Abstract
Service computing in the cloud allows applications to be deployed remotely. These are managed by third-party service providers that make virtualised resources available for these services. Self-adaptive features for load-balancing and auto-scaling are available here, but generally there is no direct access to the infrastructure or platform-level execution environment.Some quality parameters of a provided service can be directly observed while others remain hidden from the service consumer.Our solution is an autonomous self-adaptive controller for anomaly remediation in this semi-hidden setting. The objective of the controller is to, firstly, determine possible root causes of consumer-observed anomalies and, secondly, take appropriate action. This needs to happen in an underlying provider-controlled infrastructure. We use Hidden Markov Models to map observed performance anomalies into hidden resources, and to identify the root causes of the observed anomalies. We apply the model to a clustered computing resource environment that is based on three layers of aggregated resources. We discuss use cases to illustrate the utility of the proposed solution.