Cutting Latency Tail: Analyzing and Validating Replication without Canceling

Response time variability in software applications can severely degrade the quality of the user experience. To reduce this variability, request replication emerges as an effective solution by spawning multiple copies of each request and using the result of the first one to complete. Most previous st...

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Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/24250
Acceso en línea:
https://doi.org/10.1109/TPDS.2017.2706268
https://repository.urosario.edu.co/handle/10336/24250
Palabra clave:
Application programs
Benchmarking
Computer software
Computer software selection and evaluation
Legacy systems
MATLAB
Web services
Effective solution
Matrix analytic methods
Response time distribution
Response time variability
Service time
Software applications
Software quality engineering
Speculative computing
Response time (computer systems)
Correlated service times
Matrix analytic methods
Software quality engineering
Speculative computing
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License
Abierto (Texto Completo)
id EDOCUR2_f13aa401f99cf1b268263e64448a1fa6
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network_acronym_str EDOCUR2
network_name_str Repositorio EdocUR - U. Rosario
repository_id_str
spelling fc4c8af7-473a-473f-8114-951ac1db1cae80035202600626ccffb-751e-4268-8127-7e4e25d3ad64ad8f005f-cda0-4faf-a367-d2bfb7d44938c6b727dd-a371-4d51-b960-3a8d758399e12020-05-26T00:10:42Z2020-05-26T00:10:42Z2017Response time variability in software applications can severely degrade the quality of the user experience. To reduce this variability, request replication emerges as an effective solution by spawning multiple copies of each request and using the result of the first one to complete. Most previous studies have mainly focused on the mean latency for systems implementing replica cancellation, i.e., all replicas of a request are canceled once the first one finishes. Instead, we develop models to obtain the response-time distribution for systems where replica cancellation may be too expensive or infeasible to implement, as in 'fast' systems, such as web services, or in legacy systems. Furthermore, we introduce a novel service model to explicitly consider correlation in the processing times of the request replicas, and design an efficient algorithm to parameterize the model from real data. Extensive evaluations on a MATLAB benchmark and a three-tier web application (MediaWiki) show remarkable accuracy, e.g., 7 (4 percent) average error on the 99th percentile response time for the benchmark (respectively, MediaWiki), the requests of which execute in the order of seconds (respectively, milliseconds). Insights into optimal replication levels are thereby gained from this precise quantitative analysis, under a wide variety of system scenarios. © 2017 IEEE.application/pdfhttps://doi.org/10.1109/TPDS.2017.270626810459219https://repository.urosario.edu.co/handle/10336/24250engIEEE Computer Society3141No. 113128IEEE Transactions on Parallel and Distributed SystemsVol. 28IEEE Transactions on Parallel and Distributed Systems, ISSN:10459219, Vol.28, No.11 (2017); pp. 3128-3141https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032457020&doi=10.1109%2fTPDS.2017.2706268&partnerID=40&md5=bc10af0ad3bf606510446e87dbad6990Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURApplication programsBenchmarkingComputer softwareComputer software selection and evaluationLegacy systemsMATLABWeb servicesEffective solutionMatrix analytic methodsResponse time distributionResponse time variabilityService timeSoftware applicationsSoftware quality engineeringSpeculative computingResponse time (computer systems)Correlated service timesMatrix analytic methodsSoftware quality engineeringSpeculative computingCutting Latency Tail: Analyzing and Validating Replication without CancelingarticleArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Qiu Z.Pérez, Juan F.Birke R.Chen L.Harrison P.G.10336/24250oai:repository.urosario.edu.co:10336/242502022-05-02 07:37:17.052016https://repository.urosario.edu.coRepositorio institucional EdocURedocur@urosario.edu.co
dc.title.spa.fl_str_mv Cutting Latency Tail: Analyzing and Validating Replication without Canceling
title Cutting Latency Tail: Analyzing and Validating Replication without Canceling
spellingShingle Cutting Latency Tail: Analyzing and Validating Replication without Canceling
Application programs
Benchmarking
Computer software
Computer software selection and evaluation
Legacy systems
MATLAB
Web services
Effective solution
Matrix analytic methods
Response time distribution
Response time variability
Service time
Software applications
