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Network Function Virtualization (NFV) emerges as a promising paradigm with the potential for cost-efficiency, manage-convenience, and flexibility, where the service function chain (SFC) deployment scheme is a crucial technology. In this paper, we propose an Ant Colony Optimization (ACO) meta-heuristic algorithm for the Online SFC Deployment, called ACO-OSD, with the objectives of jointly minimizing the server operation cost and network latency. As a meta-heuristic algorithm, ACO-OSD performs better than the state-of-art heuristic algorithms, specifically 42.88% lower total cost on average. To reduce the time cost of ACO-OSD, we design two acceleration mechanisms: the Next-Fit (NF) strategy and the many-to-one model between SFC deployment schemes and ant-tours. Besides, for the scenarios requiring real-time decisions, we propose a novel online learning framework based on the ACO-OSD algorithm, called prior-based learning real-time placement (PLRP). It realizes near real-time SFC deployment with the time complexity of O(n), where n is the total number of VNFs of all newly arrived SFCs. It meanwhile maintains a performance advantage with 36.53% lower average total cost than the state-of-art heuristic algorithms. Finally, we perform extensive simulations to demonstrate the outstanding performance of ACO-OSD and PLRP compared with the benchmarks. 

With the rapid advancement of edge computing and network function virtualization, it is promising to provide flexible and low-latency network services at the edge. However, due to the vulnerability of edge services and the volatility of edge computing system states, i.e., service request rates, failure rates, and resource prices, it is challenging to minimize the online service cost while providing the availability guarantee. This paper considers the problem of online virtual network function backup under availability constraints (OVBAC) for cost minimization in edge environments. We formulate the problem based on the characteristics of the volatility system states derived from real-world data and show the hardness of the formulated problem. We use an online backup deployment scheme named Drift-Plus-Penalty (DPP) with provable near-optimal performance for the AVBAC problem. In particular, DPP needs to solve an integer programming problem at the beginning of each time slot. We propose a dynamic programming-based algorithm that can optimally solve the problem in pseudo-polynomial time. Extensive real-world data-driven simulations demonstrate that DPP significantly outperforms popular baselines used in practice.