Service Chain Composition with Failures in NFV Systems: A Game-Theoretic Perspective

TL;DR

This paper models service chain composition in NFV systems with failures as a non-cooperative game, proposing distributed and learning-based schemes to achieve near-optimal latency and congestion minimization.

Contribution

It introduces a game-theoretic framework for NFV service chain composition considering failures and develops distributed and learning-based algorithms to reach Nash equilibrium.

Findings

Proposed distributed schemes effectively reach Nash equilibrium with low latency.

Learning-based schemes adapt well to failures and maintain performance.

Simulation results confirm near-optimal latency and congestion reduction.

Abstract

For state-of-the-art network function virtualization (NFV) systems, it remains a key challenge to conduct effective service chain composition for different network services (NSs) with ultra-low request latencies and minimum network congestion. To this end, existing solutions often require full knowledge of the network state, while ignoring the privacy issues and overlooking the non-cooperative behaviors of users. What is more, they may fall short in the face of unexpected failures such as user unavailability and virtual machine breakdown. In this paper, we formulate the problem of service chain composition in NFV systems with failures as a non-cooperative game. By showing that such a game is a weighted potential game and exploiting the unique problem structure, we propose two effective distributed schemes that guide the service chain compositions of different NSs towards the Nash equilibrium (NE) state with both near-optimal latencies and minimum congestion. Besides, we develop two novel learning-aided schemes as comparisons, which are based on deep reinforcement learning (DRL) and Monte Carlo tree search (MCTS) techniques, respectively. Our theoretical analysis and simulation results demonstrate the effectiveness of our proposed schemes, as well as the adaptivity when faced with failures.