A Robust Adaptive Workload Orchestration in Pure Edge Computing

Zahra Safavifar, Charafeddine Mechalikh, Fatemeh Golpayegani
8/15/2023
cs.DCcs.LG

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Offloading and Quality Control for AI Generated Content Services in 6G Mobile Edge Computing Networks

81% match2023

SafeTail: Efficient Tail Latency Optimization in Edge Service Scheduling via Computational Redundancy Management

81% match2024

Bringing AI to the edge: A formal M&S specification to deploy effective IoT architectures

80% match2023

Distributed Offloading in Multi-Access Edge Computing Systems: A Mean-Field Perspective

80% match2025

Multi-UAV Enabled MEC Networks: Optimizing Delay through Intelligent 3D Trajectory Planning and Resource Allocation

79% match2024

PPO-STGNN: A Proximal Policy Optimization Approach with Spatio-Temporal Graph Neural Networks for DAG Task Scheduling in Cloud-Edge-End Computing

78% match2026

Cite this paper

@article{safavifar2023robust,
  title  = {A Robust Adaptive Workload Orchestration in Pure Edge Computing},
  author = {Zahra Safavifar and Charafeddine Mechalikh and Fatemeh Golpayegani},
  year   = {2023},
  eprint = {2309.03913},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.03913v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2309.03913.svg)](https://codeofpaper.com/paper/2309.03913)

Discussion