC and MEC generally has a limited number of cloud resources. Therefore, making a choice on offloading task to the MCC or MEC is a challenging issue for each MU. In this paper, we investigate service selection in a mobile cloud architecture, in which MUs select cloud services from two cloud service providers (CSPs), i.e., public cloud service provider (PSP) and an edge cloud service provider (ESP). We use M/M/∞ queue and M/M/1 queue to model PSP and ESP, respectively. We analyze the interaction of the two CSPs and MUs by adopting Stackelberg game, in which PSP and ESP set the prices first, and then the MUs decide to select cloud services based on performances and prices. In particular, we study the relationship between PSP and ESP in the simultaneous-play game (SPG) scenario, in which they compete to set prices of their cloud services simultaneously. Our numerical results show that MUs prefer to select service from the edge cloud if the number of tasks they run is small. In another hand, more tasks will be offloaded to the remote public cloud if the number of tasks they run becomes large. INDEX TERMS Pricing, mobile cloud computing, mobile edge computing.

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Section: Related Workmentioning

confidence: 99%