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AI-driven Workload Management in Meta OS

Published: 01 August 2024 Publication History

Abstract

Properly leveraging resources within a continuum, and in particular the satisfaction of user requirements, requires deep understanding of the workload itself and its interaction with the infrastructure in which it is executed. We present a novel approach for workload placement and execution within the cloud-to-edge continuum that leverages advanced AI capabilities. These capabilities allow the presented approach to optimize workload allocation, both in terms of resources used, and the ability to satisfy explicit and implicit user defined service level requirements. We tested the approach against real-world datasets, demonstrating the advantages over a baseline approach.

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cover image ACM Conferences
MECC '24: Proceedings of the 1st International Workshop on MetaOS for the Cloud-Edge-IoT Continuum
April 2024
53 pages
ISBN:9798400705434
DOI:10.1145/3642975
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 01 August 2024

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Author Tags

  1. AI-driven Workload Orchestration
  2. Cloud Continuum

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  • Research-article
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  • Refereed limited

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EuroSys '24
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MECC '24 Paper Acceptance Rate 7 of 15 submissions, 47%;
Overall Acceptance Rate 7 of 15 submissions, 47%

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EuroSys '25
Twentieth European Conference on Computer Systems
March 30 - April 3, 2025
Rotterdam , Netherlands

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