Adaptive resource configuration for Cloud infrastructure management

To guarantee the vision of Cloud Computing QoS goals between the Cloud provider and the customer have to be dynamically met. This so-called Service Level Agreement (SLA) enactment should involve little human-based interaction in order to guarantee the scalability and efficient resource utilization of the system. To achieve this we start from Autonomic Computing, examine the autonomic control loop and adapt it to govern Cloud Computing infrastructures. We first hierarchically structure all possible adaptation actions into so-called escalation levels. We then focus on one of these levels by analyzing monitored data from virtual machines and making decisions on their resource configuration with the help of knowledge management (KM). The monitored data stems both from synthetically generated workload categorized in different workload volatility classes and from a real-world scenario: scientific workflow applications in bioinformatics. As KM techniques, we investigate two methods, Case-Based Reasoning and a rule-based approach. We design and implement both of them and evaluate them with the help of a simulation engine. Simulation reveals the feasibility of the CBR approach and major improvements by the rule-based approach considering SLA violations, resource utilization, the number of necessary reconfigurations and time performance for both, synthetically generated and real-world data. Highlights? We apply knowledge management to guarantee SLAs and low resource wastage in Clouds. ? Escalation levels provide a hierarchical model to structure possible reconfiguration actions. ? Case-Based Reasoning and rule-based approach prove feasibility as KM techniques. ? In-depth evaluation of rule-based approach shows major improvements towards CBR. ? KM is applied to real-world data gathered from scientific bioinformatic workflows.