Software quality engineering
Speculative computing
Response time (computer systems)
Correlated service times
Matrix analytic methods
Software quality engineering
Speculative computing
title_short Cutting Latency Tail: Analyzing and Validating Replication without Canceling
title_full Cutting Latency Tail: Analyzing and Validating Replication without Canceling
title_fullStr Cutting Latency Tail: Analyzing and Validating Replication without Canceling
title_full_unstemmed Cutting Latency Tail: Analyzing and Validating Replication without Canceling
title_sort Cutting Latency Tail: Analyzing and Validating Replication without Canceling
dc.subject.keyword.spa.fl_str_mv Application programs
Benchmarking
Computer software
Computer software selection and evaluation
Legacy systems
MATLAB
Web services
Effective solution
Matrix analytic methods
Response time distribution
Response time variability
Service time
Software applications
Software quality engineering
Speculative computing
Response time (computer systems)
Correlated service times
Matrix analytic methods
Software quality engineering
Speculative computing
topic Application programs
Benchmarking
Computer software
Computer software selection and evaluation
Legacy systems
MATLAB
Web services
Effective solution
Matrix analytic methods
Response time distribution
Response time variability
Service time
Software applications
Software quality engineering
Speculative computing
Response time (computer systems)
Correlated service times
Matrix analytic methods
Software quality engineering
Speculative computing
description Response time variability in software applications can severely degrade the quality of the user experience. To reduce this variability, request replication emerges as an effective solution by spawning multiple copies of each request and using the result of the first one to complete. Most previous studies have mainly focused on the mean latency for systems implementing replica cancellation, i.e., all replicas of a request are canceled once the first one finishes. Instead, we develop models to obtain the response-time distribution for systems where replica cancellation may be too expensive or infeasible to implement, as in 'fast' systems, such as web services, or in legacy systems. Furthermore, we introduce a novel service model to explicitly consider correlation in the processing times of the request replicas, and design an efficient algorithm to parameterize the model from real data. Extensive evaluations on a MATLAB benchmark and a three-tier web application (MediaWiki) show remarkable accuracy, e.g., 7 (4 percent) average error on the 99th percentile response time for the benchmark (respectively, MediaWiki), the requests of which execute in the order of seconds (respectively, milliseconds). Insights into optimal replication levels are thereby gained from this precise quantitative analysis, under a wide variety of system scenarios. © 2017 IEEE.
publishDate 2017
dc.date.created.spa.fl_str_mv 2017
dc.date.accessioned.none.fl_str_mv 2020-05-26T00:10:42Z
dc.date.available.none.fl_str_mv 2020-05-26T00:10:42Z
dc.type.eng.fl_str_mv article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.spa.spa.fl_str_mv Artículo
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1109/TPDS.2017.2706268
dc.identifier.issn.none.fl_str_mv 10459219
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/24250
url https://doi.org/10.1109/TPDS.2017.2706268
https://repository.urosario.edu.co/handle/10336/24250
identifier_str_mv 10459219
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.citationEndPage.none.fl_str_mv 3141
dc.relation.citationIssue.none.fl_str_mv No. 11
dc.relation.citationStartPage.none.fl_str_mv 3128
dc.relation.citationTitle.none.fl_str_mv IEEE Transactions on Parallel and Distributed Systems
dc.relation.citationVolume.none.fl_str_mv Vol. 28
dc.relation.ispartof.spa.fl_str_mv IEEE Transactions on Parallel and Distributed Systems, ISSN:10459219, Vol.28, No.11 (2017); pp. 3128-3141
dc.relation.uri.spa.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032457020&doi=10.1109%2fTPDS.2017.2706268&partnerID=40&md5=bc10af0ad3bf606510446e87dbad6990
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.acceso.spa.fl_str_mv Abierto (Texto Completo)
rights_invalid_str_mv Abierto (Texto Completo)
http://purl.org/coar/access_right/c_abf2
dc.format.mimetype.none.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv IEEE Computer Society
institution Universidad del Rosario
dc.source.instname.spa.fl_str_mv instname:Universidad del Rosario
dc.source.reponame.spa.fl_str_mv reponame:Repositorio Institucional EdocUR
repository.name.fl_str_mv Repositorio institucional EdocUR
repository.mail.fl_str_mv edocur@urosario.edu.co
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