SmallTail: Scaling cores and probabilistic cloning requests for web systems

Users quality of experience on web systems are largely determined by the tail latency, e.g., 95th percentile. Scaling resources along, e.g., the number of virtual cores per VM, is shown to be effective to meet the average latency but falls short in taming the latency tail in the cloud where the perf...

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Autores:
Tipo de recurso:
Fecha de publicación:
2018
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
eng
OAI Identifier:
oai:repository.urosario.edu.co:10336/22856
Acceso en línea:
https://doi.org/10.1109/ICAC.2018.00013
https://repository.urosario.edu.co/handle/10336/22856
Palabra clave:
Controllers
Level control
Quality of service
Virtual machine
Websites
Autoscaling
Inner loop controls
Latency control
Level controllers
Performance variability
Probabilistic cloning
Probablistic
Quality of experience (qoe)
Cloning
Probablistic cloning
Query cloning
Tail latency control
Two level controller
Vertical autoscaling
Rights
License
http://purl.org/coar/access_right/c_abf2
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oai_identifier_str oai:repository.urosario.edu.co:10336/22856
network_acronym_str EDOCUR2
network_name_str Repositorio EdocUR - U. Rosario
repository_id_str
spelling SmallTail: Scaling cores and probabilistic cloning requests for web systemsControllersLevel controlQuality of serviceVirtual machineWebsitesAutoscalingInner loop controlsLatency controlLevel controllersPerformance variabilityProbabilistic cloningProbablisticQuality of experience (qoe)CloningProbablistic cloningQuery cloningTail latency controlTwo level controllerVertical autoscalingUsers quality of experience on web systems are largely determined by the tail latency, e.g., 95th percentile. Scaling resources along, e.g., the number of virtual cores per VM, is shown to be effective to meet the average latency but falls short in taming the latency tail in the cloud where the performance variability is higher. The prior art shows the prominence of increasing the request redundancy to curtail the latency either in the off-line setting or without scaling-in cores of virtual machines. In this paper, we propose an opportunistic scaler, termed SmallTail, which aims to achieve stringent targets of tail latency while provisioning a minimum amount of resources and keeping them well utilized. Against dynamic workloads, SmallTail simultaneously adjusts the core provisioning per VM and probabilistically replicates requests so as to achieve the tail latency target. The core of SmallTail is a two level controller, where the outer loops controls the core provision per distributed VMs and the inner loop controls the clones in a finer granularity. We also provide theoretical analysis on the steady-state latency for a given probabilistic replication that clones one out of N arriving requests. We extensively evaluate SmallTail on three different web systems, namely web commerce, web searching, and web bulletin board. Our testbed results show that SmallTail can ensure the 95th latency below 1000 ms using up to 53% less cores compared to the strategy of constant cloning, whereas scaling-core only solution exceeds the latency target by up to 70%. © 2018 IEEE.Institute of Electrical and Electronics Engineers Inc.20182020-05-25T23:58:24Zinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fapplication/pdfhttps://doi.org/10.1109/ICAC.2018.00013https://repository.urosario.edu.co/handle/10336/22856instname:Universidad del Rosarioreponame:Repositorio Institucional EdocURenghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85061316455&doi=10.1109%2fICAC.2018.00013&partnerID=40&md5=87c34c798122078edea62f43188634cdhttp://purl.org/coar/access_right/c_abf2Lakew E.B.Birke R.Pérez, Juan F.Elmroth E.Chen L.Y.oai:repository.urosario.edu.co:10336/228562022-05-02T07:37:17Z
dc.title.none.fl_str_mv SmallTail: Scaling cores and probabilistic cloning requests for web systems
title SmallTail: Scaling cores and probabilistic cloning requests for web systems
spellingShingle SmallTail: Scaling cores and probabilistic cloning requests for web systems
Controllers
Level control
Quality of service
Virtual machine
Websites
Autoscaling
Inner loop controls
Latency control
Level controllers
Performance variability
Probabilistic cloning
Probablistic
Quality of experience (qoe)
Cloning
Probablistic cloning
Query cloning
Tail latency control
Two level controller
Vertical autoscaling
title_short SmallTail: Scaling cores and probabilistic cloning requests for web systems
title_full SmallTail: Scaling cores and probabilistic cloning requests for web systems
title_fullStr SmallTail: Scaling cores and probabilistic cloning requests for web systems
title_full_unstemmed SmallTail: Scaling cores and probabilistic cloning requests for web systems
title_sort SmallTail: Scaling cores and probabilistic cloning requests for web systems
dc.subject.none.fl_str_mv Controllers
Level control
Quality of service
Virtual machine
Websites
Autoscaling
Inner loop controls
Latency control
Level controllers
Performance variability
Probabilistic cloning
Probablistic
Quality of experience (qoe)
Cloning
Probablistic cloning
Query cloning
Tail latency control
Two level controller
Vertical autoscaling
topic Controllers
Level control
Quality of service
Virtual machine
Websites
Autoscaling
Inner loop controls
Latency control
Level controllers
Performance variability
Probabilistic cloning
Probablistic
Quality of experience (qoe)
Cloning
Probablistic cloning
Query cloning
Tail latency control
Two level controller
Vertical autoscaling
description Users quality of experience on web systems are largely determined by the tail latency, e.g., 95th percentile. Scaling resources along, e.g., the number of virtual cores per VM, is shown to be effective to meet the average latency but falls short in taming the latency tail in the cloud where the performance variability is higher. The prior art shows the prominence of increasing the request redundancy to curtail the latency either in the off-line setting or without scaling-in cores of virtual machines. In this paper, we propose an opportunistic scaler, termed SmallTail, which aims to achieve stringent targets of tail latency while provisioning a minimum amount of resources and keeping them well utilized. Against dynamic workloads, SmallTail simultaneously adjusts the core provisioning per VM and probabilistically replicates requests so as to achieve the tail latency target. The core of SmallTail is a two level controller, where the outer loops controls the core provision per distributed VMs and the inner loop controls the clones in a finer granularity. We also provide theoretical analysis on the steady-state latency for a given probabilistic replication that clones one out of N arriving requests. We extensively evaluate SmallTail on three different web systems, namely web commerce, web searching, and web bulletin board. Our testbed results show that SmallTail can ensure the 95th latency below 1000 ms using up to 53% less cores compared to the strategy of constant cloning, whereas scaling-core only solution exceeds the latency target by up to 70%. © 2018 IEEE.
publishDate 2018
dc.date.none.fl_str_mv 2018
2020-05-25T23:58:24Z
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
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_c94f
dc.identifier.none.fl_str_mv https://doi.org/10.1109/ICAC.2018.00013
https://repository.urosario.edu.co/handle/10336/22856
url https://doi.org/10.1109/ICAC.2018.00013
https://repository.urosario.edu.co/handle/10336/22856
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061316455&doi=10.1109%2fICAC.2018.00013&partnerID=40&md5=87c34c798122078edea62f43188634cd
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dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.none.fl_str_mv instname:Universidad del Rosario
reponame:Repositorio Institucional EdocUR
instname_str Universidad del Rosario
institution Universidad del Rosario
reponame_str Repositorio Institucional EdocUR
collection Repositorio Institucional EdocUR
repository.name.fl_str_mv
repository.mail.fl_str_mv
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