Experience-Availability Analysis of Online Cloud Services using Stochastic Models

Recently a new performance metric called experience availability (EA) has been proposed to evaluate online cloud service in terms of both availability and response time. EA originates from the fact that from the prospective of quality of experience (QoE), an online cloud service is regarded as unavailable not only when it is inaccessible, but also when the tail latency is high. However, there still lacks analytic models for evaluating the EA of online services. In this paper, we propose an efficient EA-analytic model using stochastic reward net (SRN) to study the tail latency performance of online cloud services in the presence of failure-repair of the resources. Our EA-analytic model can predict the online service performance on EA, as well as support analysis on traditional availability and mean response time. We apply this model to an Apache Solr search service, and evaluate the prediction accuracy by comparing the results derived from the model to actual experimental results. It is shown that the proposed model overestimates the response time at lower percentiles and underestimates the response time at higher percentiles. Through attribution analysis, we further identify the list of factors that may affect the accuracy, and show that the 95
^th
percentile latency prediction error can be reduced to as low as 2.45% by tuning the configurations suggested by the attribution.