Online Learning for Failure-aware Edge Backup of Service Function Chains with the Minimum Latency

TL;DR

This paper introduces an online learning-based method for deploying SFC backups at the edge to minimize latency, considering popularity and failure rates, and overcoming uncertainty with bandit algorithms.

Contribution

It proposes a novel RTSD algorithm combining bandit learning with Prim-inspired and greedy strategies for failure-aware SFC backup placement.

Findings

RTSD outperforms baseline methods in reducing latency.

The approach effectively handles unknown SFC popularity and failure rates.

Simulation results validate the algorithm's efficiency and adaptability.

Abstract

Virtual network functions (VNFs) have been widely deployed in mobile edge computing (MEC) to flexibly and efficiently serve end users running resource-intensive applications, which can be further serialized to form service function chains (SFCs), providing customized networking services. To ensure the availability of SFCs, it turns out to be effective to place redundant SFC backups at the edge for quickly recovering from any failures. The existing research largely overlooks the influences of SFC popularity, backup completeness and failure rate on the optimal deployment of SFC backups on edge servers. In this paper, we comprehensively consider from the perspectives of both the end users and edge system to backup SFCs for providing popular services with the lowest latency. To overcome the challenges resulted from unknown SFC popularity and failure rate, as well as the known system parameter constraints, we take advantage of the online bandit learning technique to cope with the uncertainty issue. Combining the Prim-inspired method with the greedy strategy, we propose a Real-Time Selection and Deployment(RTSD) algorithm. Extensive simulation experiments are conducted to demonstrate the superiority of our proposed algorithms.