Workload adaptation is a performance management process in which an autonomic database management system (DBMS) efficiently makes use of its resources by filtering or controlling the workload presented to it in order to meet its Service Level Objectives (SLOs). It is a challenge to adapt multiple workloads with complex resource requirements towards their performance goals while taking their business importance into account. This thesis studies approaches and techniques for workload adaptation. First we build a general framework for workload adaptation in autonomic DBMSs, which is composed of two processes, namely workload detection and workload control. The processes are in turn made up of four functional components - workload characterization, performance modeling, workload control, and system monitoring. We then implement a query scheduler that performs workload adaptation in a DBMS, as the test bed to prove the effectiveness of the framework. The query scheduler manages multiple classes of queries to meet their performance goals by allocating DBMS resources through admission control in the presence of workload fluctuation. The resource allocation plan is derived by maximizing the objective function that encapsulates the performance goals of all classes and their importance to the business. First-principle performance models are used to predict the performance under the new resource allocation plan. Experiments with IBM® DB2® are conducted to show the effectiveness of the framework. The effectiveness of the workload adaptation depends on the accuracy of the performance prediction. Finally we introduce a tracking filter (Kalman filter) to improve the accuracy of the performance prediction. Experimental results show that the approach is able to reduce the number of unpredicted SLO violations and prediction errors. / Thesis (Ph.D, Computing) -- Queen's University, 2008-01-28 21:22:25.139

<p>This master thesis is a portion of the DySCAS project and work is performed at Enea AB. DySCAS (Dynamically Self-Configuring Automotive Systems) is a research project funded by the European</p><p>This thesis concentrates on future vehicle electronic systems. During a life cycle of the car vehicle manufacturers desire to upgrade or add new functions into the vehicle electronic systems, this is not possible with the static-runtime environment that employed into today’s car.</p><p>To tackle this difficult problem many technologies were gathered and a dynamically self-configuring automotive system was introduced by combining technologies like self-managing, service-based and middleware.</p><p>The obtained results fulfilled most of DySCAS requirements. However, the system has a few limitations and these are caused by the immature of distributed reconfigurable embedded systems in the market.</p>

This master thesis is a portion of the DySCAS project and work is performed at Enea AB. DySCAS (Dynamically Self-Configuring Automotive Systems) is a research project funded by the European This thesis concentrates on future vehicle electronic systems. During a life cycle of the car vehicle manufacturers desire to upgrade or add new functions into the vehicle electronic systems, this is not possible with the static-runtime environment that employed into today’s car. To tackle this difficult problem many technologies were gathered and a dynamically self-configuring automotive system was introduced by combining technologies like self-managing, service-based and middleware. The obtained results fulfilled most of DySCAS requirements. However, the system has a few limitations and these are caused by the immature of distributed reconfigurable embedded systems in the market.

Self-adaptive and self-managing systems optimize their own behaviour according to high-level objectives and constraints. One way for human administrators to effectively specify goals for such optimization problems is using policies. Over the past decade, researchers produced various approaches, models and techniques for policy specification in different areas including distributed systems, communication networks, web services, autonomic computing, and cloud computing. Research challenges range from characterizing policies for ease of specification in particular application domains to categorizing policies for achieving good solution qualities for particular algorithmic techniques. The contributions of this thesis are threefold. Firstly, we give a mathematical formulation for each of the three policy types, action, goal and utility function policies, introduced in the policy framework by Kephart and Walsh. In particular, we introduce a first precise characterization of goal policies for optimization problems. Secondly, this thesis introduces a mathematical framework that adds structure to the underlying optimization problem for different types of policies. Structure is added either to the objective function or the constraints of the optimization problem. These mathematical structures, imposed on the underlying problem, progressively increase the quality of the solutions obtained when using the greedy optimization technique. Thirdly, we show the applicability of our framework through case studies by analyzing several optimization problems encountered in self-adaptive and self-managing systems, such as resource allocation, quality of service management, and Service Level Agreement (SLA) profit optimization to provide quality guarantees for their solutions. Our approach combines the algorithmic results by Edmonds, Fisher et al., and Mestre, and the policy framework of Kephart and Walsh. Our characterization and approach will help designers of self-adaptive and self-managing systems formulate optimization problems, decide on algorithmic strategies based on policy requirements, and reason about solution qualities. / Graduate / 0984