A Robust Adaptive Workload Orchestration in Pure Edge Computing

Zahra Safavifar, Charafeddine Mechalikh, Fatemeh Golpayegani

2023

Abstract

Pure Edge computing (PEC) aims to bring cloud applications and services to the edge of the network to support the growing user demand for time-sensitive applications and data-driven computing. However, mobility and limited computational capacity of edge devices pose challenges in supporting some urgent and computationally intensive tasks with strict response time demands. If the execution results of these tasks exceed the deadline, they become worthless and can cause severe safety issues. Therefore, it is essential to ensure that edge nodes complete as many latency-sensitive tasks as possible. In this paper, we propose a Robust Adaptive Workload Orchestration (R-AdWOrch) model to minimize deadline misses and data loss by using priority definition and a reallocation strategy. The results show that R-AdWOrch can minimize deadline misses of urgent tasks while minimizing the data loss of lower priority tasks under all conditions.

Paper Citation

in Harvard Style

Safavifar Z., Mechalikh C. and Golpayegani F. (2023). A Robust Adaptive Workload Orchestration in Pure Edge Computing. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-623-1, pages 325-333. DOI: 10.5220/0011782500003393

in Bibtex Style

@conference{icaart23,
author={Zahra Safavifar and Charafeddine Mechalikh and Fatemeh Golpayegani},
title={A Robust Adaptive Workload Orchestration in Pure Edge Computing},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2023},
pages={325-333},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011782500003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Robust Adaptive Workload Orchestration in Pure Edge Computing
SN - 978-989-758-623-1
AU - Safavifar Z.
AU - Mechalikh C.
AU - Golpayegani F.
PY - 2023
SP - 325
EP - 333
DO - 10.5220/0011782500003393