# Integrated Quality of Service for Offline and Online Services in Edge Networks via Task Offloading and Service Caching

## TL;DR

This paper introduces a new method to improve the performance of edge computing systems by optimizing both online and offline services using task offloading and caching.

## Contribution

The novel approach integrates QoS for online and offline services using a deep Q-network to optimize task offloading and caching.

## Key findings

- The proposed algorithm improved system utility by at least 14.01% compared to baseline methods.
- The problem was formulated as an NP-hard utility maximization task and reframed as a Markov decision process.

## Abstract

Edge servers frequently manage their own offline digital twin (DT) services, in addition to caching online digital twin services. However, current research often overlooks the impact of offline caching services on memory and computation resources, which can hinder the efficiency of online service task processing on edge servers. In this study, we concentrated on service caching and task offloading within a collaborative edge computing system by emphasizing the integrated quality of service (QoS) for both online and offline edge services. We considered the resource usage of both online and offline services, along with incoming online requests. To maximize the overall QoS utility, we established an optimization objective that rewards the throughput of online services while penalizing offline services that miss their soft deadlines. We formulated this as a utility maximization problem, which was proven to be NP-hard. To tackle this complexity, we reframed the optimization problem as a Markov decision process (MDP) and introduced a joint optimization algorithm for service caching and task offloading by leveraging the deep Q-network (DQN). Comprehensive experiments revealed that our algorithm enhanced the utility by at least 14.01% compared with the baseline algorithms.