# Service Capacity Enhanced Task Offloading and Resource Allocation in Multi-Server Edge Computing Environment

## TL;DR

This paper proposes an online resource allocation algorithm for multi-server edge computing that optimizes service latency, power consumption, and capacity, demonstrating significant performance improvements over baseline methods.

## Contribution

It introduces a novel stochastic optimization framework with an online Lyapunov-based algorithm for resource allocation in multi-server edge computing environments.

## Key findings

- Significant reduction in service latency and power consumption.
- Increased number of offloaded computation tasks.
- Outperforms baseline approaches in experiments.

## Abstract

An edge computing environment features multiple edge servers and multiple service clients. In this environment, mobile service providers can offload client-side computation tasks from service clients' devices onto edge servers to reduce service latency and power consumption experienced by the clients. A critical issue that has yet to be properly addressed is how to allocate edge computing resources to achieve two optimization objectives: 1) minimize the service cost measured by the service latency and the power consumption experienced by service clients; and 2) maximize the service capacity measured by the number of service clients that can offload their computation tasks in the long term. This paper formulates this long-term problem as a stochastic optimization problem and solves it with an online algorithm based on Lyapunov optimization. This NP-hard problem is decomposed into three sub-problems, which are then solved with a suite of techniques. The experimental results show that our approach significantly outperforms two baseline approaches.