Non-Parametric Bootstrap Detection of Availability Service Level Objective Violations in Cloud Storage

Abstract

Service quality commitments in cloud service provisioning are typically described in Service Level Agreements (SLA). Service availability is always a major parameter to be included in such SLAs. and the cloud provider is bounded to guarantee a minimum availability value, for which current cloud monitoring systems employ a naive estimator. In this paper a new estimation method is proposed for service availability, which is based on the bootstrap technique and employs a non-parametric statistical hypothesis test. Through Monte Carlo simulation, the method is shown to be much more accurate than the naive one under three stochastic models for the durations of operating and outage periods, exhibiting a Type I error probability lower than 1 % in most cases, while the naive estimator yields error probabilities around 40%.