基因演算法用於邊緣運算之服務功能部署;Genetic-Algorithm-Based Service Function Deployment for Edge Computing

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Title:	基因演算法用於邊緣運算之服務功能部署;Genetic-Algorithm-Based Service Function Deployment for Edge Computing
Authors:	陳柏琿;Chen, Bo-Hun
Contributors:	資訊工程學系
Keywords:	邊緣運算;網路功能虛擬化;網路延遲;服務功能部署;基因演算法;Edge Computing;Service Function;low-latency;service function deployment;genetic algorithm
Date:	2018-08-22
Issue Date:	2018-08-31 14:56:02 (UTC+8)
Publisher:	國立中央大學
Abstract:	近幾年來，行動寬頻的網路傳輸量不斷地上升，大部分的流量主要都是來自於高互動的應用服務，如虛擬實境、工業物聯網及機器類型通訊(Machine-Type Communication)，低延遲的需求將被視為第五代網路通訊標準制定的首要關鍵任務。為了滿足低延遲的需求，行動邊緣運算的概念被提了出來，將雲端運算的計算資源下放至邊緣網路，以及透過虛擬化的技術，讓服務提供商租用計算資源，或者網路營運商部署其虛擬網路功能(VNF)至邊緣網路，降低網路的延遲，然而如何適當地將服務功能部署到邊緣網路將會是問題，部署的結果將會影響到整體使用者的效能。本論文所提出的GASDE是一種高效能的部署策略，用於邊緣網路環境中的服務功能部署。GASDE使用了基因演算法，在考慮多個租戶租用邊緣運算的計算資源之情況下，降低用戶端存取服務的平均網路延遲，並且在部署決策時考慮了服務功能部署的成本。GASDE部署策略不僅能用在純邊緣運算情境的部署，還能用在同時考慮邊緣運算以及雲端運算情境的部署。模擬結果顯示，與其他2種部署策略 : GRE以及DCB相比，無論是在純邊緣運算的情境或是同時考慮邊緣運算以及雲端運算的情境，在網路延遲和服務功能部署成本的表現上，均表現出較佳的效能。此外，本論文還在XenServer中設計並實作了一個服務功能邊緣平台，驗證邊緣運算對於網路延遲的重要性，以及本論文所提出的演算法之可應用性。 ;In recent years, the mobile data traffic has a tremendous growth, especially most of these traffic originate from highly interactive applications such as virtual reality, Internet of Things (IoT) and Machine-Type Communication(MTC). The demand for low-latency communications has been considered as one of critical issue for fifth-generation standardization. In order to satisfy the demand of low-latency, the concept of mobile edge computing is recently emerged by placing computation resource to the edge network. With the technology of virtualization, service providers can rent computation resource from the infrastructure of network operator, and network operators also can deploy service functions(SFs) to the edge network to reduce the network latency. However, how to appropriately deploy these service functions into edge network will be a problem. We propose GASDE, a high-performance approach for deploying service functions into the edge network. GASDE uses genetic-algorithm(GA) to reduce network delay and cost of deployment, which considers the situation multi-tenancy would deploy their service functions into edge network. The result of simulation shows that when compared with other two strategies: GRE and DCB has the better performance of network delay and cost of deployment no matter in considering the case of only edge computing or cloud edge computing. We also implement a service function edge platform in XenServer to verify our works are more comprehensive and realistic.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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