Predicting service metrics for cluster-based services using real-time analytics

Predicting the performance of cloud services is intrinsically hard. In this work, we pursue an approach based upon statistical learning, whereby the behaviour of a system is learned from observations. Specifically, our testbed implementation collects device statistics from a server cluster and uses...

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Vydáno v:2015 11th International Conference on Network and Service Management (CNSM) s. 135 - 143
Hlavní autoři: Yanggratoke, Rerngvit, Ahmed, Jawwad, Ardelius, John, Flinta, Christofer, Johnsson, Andreas, Gillblad, Daniel, Stadler, Rolf
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IFIP 01.11.2015
Témata:
cloud computing
Computational modeling
machine learning
Measurement
network analytics
Predictive models
Quality of service
Real-time systems
Servers
Statistical learning
Yttrium
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Abstract Predicting the performance of cloud services is intrinsically hard. In this work, we pursue an approach based upon statistical learning, whereby the behaviour of a system is learned from observations. Specifically, our testbed implementation collects device statistics from a server cluster and uses a regression method that accurately predicts, in real-time, client-side service metrics for a video streaming service running on the cluster. The method is service-agnostic in the sense that it takes as input operating-systems statistics instead of service-level metrics. We show that feature set reduction significantly improves prediction accuracy in our case, while simultaneously reducing model computation time. We also discuss design and implementation of a real-time analytics engine, which processes streams of device statistics and service metrics from testbed sensors and produces model predictions through online learning.
AbstractList Predicting the performance of cloud services is intrinsically hard. In this work, we pursue an approach based upon statistical learning, whereby the behaviour of a system is learned from observations. Specifically, our testbed implementation collects device statistics from a server cluster and uses a regression method that accurately predicts, in real-time, client-side service metrics for a video streaming service running on the cluster. The method is service-agnostic in the sense that it takes as input operating-systems statistics instead of service-level metrics. We show that feature set reduction significantly improves prediction accuracy in our case, while simultaneously reducing model computation time. We also discuss design and implementation of a real-time analytics engine, which processes streams of device statistics and service metrics from testbed sensors and produces model predictions through online learning.
Author Gillblad, Daniel
Ahmed, Jawwad
Flinta, Christofer
Yanggratoke, Rerngvit
Ardelius, John
Johnsson, Andreas
Stadler, Rolf
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  surname: Yanggratoke
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  givenname: Jawwad
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  organization: Swedish Inst. of Comput. Sci. (SICS), Sweden
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  givenname: Rolf
  surname: Stadler
  fullname: Stadler, Rolf
  email: stadler@kth.se
  organization: ACCESS Linnaeus Center, KTH R. Inst. of Technol., Stockholm, Sweden
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StartPage 135
SubjectTerms cloud computing
Computational modeling
machine learning
Measurement
network analytics
Predictive models
Quality of service
Real-time systems
Servers
Statistical learning
Yttrium
Title Predicting service metrics for cluster-based services using real-time analytics
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