Service Function Chain Placement for Joint Cost and Latency Optimization

Mohammad Ali Khoshkholghi,Andreas Kassler,Zhengzhe Xiang,Xiaoxian Yang,Deval Bhamare,Javid Taheri,Michel Gokan Khan,Shuiguang Deng,Kyoomars Alizadeh Noghani

doi:10.1007/s11036-020-01661-w

Mohammad Ali Khoshkholghi, Andreas Kassler + Show 7 more

Open Access

https://doi.org/10.1007/s11036-020-01661-w

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Abstract

Network Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).

Highlights

Network Function Virtualization (NFV) is an innovational network architecture to provide network services by decoupling network functions such as firewalls, intrusion detection, load balancing and routing from physical boxes so that they can run as software-based applications
We propose a multi-objective optimization model formulated as Mixed Integer Programming (MIP), to jointly minimize the deployment cost and the end-to-end latency regarding a range of constraints such as routing, capacity, delay, location constraints
We proposed a multi-objective and comprehensive optimization model to joint minimizing deployment cost and end-toend latency considering a variety of parameters

Summary

Introduction

NFV is an innovational network architecture to provide network services by decoupling network functions such as firewalls, intrusion detection, load balancing and routing from physical boxes so that they can run as software-based applications It can improve flexibility and agility of the network since it is easier to dynamically scale the VNF instances, send the functions across a distributed infrastructure and upgrade the software without interrupting the service. Increasing the resource utilization in order to minimizing the number of active physical nodes can reduce deployment cost, but at the same time it may lead to an increase in latency of demanded services It can happen since the aggregation traffic on the physical nodes and links increases and it degrades the efficiency of latency objective. On the other side, minimizing network latency can be resulted in an increase in deployment and network cost because of more resources needed for providing services [3]

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