FDN: Function Delivery Network - Optimizing service chain deployment in NFV

Hirwe, Anish and Kataoka, Kotaro (2020) FDN: Function Delivery Network - Optimizing service chain deployment in NFV. IEICE Transactions on Communications. ISSN 0916-8516

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Abstract

The static deployment of Virtualized Network Functions (VNFs) introduces 1) significant degradation of Quality of Service (QoS), 2) inefficiency in the network and computing resource utilization, and 3) Network Function Virtualization (NFV)-based services with insufficient scalability, optimality, and flexibility. Caching VNFs is a promising solution to satisfy the dynamic demand to deploy a variety of VNFs and to maximize the performance as well as cost effectiveness. Although the concept of Content Delivery Network (CDN) is popular for efficiently caching and distributing contents, VNF deployment does not realize the benefit of CDN-based caching approaches. The challenges to caching VNFs are 1) to cover the large variety of VNFs and their properties, including the necessity of service chaining, and 2) to achieve high acceptance ratio given the limited availability of resources. This paper proposes Function Delivery Network (FDN), which is a cluster of distributed edge hypervisors for caching VNFs over a Software-Defined Network (SDN). The deployment and quality of the network function can be significantly improved by serving them closer to the end-users from the cached VNFs. FDN introduces a new strategy called Value-based caching that considers 1) the locality of reference and performance parameters of network and edge hypervisors together and 2) a partial deployment of service chains across multiple edge hypervisors for further efficient utilization of hypervisors resources. Evaluations on different patterns of input requests confirm that Value-based caching introduces significant improvement on both QoS and resource utilization in NFV.

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IITH Creators:
IITH CreatorsORCiD
Kataoka, KotaroUNSPECIFIED
Item Type: Article
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 17 Jan 2020 04:46
Last Modified: 17 Jan 2020 04:46
URI: http://raiith.iith.ac.in/id/eprint/7344
Publisher URL: http://doi.org/10.1587/transcom.2019EBP3167
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