SAP: Subchain-Aware NFV Service Placement in Mobile Edge Cloud

作     者：Doan, Tung V. V. Nguyen, Giang T. T. Reisslein, Martin H. P. Fitzek, Frank H. P. 

作者机构：Tech Univ Dresden Deutsch Telekom Chair Commun Networks D-01187 Dresden Germany Tech Univ Dresden Ctr Tactile Internet Human In The Loop D-01187 Dresden Germany Tech Univ Dresden Chair Hapt Commun Syst D-01187 Dresden Germany Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA 

出 版 物：《IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT》 (IEEE Trans. Netw. Serv. Manage.)

年 卷 期：2023年第20卷第1期

页      面：319-341页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) Germany's Excellence Strategy [EXC 2050/1, ID 390696704] 

主　　题：Noise measurement Costs Reliability Software reliability Cloud computing Network function virtualization Hardware Configuration cost multi-access edge computing (MEC) network function placement network function virtualization (NFV) reuse service function chain (SFC) 

摘      要：Existing Network Function Virtualization (NFV) service placements that reuse already deployed network functions either reuse an entire Service Function Chain (SFC) or only individual network functions while ignoring the chain configuration cost for configuring the SFC traffic steering and ignoring the reliability of the network functions. Also, the Mobile Edge Cloud (MEC) frameworks that are required to implement an NFV service placement should ideally seamlessly cooperate with the various existing NFV Management and Orchestration (MANO) frameworks. However, the existing MEC frameworks lack multi-MANO support. We formulate the novel Subchain-Aware NFV service Placement (SAP) optimization model that accounts for the configuration cost for stitching together reused network functions to an SFC and strives to reuse existing subchains of consecutive network functions (with already deployed SFC traffic steering), while accounting for the recovery cost of network functions with limited reliability. We develop Tabu-SAP, a Tabu search approach to solve the SAP optimization problem. Furthermore, we introduce the novel Automated Provisioning framework for MEC (APMEC) with open-source OpenStack implementation to enable the deployment of Tabu-SAP in real networks;APMEC supports multiple MANOs through a loose coupling MANO-MEC design. Our Tabu-SAP evaluations indicate an around eightfold increase of the number of supported SFCs compared to the state-of-the-art reuse of individual network functions, while substantially reducing the total cost, which includes the chain configuration cost. Also, for long SFCs of seven or more network functions, the Tabu-SAP total cost is less than 10% higher than the optimal solution (which requires over ten times longer execution time).