Placing Timely Refreshing Services at the Network Edge

TL;DR

This paper addresses the challenge of placing timely refreshing services at the network edge to minimize backhaul costs, proposing a novel approximation method validated through experiments and simulations.

Contribution

It introduces a new problem formulation for edge service placement considering refreshing costs and proposes a lightweight approximation algorithm with proven bounds.

Findings

DVA reduces transmission costs by up to 59.1%

The problem is NP-hard and highly coupled spatially and temporally

The proposed method outperforms state-of-the-art baselines

Abstract

Accommodating services at the network edge is favorable for time-sensitive applications. However, maintaining service usability is resource-consuming in terms of pulling service images to the edge, synchronizing databases of service containers, and hot updates of service modules. Accordingly, it is critical to determine which service to place based on the received user requests and service refreshing (maintaining) cost, which is usually neglected in existing studies. In this work, we study how to cooperatively place timely refreshing services and offload user requests among edge servers to minimize the backhaul transmission costs. We formulate an integer non-linear programming problem and prove its NP-hardness. This problem is highly non-tractable due to the complex spatial-and-temporal coupling effect among service placement, offloading, and refreshing costs. We first decouple the problem in the temporal domain by transforming it into a Markov shortest-path problem. We then propose a light-weighted Discounted Value Approximation (DVA) method, which further decouples the problem in the spatial domain by estimating the offloading costs among edge servers. The worst performance of DVA is proved to be bounded. 5G service placement testbed experiments and real-trace simulations show that DVA reduces the total transmission cost by up to 59.1% compared with the state-of-the-art baselines.