<div class="csl-bib-body">
<div class="csl-entry">Morichetta, A., Casamayor Pujol, V., Nastic, S., Dustdar, S., Vij, D., Xiong, Y., & Zhang, Z. (2023). PolarisProfiler: A Novel Metadata-Based Profiling Approach for Optimizing Resource Management in the Edge-Cloud Continnum. In <i>2023 IEEE International Conference on Service-Oriented System Engineering (SOSE)</i> (pp. 27–36). IEEE. https://doi.org/10.1109/SOSE58276.2023.00010</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/189538
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dc.description.abstract
Resource provisioning is vital in large-scale, geo-graphically distributed, and hierarchically organized infrastructures, and, at the same time, it represents one of the stiffest challenges in their management. The goal is to optimally allocate infrastructure resources to jobs, ensuring jobs' Service Level Objectives (SLOs) while retaining high resource utilization across the entire resource pool. In this context, accurate workload profiling is crucial to achieving optimal resource management, giving more context to the system. However, approaches either make static guesses or use runtime profiling - that may be delayed by sandbox testing - and fall short in providing fast and accurate information. We aim to overcome these challenges with a novel profiling approach and methodology, the PolarisProfiler. We discard the consistency assumptions and assume a broader and less influenced perspective. We use apriori available, static metadata to enable generic and immediate job profiling based on historic execution traces. The PolarisProfiler proposes a novel dynamic profiling model, a generic workload profile generator, and a metadata-based profile classifier. We illustrate the practical feasibility of our approach by evaluating the PolarisProfiler in a case study. We target machine learning workloads, leveraging a publicly available dataset from Alibaba. We offer a reference implementation of our profiling methodology, combining a density-based hierarchical clustering technique and an interpretable decision-tree model for the classifier. We test the PolarisProfiler for job duration estimation. Despite being based solely on static, apriori metadata, we obtain convincing results compared to the state-of-the-art, yielding an estimation error rate of 5% for the 80% of profiled jobs.
en
dc.language.iso
en
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dc.subject
Polaris
en
dc.subject
Profiling
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dc.subject
Service Level Objectives
en
dc.title
PolarisProfiler: A Novel Metadata-Based Profiling Approach for Optimizing Resource Management in the Edge-Cloud Continnum
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
Futurewei Technologies, Inc., USA
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dc.contributor.affiliation
Futurewei Technologies, Inc., USA
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dc.contributor.affiliation
Futurewei Technologies, Inc., USA
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dc.relation.isbn
979-8-3503-2239-2
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dc.relation.doi
10.1109/SOSE58276.2023
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dc.relation.issn
2640-8228
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dc.description.startpage
27
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dc.description.endpage
36
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dc.type.category
Full-Paper Contribution
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dc.relation.eissn
2642-6587
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tuw.booktitle
2023 IEEE International Conference on Service-Oriented System Engineering (SOSE)
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tuw.peerreviewed
true
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tuw.relation.publisher
IEEE
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tuw.relation.publisherplace
Piscataway
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tuw.researchTopic.id
I4
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tuw.researchTopic.name
Information Systems Engineering
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E194-02 - Forschungsbereich Distributed Systems
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tuw.publisher.doi
10.1109/SOSE58276.2023.00010
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dc.description.numberOfPages
10
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tuw.author.orcid
0000-0003-3765-3067
-
tuw.author.orcid
0000-0003-2830-8368
-
tuw.author.orcid
0000-0003-0410-6315
-
tuw.author.orcid
0000-0001-6872-8821
-
tuw.event.name
17th IEEE International Conference on Service-Oriented System Engineering (IEEE SOSE 2023)
en
tuw.event.startdate
17-07-2023
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tuw.event.enddate
20-07-2023
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tuw.event.online
Hybrid
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tuw.event.type
Event for scientific audience
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tuw.event.place
Athens
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tuw.event.country
GR
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tuw.event.presenter
Morichetta, Andrea
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tuw.presentation.online
Online
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wb.sciencebranch
Informatik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.value
100
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item.fulltext
no Fulltext
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item.languageiso639-1
en
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item.openairecristype
http://purl.org/coar/resource_type/c_5794
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item.openairetype
conference paper
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item.grantfulltext
none
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item.cerifentitytype
Publications
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crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
-
crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
-
crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
-
crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
-
crisitem.author.dept
Futurewei Technologies, Inc., Santa Clara, CA, USA
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crisitem.author.orcid
0000-0003-3765-3067
-
crisitem.author.orcid
0000-0003-2830-8368
-
crisitem.author.orcid
0000-0003-0410-6315
-
crisitem.author.orcid
0000-0001-6872-8821
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering