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Utilizing user perceived latency as an indicator for system failure
KTH, School of Computer Science and Communication (CSC).
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Användning av fördröjningstider hos slutanvändaren som en indikator för systemfel (Swedish)
Abstract [en]

Monitoring systems used in the industry mainly trigger alerts based on system metrics such as CPU usage, memory usage and disk space. There is a trend on also alerting on metrics related to end user experience. This study evaluates the use of playback latency in the Spotify music streaming service as an indicator for detecting system failures. Six months of playback log data together with tickets from a system incidents tracker were analyzed. The playback latency distribution was studied using a sliding window aggregation method. A cyclic pattern was found and two simple anomaly detection algorithms were then applied on the time series. The detected anomalies were matched together with tickets from the system incident tracker. In the most efficient algorithm, in terms of finding anomalies matching a ticket, the hit ratio was 57 %. However, since the system incident tracker was the only source for system failures, there was a possibility that unreported failures occurred. Metrics related to end user experience are in many cases business critical, and this motivates the need of monitoring playback latency, although it does not seem to be a silver bullet for finding system failures.

Abstract [sv]

Inom industrin är det vanligt att övervaka mätvärden som till exempel CPU- och minnesanvändning samt ledigt diskutrymme. Det finns också en trend att övervaka mätvärden som hänger ihop med slutanvändarens upplevelse. Denna studie undersöker användningen av latenstider i Spotifys musiktjänst som en indikator för systemfel. Sex månaders uppspelningsstatistik med fördröjningstider analyserades tillsammans med en systemfelslogg innehållande systemincidenter. Distributionen av latenstiden undersöktes och genom en metod för att aggregera latensen studerades den också i tidsserier. Två enkla algoritmer för avvikelsedetektering applicerades på tidsserierna och avvikelser matchades med incidenter från systemfelsloggen. I den mest effektiva algoritmen matchade 57 % av de detekterade anomalierna en incident. Eftersom systemfelsloggen var den enda källan för incidenter kunde man ej utesluta att oregistrerade fel inträffade under perioden. Mätvärden relaterade till slutanvändarens upplevelse är i många fall ett affärskritiskt mått och detta motiverar övervakning av latenstider. Dock verkar det ej räcka med att bara övervaka detta mått för att tillförlitligt hitta systemfel.

Place, publisher, year, edition, pages
2014.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-161730OAI: oai:DiVA.org:kth-161730DiVA: diva2:799915
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2015-04-20 Created: 2015-03-15 Last updated: 2015-04-20Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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