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DMC: A Differential Marketplace for Cloud Resources

机译:DMC:云资源的差异市场

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The currently trending paradigms of edge and fog computing attempt to provide services close to the end user, to meet the demands of latency-sensitive applications and to limit bandwidth consumption in the network core. One open issue is the pricing of edge and fog resources. Current pricing schemes are usually oligopolistic and not fair. In this work, we propose DMC, a marketplace that can dynamically determine the fair price for arbitrary resource types and instances based on supply and demand existing at that period. Unlike the state-of-the-art solutions, DMC performs integral allocation of resources and thereby avoids the unbounded integrality gap. Additionally, DMC provides differential pricing among instances to allow varying prices based on the perceived value of a resource. We evaluate DMC with both heavy and non-heavy tailed distributions to reflect diverse buying interests and the number of resources sold to demonstrate the feasibility of our solution for several realistic scenarios. We observe that (i) DMC arrives at market-clearing prices; (ii) DMC generates 10x to 100x more profit than state-of-the-art solutions, while still maximizing the Nash Social Welfare to achieve prices that are fair to both buyers and the resource providers; and (iii) the computation time for DMC does not exceed 10 seconds even in the case of 500 resource types with 500 buyers each, making it applicable for real-time use cases.
机译:边缘和雾计算的当前趋势范例试图提供接​​近最终用户的服务,以满足对延迟敏感的应用程序的需求,并限制网络核心中的带宽消耗。一个未解决的问题是边缘和雾资源的定价。当前的定价方案通常是寡头的,而且不公平。在这项工作中,我们提出了DMC,它是一个可以根据当时存在的供求动态确定任意资源类型和实例的公平价格的市场。与最新的解决方案不同,DMC执行资源的整体分配,从而避免了无限的整体性差距。此外,DMC提供实例之间的差异定价,以允许基于资源的感知价值而变化价格。我们使用重尾分布和非重尾分布评估DMC,以反映不同的购买兴趣和出售的资源数量,以证明我们的解决方案在几种现实情况下的可行性。我们观察到:(i)DMC达到了市场清算价格; (ii)DMC产生的利润比最新解决方案高出10到100倍,同时仍使Nash社会福利最大化,以实现对购买者和资源提供者均公平的价格; (iii)DMC的计算时间不超过10秒,即使在500种资源类型(每种资源有500个购买者)的情况下,也适用于实时用例。

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