Model-Based Dynamic Resource Management for Service Oriented Clouds

Cloud computing is a flexible platform for software as a service, as more and more applications are deployed on cloud. Major challenges in cloud include how to characterize the workload of the applications and how to manage the cloud resources efficiently by sharing them among many applications. The current state of the art considers a simplified model of the system, either ignoring the software components altogether or ignoring the relationship between individual software services. This thesis considers the following resource management problems for cloud-based service providers: (i) how to estimate the parameters of the current workload, (ii) how to meet Quality of Service (QoS) targets while minimizing infrastructure cost, (iii) how to allocate resources considering performance costs of virtual machine reconfigurations. To address the above problems, we propose a model-based feedback loop approach. The cloud infrastructure, the services, and the applications are modelled using Layered Queuing Models (LQM). These models are then optimized. Mathematical techniques are used to reduce the complexity of the models and address the scalability issues. The main contributions of this thesis are: (i) Extended Kalman Filter (EKF) based techniques improved by dynamic clustering for scalable estimation of workload parameters, (ii) combination of adaptive empirical models (tuned during runtime) and stepwise optimizations for improving the overall allocation performance, (iii) dynamic service placement algorithms that consider the cost of virtual machine reconfiguration

Engineering Model-Based Adaptive Software Systems

Adaptive software systems are able to cope with changes in the environment by self-adjusting their structure and behavior. Robustness refers to the ability of the systems to deal with uncertainty, i.e. perturbations (e.g., Denial of Service attacks) or not-modeled system dynamics (e.g., independent cloud applications hosted on the same physical machine) that can affect the quality of the adaptation. To build robust adaptive systems we need models that accurately describe the managed system and methods for how to react to different types of change. In this thesis we introduce techniques that will help an engineer design adaptive systems for web applications. We describe methods to accurately model web applications deployed in cloud in such a way that it accounts for cloud variability and to keep the model synchronized with the actual system at runtime. Using the model, we present methods to optimize the deployed architecture at design- and run-time, uncover bottlenecks and the workloads that saturate them, maintain the service level objective by changing the quantity of available resources (for regular operating conditions or during a Denial of Service attack). We validate the proposed contributions on experiments performed on Amazon EC2 and simulators. The types of applications that benefit the most from our contributions are web-based information systems deployed in cloud