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A service-agnostic method for predicting service metrics in real-time
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Communication Networks. (Kommunikationsnät, Communication Networks)ORCID iD: 0000-0002-2680-9065
(Ericsson Research, Sweden)
(Swedish Institute of Computer Science (SICS), Sweden)
(Ericsson Research, Sweden)
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

We predict performance metrics of cloud services using statistical learning, whereby the behavior 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. 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.

Keyword [en]
Quality of service, cloud computing, network analytics, statistical learning, machine learning
National Category
Computer Systems Communication Systems Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-184203OAI: oai:DiVA.org:kth-184203DiVA: diva2:915578
Projects
REALM
Funder
VINNOVA, 2013-03895
Note

QC 20160411

Available from: 2016-03-30 Created: 2016-03-30 Last updated: 2016-04-11Bibliographically approved
In thesis
1. Data-driven Performance Prediction and Resource Allocation for Cloud Services
Open this publication in new window or tab >>Data-driven Performance Prediction and Resource Allocation for Cloud Services
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cloud services, which provide online entertainment, enterprise resource management, tax filing, etc., are becoming essential for consumers, businesses, and governments. The key functionalities of such services are provided by backend systems in data centers. This thesis focuses on three fundamental problems related to management of backend systems. We address these problems using data-driven approaches: triggering dynamic allocation by changes in the environment, obtaining configuration parameters from measurements, and learning from observations. 

The first problem relates to resource allocation for large clouds with potentially hundreds of thousands of machines and services. We developed and evaluated a generic gossip protocol for distributed resource allocation. Extensive simulation studies suggest that the quality of the allocation is independent of the system size for the management objectives considered.

The second problem focuses on performance modeling of a distributed key-value store, and we study specifically the Spotify backend for streaming music. We developed analytical models for system capacity under different data allocation policies and for response time distribution. We evaluated the models by comparing model predictions with measurements from our lab testbed and from the Spotify operational environment. We found the prediction error to be below 12% for all investigated scenarios.

The third problem relates to real-time prediction of service metrics, which we address through statistical learning. Service metrics are learned from observing device and network statistics. We performed experiments on a server cluster running video streaming and key-value store services. We showed that feature set reduction significantly improves the prediction accuracy, while simultaneously reducing model computation time. Finally, we designed and implemented a real-time analytics engine, which produces model predictions through online learning.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. 53 p.
Series
TRITA-EE, ISSN 1653-5146 ; 2016:020
National Category
Communication Systems Computer Systems Telecommunications Computer Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-184601 (URN)978-91-7595-876-7 (ISBN)
Public defence
2016-05-03, F3, Lindstedtsvägen 26, KTH Campus, Stockholm, 14:00 (English)
Opponent
Supervisors
Funder
VINNOVA, 2013-03895
Note

QC 20160411

Available from: 2016-04-11 Created: 2016-04-01 Last updated: 2016-05-30Bibliographically approved

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