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Optimal Computing Resource Management Based on Utility Maximization in Mobile Crowdsourcing

机译：基于移动众包的实用性最大化的最优计算资源管理

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摘要

Mobile crowdsourcing, as an emerging service paradigm, enables the computing resource requestor (CRR) to outsource computation tasks to each computing resource provider (CRP). Considering the importance of pricing as an essential incentive to coordinate the real-time interaction among the CRR and CRPs, in this paper, we propose an optimal real-time pricing strategy for computing resource management in mobile crowdsourcing. Firstly, we analytically model the CRR and CRPs behaviors in form of carefully selected utility and cost functions, based on concepts from microeconomics. Secondly, we propose a distributed algorithm through the exchange of control messages, which contain the information of computing resource demand/supply and real-time prices. We show that there exist real-time prices that can align individual optimality with systematic optimality. Finally, we also take account of the interaction among CRPs and formulate the computing resource management as a game with Nash equilibrium achievable via best response. Simulation results demonstrate that the proposed distributed algorithm can potentially benefit both the CRR and CRPs. The coordinator in mobile crowdsourcing can thus use the optimal real-time pricing strategy to manage computing resources towards the benefit of the overall system.
机译：移动众包作为一种新兴的服务模式，使计算资源请求者（CRR）能够将计算任务外包给每个计算资源提供者（CRP）。考虑到定价作为协调CRR和CRP之间实时交互的必要激励的重要性，本文提出了一种移动众包中计算资源管理的最优实时定价策略。首先，我们基于微观经济学的概念，以精心选择的效用函数和成本函数的形式对CRR和CRP行为进行分析建模。其次，我们提出了一种通过控制消息交换的分布式算法，其中包含计算资源需求/供应和实时价格的信息。我们证明了存在实时价格，可以将个体最优与系统最优结合起来。最后，我们还考虑了CRP之间的相互作用，将计算资源管理描述为一个博弈，通过最佳响应实现纳什均衡。仿真结果表明，所提出的分布式算法对CRR和CRP都有潜在的好处。因此，移动众包中的协调人可以使用最佳实时定价策略来管理计算资源，从而使整个系统受益。

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