A Novel Strategy for VNF Placement in Edge Computing Environments

Abstract

Network function virtualization (NFV) is a novel technology that virtualizes computing, network, and storage resources to decouple the network functions from the underlying hardware, thus allowing the software implementation of such functions to run on commodity hardware. By doing this, NFV provides the necessary flexibility to enable agile, cost-effective, and on-demand service delivery models combined with automated management. Different management and orchestration challenges arise in such virtualized and distributed environments. A major challenge in the selection of the most suitable edge nodes is that of deploying virtual network functions (VNFs) to meet requests from multiple users. This article addresses the VNF placement problem by providing a novel integer linear programming (ILP) optimization model and a novel VNF placement algorithm. In our definition, the multi-objective optimization problem aims to (i) minimize the energy consumption in the edge nodes; (ii) minimize the total latency; and (iii) reducing the total cost of the infrastructure. Our new solution formulates the VNF placement problem by taking these three objectives into account simultaneously. In addition, the novel VNF placement algorithm leverages VNF sharing, which reuses VNF instances already placed to potentially reduce computational resource usage. Such a feature is still little explored in the community. Through simulation, numerical results show that our approach can perform better than other approaches found in the literature regarding resource consumption and the number of SFC requests met.

SP-ADMM: a distributed optimization method of SFC placement for 5G-MEC networks

“…In addition, we set up the widely used greedy algorithm (Greedy) [11], genetic algorithm (GA) [22] and constructed the greedy-based genetic algorithm (GBGA) as baselines. In GBGA, GA is used to solve the instantiation scheme of SFs and GA is used to solve the SFC placement problem.…”

“…Literature [10] explored the deployment and resource allocation problem of VNFs in NFV/SDN-enabled MEC networks to reduce the overall deployment and resource costs. Literature [11] jointly considered the energy, cost and delay issues; modeled the problem as an ILP problem; and proposed a heuristic SFC-optimized deployment strategy for VNF sharing. Several studies have focused on improving system reliability and robustness.…”

An Online Ensemble Method for Auto-scaling NFV-based Applications in the Edge

“…While various algorithms address VNF placement in edge computing contexts [80][81][82], it's important to note that this work does not introduce or discuss a new placement algorithm. Instead, for the evaluations, we utilized a placement algorithm proposed in [83] to create a placement plan that ensures edge nodes have sufficient resources to run the VNF instances and meet the minimum QoS requirement of VNFs. After establishing the placement plan and completing the placement process, packets are generated according to a specified workload pattern and forwarded to the respective SFC (ordered chain of VNFs representing a service).…”