A service‐agnostic method for predicting service metrics in real time

Summary We predict performance metrics of cloud services using statistical learning, whereby the behaviour of a system is learned from observations. Specifically, we collect device and network statistics from a cloud testbed and apply regression methods to predict, in real‐time, client‐side service...

Full description

Saved in:
Bibliographic Details
Published in:International journal of network management Vol. 28; no. 2
Main Authors: Yanggratoke, Rerngvit, Ahmed, Jawwad, Ardelius, John, Flinta, Christofer, Johnsson, Andreas, Gillblad, Daniel, Stadler, Rolf
Format: Journal Article
Language:English
Published: Chichester Wiley Subscription Services, Inc 01.03.2018
Subjects:
Accuracy
Cloud computing
Design and implementations
Distance learning
Distributed computer systems
Forecasting
Learning systems
machine learning
Mean absolute error
Network statistics
Operating systems
Performance measurement
Performance metrics
Prediction accuracy
Predictions
quality of service
Real time
Real time network
Real-time analytics
real-time network analytics
Regression analysis
Statistical analysis
statistical learning
Statistics
Testbeds
Video streaming
Video transmission
ISSN:1055-7148, 1099-1190, 1099-1190
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Summary We predict performance metrics of cloud services using statistical learning, whereby the behaviour of a system is learned from observations. Specifically, we collect device and network statistics from a cloud testbed and apply regression methods to predict, in real‐time, client‐side service metrics for video streaming and key‐value store services. Results from intensive evaluation on our testbed indicate that our method accurately predicts service metrics in real time (mean absolute error below 16% for video frame rate and read latency, for instance). Further, our method is service agnostic in the sense that it takes as input operating systems and network statistics instead of service‐specific metrics. We show that feature set reduction significantly improves the prediction accuracy in our case, while simultaneously reducing model computation time. We find that the prediction accuracy decreases when, instead of a single service, both services run on the same testbed simultaneously or when the network quality on the path between the server cluster and the client deteriorates. Finally, we discuss the 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. We collect device and network statistics from a cloud testbed and use statistical learning to predict client‐side service metrics for video streaming and key‐value store services. Our method predicts service metrics in real time, with mean absolute error below 16% for video frame rate and read latency. The method is service agnostic as it does not rely on service‐specific metrics on the server side. We find that feature set reduction can improve prediction accuracy, while significantly reducing model computation time.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1055-7148
1099-1190
1099-1190
DOI:10.1002/nem.1991