Truthful auction mechanisms for VNF chain provisioning and allocation across geo-distributed datacenters

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Vol. 217; p. 109331
• Main Authors: Wang, Xueyi, Wang, Xingwei, Wu, Dongkuo, Ma, Lianbo, Huang, Min
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 09.11.2022; Elsevier Sequoia S.A
• Subjects: Algorithms; Combinatorial analysis; Data centers; Dynamic provisioning; Flexible deployment; Incentives; Integer programming; Network function virtualization; Optimization; Polynomials; Provisioning; Randomized combinatorial auction; Truthfulness; Virtual networks; Flexible deployment; Network function virtualization; Randomized combinatorial auction; Truthfulness; Dynamic provisioning
• ISSN: 1389-1286; 1872-7069
• DOI: 10.1016/j.comnet.2022.109331

With the fast development of network function virtualization (NFV), NFV markets are emerging that allows network service providers (NSPs) to trade different virtual network function (VNF) chains among users. Therefore, efficient mechanisms for VNF chain provisioning and allocation are key to guarantee efficient operations of the NFV markets. One fundamental issue is how to maximize the social welfare of the market such that selfish users have incentives to acquire VNF chains from the markets. To overcome this issue, in this paper, we propose a truthful randomized combinatorial auction mechanism (TRCAM), which integrates the primal–dual approximation algorithm design technique and efficient pricing strategy. This mechanism considers dynamic VNF chain provisioning of the NSP and flexible VNF chain deployment of users. To be specific, we first formulate the social welfare maximization problem as an integer linear program, which is proven to be NP-hard. Then, we propose a primal–dual approximate algorithm which is near-optimal with polynomial-time complexity and employ it as a building block to design the TRCAM. Furthermore, we develop a novel binary-search-based algorithm to boost the average-case performance of the TRCAM. Both strict theoretical analysis and extensive experimental studies demonstrate the effectiveness of our proposed TRCAM, which achieves approximate truthfulness, near-optimal social welfare, individual rationality, and computational efficiency.