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MARC: A Resource Consumption Modeling Service for Self-Aware Autonomous Agents

Authors:

Matteo Ferroni,

Andrea Damiani,

Rolando Brondolin,

John D. Kubiatowicz,

Donatella Sciuto,

Marco D. SantambrogioAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 12, Issue 4

Article No.: 21, Pages 1 - 29

https://doi.org/10.1145/3127499

Published: 14 November 2017 Publication History

Abstract

Autonomicity is a golden feature when dealing with a high level of complexity. This complexity can be tackled partitioning huge systems in small autonomous modules, i.e., agents. Each agent then needs to be capable of extracting knowledge from its environment and to learn from it, in order to fulfill its goals: this could not be achieved without proper modeling techniques that allow each agent to gaze beyond its sensors. Unfortunately, the simplicity of agents and the complexity of modeling do not fit together, thus demanding for a third party to bridge the gap.

Given the opportunities in the field, the main contributions of this work are twofold: (1) we propose a general methodology to model resource consumption trends and (2) we implemented it into MARC, a Cloud-service platform that produces Models-as-a-Service, thus relieving self-aware agents from the burden of building their custom modeling framework. In order to validate the proposed methodology, we set up a custom simulator to generate a wide spectrum of controlled traces: this allowed us to verify the correctness of our framework from a general and comprehensive point of view.

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Cited By

Zafar I(2024)Towards Interference-Resilient Multi-Tenant Microservices via Spatio-Temporal Models of Self-Configuration2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)10.1109/ACSOS-C63493.2024.00054(173-175)Online publication date: 16-Sep-2024
https://doi.org/10.1109/ACSOS-C63493.2024.00054
Gheibi OWeyns DQuin F(2021)Applying Machine Learning in Self-adaptive SystemsACM Transactions on Autonomous and Adaptive Systems10.1145/346944015:3(1-37)Online publication date: 18-Aug-2021
https://doi.org/10.1145/3469440
Gheibi OWeyns DQuin F(2021)On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)10.1109/SEAMS51251.2021.00023(104-110)Online publication date: May-2021
https://doi.org/10.1109/SEAMS51251.2021.00023
Show More Cited By

Index Terms

MARC: A Resource Consumption Modeling Service for Self-Aware Autonomous Agents
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
  2. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Abstraction, modeling and modularity

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Information & Contributors

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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 12, Issue 4

December 2017

224 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/3155314

Editor:
Bashar Nuseibeh
The Open University, UK

Issue’s Table of Contents

Copyright © 2017 ACM.

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 ACM 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 November 2017

Accepted: 01 July 2017

Revised: 01 April 2017

Received: 01 September 2016

Published in TAAS Volume 12, Issue 4

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Cited By

Zafar I(2024)Towards Interference-Resilient Multi-Tenant Microservices via Spatio-Temporal Models of Self-Configuration2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)10.1109/ACSOS-C63493.2024.00054(173-175)Online publication date: 16-Sep-2024
https://doi.org/10.1109/ACSOS-C63493.2024.00054
Gheibi OWeyns DQuin F(2021)Applying Machine Learning in Self-adaptive SystemsACM Transactions on Autonomous and Adaptive Systems10.1145/346944015:3(1-37)Online publication date: 18-Aug-2021
https://doi.org/10.1145/3469440
Gheibi OWeyns DQuin F(2021)On the Impact of Applying Machine Learning in the Decision-Making of Self-Adaptive Systems2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)10.1109/SEAMS51251.2021.00023(104-110)Online publication date: May-2021
https://doi.org/10.1109/SEAMS51251.2021.00023
Brondolin RArnaboldi MSantambrogio M(2020)Power consumption management under a low-level performance constraint in the Xen hypervisorACM SIGBED Review10.1145/3412821.341282817:1(42-48)Online publication date: 27-Jul-2020
https://dl.acm.org/doi/10.1145/3412821.3412828
Brondolin RSardelli TSantambrogio M(2018)DEEP-Mon: Dynamic and Energy Efficient Power Monitoring for Container-Based Infrastructures2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW.2018.00110(676-684)Online publication date: May-2018
https://doi.org/10.1109/IPDPSW.2018.00110
Ferroni MCorna ADamiani ABrondolin RColmenares JHofmeyr SKubiatowicz JSantambrogio M(2017)Power Consumption Models for Multi-Tenant Server InfrastructuresACM Transactions on Architecture and Code Optimization10.1145/314896514:4(1-22)Online publication date: 14-Nov-2017
https://dl.acm.org/doi/10.1145/3148965

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