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

arXiv: https://arxiv.org/abs/1903.04709 · HTML: https://mlsanity.com/paper/1903.04709/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [Learning the Optimal Path and DNN Partition for Collaborative Edge Inference](https://mlsanity.com/paper/2410.01857/) (2024)

## Abstract (arXiv)

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.