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The Value of Cooperation: Minimizing User Costs in Multi-Broker Mobile Cloud Computing Networks

机译:合作的价值:在多代理移动云计算网络中最大程度地降低用户成本

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We study the problem of user cost minimization in mobile cloud computing (MCC) networks. We consider a MCC model where multiple brokers assign cloud resources to mobile users. The model is characterized by an heterogeneous cloud architecture (which includes a public cloud and a cloudlet) and by the heterogeneous pricing strategies of cloud service providers. In this setting, we investigate two classes of cloud reservation strategies, i.e., a competitive strategy, and a compete-then-cooperate strategy as a performance bound. We first study a purely competitive scenario where brokers compete to reserve computing resources from remote public clouds (which are affected by long delays) and from local cloudlets (which have limited computational resources but short delays). We provide theoretical results demonstrating the existence of disagreement points (i.e., the equilibrium reservation strategy that no broker has incentive to deviate unilaterally from) and convergence of the best-response strategies of the brokers to disagreement points. We then consider the scenario in which brokers agree to cooperate in exchange for a lower average cost of resources. We formulate a cooperative problem where the objective is to minimize the total average price of all brokers, under the constraint that no broker should pay a price higher than the disagreement price (i.e., the competitive price). We design new globally optimal solution algorithm to solve the resulting non-convex cooperative problem, based on a combination of the branch and bound framework and of advanced convex relaxation techniques. The resulting optimal solution provides a lower bound on the achievable user cost without complete collusion among brokers. Compared with pure competition, we found that (i) noticeable cooperative gains can be achieved over pure competition in markets with a few brokers only, and (ii) the cooperative gain is only marginal in crowded markets, i.e., with a high number of brokers, hence there is no clear incentive for brokers to cooperate.
机译:我们研究了移动云计算(MCC)网络中的用户成本最小化问题。我们考虑一个MCC模型,其中多个代理将云资源分配给移动用户。该模型的特征在于异构云体系结构(包括公共云和cloudlet)以及云服务提供商的异构定价策略。在这种情况下,我们研究了两类云预留策略,即竞争策略和竞争然后合作策略作为性能约束。我们首先研究一种纯粹竞争的情况,其中经纪人竞争从远程公共云(受长时间延迟影响)和本地小云(计算资源有限但延迟短)中保留计算资源。我们提供的理论结果证明了分歧点的存在(即没有经纪人有动机单方面背离的均衡保留策略)以及经纪人对分歧点的最佳响应策略的收敛。然后,我们考虑以下场景:经纪人同意合作以换取较低的平均资源成本。我们制定了一个合作问题,其目标是在不让经纪人支付高于分歧价格(即竞争价格)的价格的约束下,将所有经纪人的总平均价格降至最低。我们基于分支和边界框架以及先进的凸松弛技术的组合,设计了新的全局最优解算法来解决由此产生的非凸合作问题。最终的最佳解决方案提供了可实现的用户成本的下限,而经纪人之间却没有完全勾结。与纯竞争相比,我们发现(i)在只有几个经纪人的市场中,纯竞争可以获得明显的合作收益;(ii)在拥挤的市场中,即在经纪人众多的情况下,合作收益仅是微不足道的。 ,因此没有明显的动机来鼓励经纪人合作。

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