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

Yanggratoke, Rerngvit, 1983- (författare)
KTH,ACCESS Linnaeus Centre,Kommunikationsnät,Kommunikationsnät, Communication Networks,KTH Royal Institute of Technology, Sweden
Ahmed, Jawwad (författare)
Ericsson Research, Sweden
Ardelius, John (författare)
RISE,SICS,Swedish Institute of Computer Science (SICS), Sweden
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Flinta, Christofer (författare)
Ericsson Research, Sweden
Johnsson, Andreas (författare)
Ericsson Research, Sweden
Gillblad, Daniel (författare)
RISE,SICS,Swedish Institute of Computer Science (SICS), Sweden
Stadler, Rolf (författare)
KTH,RISE,SICS,KTH Royal Institute of Technology, Sweden,ACCESS Linnaeus Centre,Kommunikationsnät, Communication Networks
visa färre...
 (creator_code:org_t)
Institute of Electrical and Electronics Engineers Inc. 2015
2015
Engelska.
Ingår i: Proceedings of the 11th International Conference on Network and Service Management, CNSM 2015. - : Institute of Electrical and Electronics Engineers Inc.. - 9783901882777 ; , s. 135-143
  • Konferensbidrag (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

cloud computing
machine learning
network analytics
Quality of service
statistical learning
Artificial intelligence
Forecasting
Learning systems
Regression analysis
Statistics
Testbeds
Video streaming
Design and implementations
Model computation
Model prediction
Prediction accuracy
Real-time analytics
Regression method
Video streaming services
Distributed computer systems

Publikations- och innehållstyp

ref (ämneskategori)
kon (ämneskategori)

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