https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Makespan reduction for dynamic workloads in cluster-based data grids using reinforcement-learning based scheduling
University of Kashan, Kashan, Iran.ORCID iD: 0000-0003-3354-1463
University of Kashan, Kashan, Iran.
2018 (English)In: Journal of Computational Science, ISSN 1877-7503, E-ISSN 1877-7511, Vol. 24, p. 402-412Article in journal (Refereed) Published
Abstract [en]

Scheduling is one of the important problems within the scope of control and management in grid and cloud-based systems. Data grid still as a primary solution to process data-intensive tasks, deals with managing large amounts of distributed data in multiple nodes. In this paper, a two-phase learning-based scheduling algorithm is proposed for data-intensive tasks scheduling in cluster-based data grids. In the proposed scheduling algorithm, a hierarchical multi agent system, consisting of one global broker agent and several local agents, is applied to scheduling procedure in the cluster-based data grids. At the first step of the proposed scheduling algorithm, the global broker agent selects the cluster with the minimum data cost based on the data communication cost measure, then an adaptive policy based on Q-learning is used by the local agent of the selected cluster to schedule the task to the proper node of the cluster. The impacts of three action selection strategies have been investigated in the proposed scheduling algorithm, and the performance of different versions of the scheduling algorithm regarding different action selection strategies, has been evaluated under three types of workloads with heterogeneous tasks. Experimental results show that for dynamic workloads with varying task submission patterns, the proposed learning-based scheduling algorithm gives better performance compared to four common scheduling algorithm, Queue Length (Shortest Queue), Access Cost, Queue Access Cost (QAC) and HCS, which use regular combinations of primary parameters such as, data communication cost and queue length. Applying a learning-based strategy provides the scheduling algorithm with more adaptability to the changing conditions in the environment.

Place, publisher, year, edition, pages
Netherlands: Elsevier , 2018. Vol. 24, p. 402-412
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-46607DOI: 10.1016/j.jocs.2017.09.016ISI: 000426412200036Scopus ID: 2-s2.0-85030639866OAI: oai:DiVA.org:mdh-46607DiVA, id: diva2:1381273
Available from: 2019-12-20 Created: 2019-12-20 Last updated: 2022-11-08Bibliographically approved
In thesis
1. Machine Learning-Assisted Performance Assurance
Open this publication in new window or tab >>Machine Learning-Assisted Performance Assurance
2020 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

With the growing involvement of software systems in our life, assurance of performance, as an important quality characteristic, rises to prominence for the success of software products. Performance testing, preservation, and improvement all contribute to the realization of performance assurance. Common approaches to tackle challenges in testing, preservation, and improvement of performance mainly involve techniques relying on performance models or using system models or source code. Although modeling provides a deep insight into the system behavior, drawing a well-detailed model is challenging. On the other hand, those artifacts such as models and source code might not be available all the time. These issues are the motivations for using model-free machine learning techniques such as model-free reinforcement learning to address the related challenges in performance assurance.

Reinforcement learning implies that if the optimal policy (way) for achieving the intended objective in a performance assurance process could instead be learnt by the acting system (e.g., the tester system), then the intended objective could be accomplished without advanced performance models. Furthermore, the learnt policy could later be reused in similar situations, which leads to efficiency improvement by saving computation time while reducing the dependency on the models and source code.

In this thesis, our research goal is to develop adaptive and efficient performance assurance techniques meeting the intended objectives without access to models and source code. We propose three model-free learning-based approaches to tackle the challenges; efficient generation of performance test cases, runtime performance (response time) preservation, and performance improvement in terms of makespan (completion time) reduction. We demonstrate the efficiency and adaptivity of our approaches based on experimental evaluations conducted on the research prototype tools, i.e. simulation environments that we developed or tailored for our problems, in different application areas.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2020
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 289
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-47501 (URN)978-91-7485-463-3 (ISBN)
Presentation
2020-06-02, Online/Zoom, Västerås, 09:15 (English)
Opponent
Supervisors
Available from: 2020-04-17 Created: 2020-04-14 Last updated: 2021-01-21Bibliographically approved
2. Intelligence-Driven Software Performance Assurance
Open this publication in new window or tab >>Intelligence-Driven Software Performance Assurance
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Software performance assurance is of great importance for the success of software products, which are nowadays involved in many parts of our life. Performance evaluation approaches such as performance modeling, testing, as well as runtime performance control methods, all can contribute to the realization of software performance assurance. Many of the common approaches to tackle challenges in this area involve relying on performance models or using system models and source code. Although modeling provides a deep insight into the system behavior, developing a  detailed model is challenging.  Furthermore, software artifacts such as models and source code might not be readily available at all times in the development lifecycle. This thesis focuses on leveraging the potential of machine learning (ML) and evolutionary search-based techniques to provide viable solutions for addressing the challenges in different aspects of software performance assurance efficiently and effectively.

In this thesis, we first investigate the capabilities of model-free reinforcement learning to address the objectives in robustness testing problems. We develop two self-adaptive reinforcement learning-driven test agents called SaFReL and RELOAD. They generate effective platform-based test scenarios and test workloads, respectively. The output scenarios and workloads help testers and software engineers meet their objectives efficiently without relying on models or source code. SaFReL and RELOAD learn the optimal policies (ways) to meet the test objectives and can reuse the learned policies adaptively in other testing settings. Policy reuse can lead to higher test efficiency and cost savings, for example, when testing similar test objectives or software systems with comparable performance sensitivity.

Next, we leverage the potential of evolutionary computation algorithms, i.e., genetic algorithms, evolution strategies, and particle swarm optimization, to generate failure-revealing test scenarios for robustness testing of AI systems. In this part, we choose autonomous driving systems as a prevailing example of contemporary AI systems. We study the efficacy of the proposed evolutionary search-based test generation techniques and evaluate primarily to what extent they can trigger failures. Moreover, we investigate the diversity of those failures and compare them to existing baseline solutions. 

Finally, we again use the potential of model-free reinforcement learning to develop adaptive ML-driven runtime performance control approaches. We present a response time preservation method for a sample type of industrial applications and a resource allocation technique for dynamic workloads in a data grid application. The proposed ML-driven techniques learn how to adjust the tunable parameters and resource configuration at runtime to keep the performance continually compliant with the requirements and to further optimize the runtime performance. We evaluate the efficacy of the approaches and show how effectively they can improve the performance and keep the performance requirements satisfied under varying conditions such as dynamic workloads and the occurrence of runtime events that lead to substantial response time deviations.

Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2022
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 358
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-58065 (URN)978-91-7485-549-4 (ISBN)
Public defence
2022-06-03, Alfa, Mälardalens universitet, Västerås, 14:00 (English)
Opponent
Supervisors
Available from: 2022-04-20 Created: 2022-04-20 Last updated: 2022-11-08Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Helali Moghadam, Mahshid
In the same journal
Journal of Computational Science
Engineering and TechnologyComputer Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 112 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf