research-article

SARDE: A Framework for Continuous and Self-Adaptive Resource Demand Estimation

Authors:

Johannes Grohmann,

Nikolas Herbst,

Samuel KounevAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 15, Issue 2

Article No.: 6, Pages 1 - 31

https://doi.org/10.1145/3463369

Published: 09 June 2021 Publication History

Abstract

Resource demands are crucial parameters for modeling and predicting the performance of software systems. Currently, resource demand estimators are usually executed once for system analysis. However, the monitored system, as well as the resource demand itself, are subject to constant change in runtime environments. These changes additionally impact the applicability, the required parametrization as well as the resulting accuracy of individual estimation approaches. Over time, this leads to invalid or outdated estimates, which in turn negatively influence the decision-making of adaptive systems. In this article, we present SARDE, a framework for self-adaptive resource demand estimation in continuous environments. SARDE dynamically and continuously tunes, selects, and executes an ensemble of resource demand estimation approaches to adapt to changes in the environment. This creates an autonomous and unsupervised ensemble estimation technique, providing reliable resource demand estimations in dynamic environments. We evaluate SARDE using two realistic datasets. One set of different micro-benchmarks reflecting different possible system states and one dataset consisting of a continuously running application in a changing environment. Our results show that by continuously applying online optimization, selection and estimation, SARDE is able to efficiently adapt to the online trace and reduce the model error using the resulting ensemble technique.

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Töpfer MAbdullah MBureš THnětynka PKruliš M(2023)Machine-learning abstractions for component-based self-optimizing systemsInternational Journal on Software Tools for Technology Transfer (STTT)10.1007/s10009-023-00726-x25:5-6(717-731)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10009-023-00726-x
Bożek ARak TRzonca D(2022)Timed Colored Petri Net-Based Event Generators for Web Systems SimulationApplied Sciences10.3390/app12231238512:23(12385)Online publication date: 3-Dec-2022
https://doi.org/10.3390/app122312385
Show More Cited By

Index Terms

SARDE: A Framework for Continuous and Self-Adaptive Resource Demand Estimation
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
  2. Modeling and simulation
    1. Model development and analysis
2. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software performance

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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 15, Issue 2

June 2020

91 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/3461693

Editor:
Valérie Issarny
Inria, France

Issue’s Table of Contents

Copyright © 2021 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 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 June 2021

Accepted: 01 April 2021

Revised: 01 February 2021

Received: 01 October 2020

Published in TAAS Volume 15, Issue 2

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Cited By

Kirschner YGstür MSağlam TWeber SKoziolek A(2025)Retriever: A view-based approach to reverse engineering software architecture modelsJournal of Systems and Software10.1016/j.jss.2024.112277220(112277)Online publication date: Feb-2025
https://doi.org/10.1016/j.jss.2024.112277
Töpfer MAbdullah MBureš THnětynka PKruliš M(2023)Machine-learning abstractions for component-based self-optimizing systemsInternational Journal on Software Tools for Technology Transfer (STTT)10.1007/s10009-023-00726-x25:5-6(717-731)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10009-023-00726-x
Bożek ARak TRzonca D(2022)Timed Colored Petri Net-Based Event Generators for Web Systems SimulationApplied Sciences10.3390/app12231238512:23(12385)Online publication date: 3-Dec-2022
https://doi.org/10.3390/app122312385
Martinez MPandey S(2022)Predictive function placement for distributed serverless environments2022 25th Conference on Innovation in Clouds, Internet and Networks (ICIN)10.1109/ICIN53892.2022.9758140(86-90)Online publication date: 7-Mar-2022
https://doi.org/10.1109/ICIN53892.2022.9758140
Abdullah MTöpfer MBureš THnětynka PKruliš MPlášil F(2022)Introducing Estimators—Abstraction for Easy ML Employment in Self-adaptive ArchitecturesSoftware Architecture. ECSA 2022 Tracks and Workshops10.1007/978-3-031-36889-9_25(370-385)Online publication date: 19-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-36889-9_25

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