A game-based virtual machine pricing mechanism in federated clouds

Ying Hu
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引用次数: 3

Abstract

In a federated cloud environment, diverse pricing schemes among different IaaS service providers (ISPs) form a complex economic landscape that nurtures the market of cloud brokers. Although pricing mechanisms have been proposed in the past few years, few of them address the issue of competitive and cooperative behaviours among different ISPs. In this paper, we employ the learning curve to model the operation cost of ISPs, and introduce a novel algorithm that determines the cooperative pricing mechanism among different ISPs. The cooperation decision algorithm uses the operation cost computed based on the learning curve model and price policies obtained from the competition part as parameters to calculate the final revenue when outsourcing or locally satisfying users' resource requests. Extensive experiments are conducted in a real-world federated cloud platform, and the experimental results are compared with three existing pricing mechanisms. Our experimental results show that the proposed pricing mechanism is effective to improve resource utilisation as well as reduce the profit loss caused by request rejection.
联邦云中基于游戏的虚拟机定价机制
在联合云环境中,不同IaaS服务提供商(isp)之间的不同定价方案形成了一个复杂的经济格局,从而培育了云代理市场。虽然在过去几年中提出了定价机制,但其中很少涉及不同互联网服务提供商之间的竞争和合作行为问题。本文采用学习曲线对网络服务提供商的运营成本进行建模,并引入一种新的算法来确定不同网络服务提供商之间的合作定价机制。合作决策算法以基于学习曲线模型计算的运营成本和从竞争方获得的价格策略为参数,计算外包或本地满足用户资源需求时的最终收益。在现实世界的联邦云平台上进行了大量的实验,并将实验结果与现有的三种定价机制进行了比较。实验结果表明,所提出的定价机制能够有效地提高资源利用率,减少因拒绝请求而造成的利润损失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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