Signature-based Selection of IaaS Cloud Services

We propose a novel approach to select IaaS cloud services for a long-term period where the service providers offer limited QoS information. The proposed approach leverages free short-term trials to obtain the previously undisclosed QoS information. A new significance-based trial scheme is proposed using frequency distribution analysis to test a consumer's long-term workloads in a short trial. We introduce a novel IaaS signature technique to uniquely identify the variability of a provider's QoS performance. A Signature-based QoS Performance Discovery (SPD) algorithm is proposed which leverages the combination of free trials and IaaS signatures. A set of exhaustive experiments with real-world datasets is conducted to evaluate the proposed approach.Comment: 8 pages, Accepted and to appear in 2020 IEEE International Conference on Web Services (ICWS). Content may change prior to final publicatio

Event-based Detection of Changes in IaaS Performance Signatures

We propose a novel ECA approach to manage changes in IaaS performance signatures. The proposed approach relies on the detection of anomalous performance behavior in the context of IaaS performance signatures. A novel anomaly-based event detection technique is proposed. It utilizes the experience of free trial users to detect potential changes in IaaS performance signatures. A signature change detection technique is proposed using the cumulative sum control chart analysis. Additionally, a self-adjustment method is introduced to improve the accuracy of the proposed approach. A set of experiments based on real-world datasets are conducted to show the effectiveness of the proposed approach.Comment: 8 pages, Accepted and to appear in 2020 International Conference on Services Computing (IEEE SCC 2020). Content may change prior to final publicatio