Temporal-Perturbation aware Reliability Sensitivity Measurement for Adaptive Cloud Service Selection

Benefiting from the pay-as-you-go business model, cloud computing has significantly promoted service computing techniques in real-world industrial applications. Software applications based on cloud computing are becoming more and more popular. By integrating existing component cloud services through the internet, composite cloud systems can be built to meet sophisticated application logic. Stable execution of such systems is desirable in the long term so that the service-level agreements (SLAs), as well as users’ quality of experience (QoE), can be fulfilled. To achieve this goal, it is critical to identify and fault-tolerate system components at high risks of failing. This is extremely challenging due to the dynamic and uncertainty of the cloud environment that hosts the component cloud services. Nevertheless, existing approaches pay little attention to the modeling and analysis of system components’ reliability time series. To address the above issues, we first present a reliability evaluation method for component cloud services based on the reliability model and their failure probability under continuous client-side invocation tests. Then, we propose a perturbation-aware reliability sensitivity measurement approach (named PARS) for measuring the reliability sensitivity of component cloud services. It first analyzes the negative perturbations in component cloud services’ historical reliability time series based on the Markov chain rule. Then, it calculates the reliability sensitivity of component cloud services by analyzing how their reliability perturbations impact the reliability of the entire cloud system. To guarantee the execution quality of the composite cloud system, we further propose a proactive adaptation approach named PA-PARS that enables 1-out-of-2 N-version Programming fault-tolerance for composite cloud systems based on PARS. PA-PARS takes the reliability sensitivity of component cloud services estimated by PARS as input to assure the reliability of the cloud system. It consists of four parts: 1) risky system component identification; 2) adaptation trigger; 3) candidate component cloud service selection; and 4) NVP-based system construction as the proactive adaptation for the composite cloud system. The results of experiments conducted on two widely-used datasets demonstrate the effectiveness and efficiency of the proposed approaches in ensuring the reliability of composite cloud systems.