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Optimal Service Provisioning for the Scalable Fog/Edge Computing Environment

机译:可扩展雾/边缘计算环境的最佳服务配置

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摘要

In recent years, we observed the proliferation of cloud data centers (CDCs) and the Internet of Things (IoT). Cloud computing based on CDCs has the drawback of unpredictable response times due to variant delays between service requestors (IoT devices and end devices) and CDCs. This deficiency of cloud computing is especially problematic in providing IoT services with strict timing requirements and as a result, gives birth to fog/edge computing (FEC) whose responsiveness is achieved by placing service images near service requestors. In FEC, the computing nodes located close to service requestors are called fog/edge nodes (FENs). In addition, for an FEN to execute a specific service, it has to be provisioned with the corresponding service image. Most of the previous work on the service provisioning in the FEC environment deals with determining an appropriate FEN satisfying the requirements like delay, CPU and storage from the perspective of one or more service requests. In this paper, we determined how to optimally place service images in consideration of the pre-obtained service demands which may be collected during the prior time interval. The proposed FEC environment is scalable in the sense that the resources of FENs are effectively utilized thanks to the optimal provisioning of services on FENs. We propose two approaches to provision service images on FENs. In order to validate the performance of the proposed mechanisms, intensive simulations were carried out for various service demand scenarios.
机译:近年来,我们观察到云数据中心(CDC)的扩散和物联网(物联网)。基于CDC的云计算具有由于服务请求者(物联网设备和最终设备)和CDC之间的变体延迟而导致的不可预测的响应时间的缺点。这种云计算缺乏在提供具有严格的时序要求的情况下提供物联网服务尤其有问题,因此提供了通过在服务请求者附近放置服务图像来实现响应性的雾/边缘计算(FEC)。在FEC中,靠近服务请求者的计算节点称为FOG / EDGE节点(FINS)。另外,对于执行特定服务的FEN,必须使用相应的服务图像进行配置。 FEC环境中的服务供应中的大多数工作涉及从一个或多个服务请求的角度来确定满足延迟,CPU和存储等要求的适当的芬文。在本文中,我们确定了如何考虑到在先前时间间隔期间可以收集的预先获得的服务需求来最佳地放置服务图像。拟议的FEC环境是可扩展的,因为由于Fens上的服务提供了优化的服务,因此有效地利用了Fens的资源。我们提出了两种方法,可以在FUNS上提供服务图像。为了验证拟议机制的性能,为各种服务需求方案进行了密集的模拟。

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