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Dynamic Optimal Pricing for Heterogeneous Service-Oriented Architecture of Sensor-Cloud Infrastructure

机译:传感器-云基础架构的异构服务导向架构的动态最优定价

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摘要

This paper proposes a dynamic and optimal pricing scheme for provisioning Sensors-as-a-Service (Se-aaS) [1] within the sensor-cloud infrastructure. Existing cloud pricing models are limited in terms of the homogeneity in service-types, and hence, are not compliant for the heterogeneous service oriented architecture of Se-aaS. We propose a new pricing model comprising of two components, applicable for Se-aaS architecture: pricing attributed to Hardware (pH) and pricing attributed to Infrastructure (pI ). pH addresses the problem of pricing the physical sensor nodes subject to variable demand and utility of the end-users. It maximizes the profit incurred by every sensor owner, while keeping in mind the end-users’ utility. pI mainly focuses on the pricing incurred due to the virtualization of resources. It takes into account the cost for the usage of the infrastructural resources, inclusive of the cost for maintaining virtualization within sensor-cloud. pI maximizes the profit of the sensor-cloud service provider (SCSP) by considering the user satisfaction. Simulation results depict improved performance of pH in comparison to the traditional hardware pricing algorithms, viz. PPM and Sprite, in terms of the residual energy, proximity to the base station (BS), received signal strength (RSS), overhead, and cumulative energy consumption. The results also show the tendency of the sensor-owners to converge to the end-user utility, but not exceed it. We also analyze the performance of pI. The results show the optimality in the profit incurred by SCSP and the user satisfaction.
机译:本文提出了一种动态且最优的定价方案,用于在传感器云基础架构中预配传感器即服务(Se-aaS)[1]。现有的云定价模型在服务类型的同质性方面受到限制,因此不符合Se-aaS面向异构服务的体系结构。我们提出了一种新的定价模型,该模型包括两个部分,适用于Se-aaS体系结构:归因于硬件(pH)的定价和归因于基础架构(pI)的定价。 pH解决了根据最终用户的可变需求和效用为物理传感器节点定价的问题。它最大程度地提高了每个传感器所有者的利润,同时牢记最终用户的实用程序。 pI主要关注由于资源虚拟化而产生的定价。它考虑了基础设施资源的使用成本,包括维护传感器云内虚拟化的成本。 pI通过考虑用户满意度来最大化传感器云服务提供商(SCSP)的利润。仿真结果显示,与传统的硬件定价算法相比,pH的性能有所提高。 PPM和Sprite就剩余能量,与基站(BS)的接近程度,接收信号强度(RSS),开销和累积能量消耗而言。结果还显示了传感器所有者趋向于最终用户实用程序的趋势,但并没有超过这一趋势。我们还分析了pI的性能。结果表明,SCSP带来的利润和用户满意度都是最优的。

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