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

This study examines how gender as a socio-cultural construction factors in the currently occurring change from a bureaucratic work organization to more interactive and team-based structures. Informed by Joan Acker's theory of gendered organization, I identify processes that produce and reproduce gendered relationships of domination and subordination in self-managing teams, despite the premise that self-managing teams foster more egalitarian workplace relations. In a multiple case study, using in-depth interviews and participant observation, I examine four currently functioning, mixed-sex, self-managing teams in two service sector organizations and one manufacturing plant. The objective of the study is to uncover how and in what ways gender is present in teamwork and shapes various routine work processes. The so-called “gendered processes“ I found to occur in the four case-study teams include a gender division of team tasks that required women to perform clerical work even when teams were supposed to implement cross-functional task sharing. Gendered processes also took place through interaction and team metaphors of “family“ and “football team.“ I illustrate how the construction of emotions in teamwork marginalized women's contributions and how women and men consciously employed strategies to fit into expectations of gender-appropriate behavior. Despite these gender divisions, I suggest that one possible way for teams to improve organizational gender equality is that they emphasize non-hierarchical spatial arrangements. Finally, although I found gendered processes in all four teams, the ways in which gender shaped teamwork varied according to the organizational status position of a team. Also self-management proved the most comprehensive in teams that functioned at the higher organizational levels. I thank the Finnish Work Environment Fund, The Foundation for Economic Education, and Ella and Georg Ehrnrooth's Foundation, all of Helsinki, Finland, for their financial support towards the completion of this dissertation. This study was also supported by dissertation grants from Eemil Aaltonen's Foundation of Tampere, Finland and Oskar à flund's Foundation of Espoo, Finland, for which I am grateful. / Ph. D.

<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>

[pt] A presente dissertação tem como objetivo identificar e compreender o processo de concepção e implantação de grupos autogerenciáveis na produção, por meio da análise de dois casos de implantação desses grupos, nas empresas Ambev e Michelin. Busca-se primeiramente analisar as abordagens que introduziram o conceito de grupos autogerenciáveis, ressaltando suas características. Os casos são descritos de forma a mostrar a situação das empresas antes das mudanças, bem como as etapas mais relevantes nos processos de concepção e implantação do novo modelo. Uma análise comparativa dos dois casos foi realizada com vistas a compreender os fatores importantes na condução dos processos de concepção e implantação de grupos autogerenciáveis. Conclui-se, a partir da análise, que a definição dos modelos foi influenciada pela observação de casos concretos de implantação em outras empresas, não levando em consideração aspectos teóricos. Os processos de implantação apresentam muitas semelhanças. A distinção, por sua vez, apresenta-se em algumas especificidades das etapas. Por fim, foram destacados alguns fatores que podem ser indutivos ou limitantes para o desenvolvimento dos grupos autogerenciáveis. / [en] The present dissertation aims at identifying and understanding the process of conception and implementation of self-managing groups in production. Following the methodology of case study, the experiences of two companies are observed: AmBev and Michelin. The main intent is to understand each approach, describing the organizational context before the changes towards self-managing groups and also the key steps for implementing the new model. Then, the two cases are comparatively interpreted, leading to some conclusions about what should be the most important factors to be taken into account when implementing self-managing groups. It is worth noting that, in both companies, the conception of the respective models emerges from observations of a variety of concrete cases related to the experiences of other plants or companies. They were hardly influenced by theory. Although the two approaches share many similar steps, some significant differences can be highlighted. Finally, such similarities and differences are analyzed in order to grasp factors that can induce or constrain the development of self-managing groups.

The scale of computing systems has extensively grown over the past few decades in order to satisfy emerging business requirements. As a result of this evolution, the complexity of these systems has increased significantly, which has led to many difficulties in managing and administering them. The solution to this problem is to build systems that are capable of managing themselves, given high-level objectives. This vision is also known as Autonomic Computing. A self-managing system is governed by a closed control loop, which is responsible for dynamically monitoring the underlying system, analyzing the observed situation, planning the recovering actions, and executing the plan to maintain the system equilibrium. The realization of such systems poses several developmental and operational challenges, including: developing their architecture, constructing the control loop, and creating services that enable dynamic adaptation behavior. Software frameworks are effective in addressing these challenges: they can simplify the development of such systems by reducing design and implementation efforts, and they provide runtime services for supporting self-managing behavior. This dissertation presents a novel software framework, called StarMX, for developing adaptive and self-managing Java-based systems. It is a generic configurable framework based on standards and well-established principles, and provides the required features and facilities for the development of such systems. It extensively supports Java Management Extensions (JMX) and is capable of integrating with different policy engines. This allows the developer to incorporate and use these techniques in the design of a control loop in a flexible manner. The control loop is created as a chain of entities, called processes, such that each process represents one or more functions of the loop (monitoring, analyzing, planning, and executing). A process is implemented by either a policy language or the Java language. At runtime, the framework invokes the chain of processes in the control loop, providing each one with the required set of objects for monitoring and effecting. An open source Java-based Voice over IP system, called CC2, is selected as the case study used in a set of experiments that aim to capture a solid understanding of the framework suitability for developing adaptive systems and to improve its feature set. The experiments are also used to evaluate the performance overhead incurred by the framework at runtime. The performance analysis results show the execution time spent in different components, including the framework itself, the policy engine, and the sensors/effectors. The results also reveal that the time spent in the framework is negligible, and it has no considerable impact on the system's overall performance.

The scale of computing systems has extensively grown over the past few decades in order to satisfy emerging business requirements. As a result of this evolution, the complexity of these systems has increased significantly, which has led to many difficulties in managing and administering them. The solution to this problem is to build systems that are capable of managing themselves, given high-level objectives. This vision is also known as Autonomic Computing. A self-managing system is governed by a closed control loop, which is responsible for dynamically monitoring the underlying system, analyzing the observed situation, planning the recovering actions, and executing the plan to maintain the system equilibrium. The realization of such systems poses several developmental and operational challenges, including: developing their architecture, constructing the control loop, and creating services that enable dynamic adaptation behavior. Software frameworks are effective in addressing these challenges: they can simplify the development of such systems by reducing design and implementation efforts, and they provide runtime services for supporting self-managing behavior. This dissertation presents a novel software framework, called StarMX, for developing adaptive and self-managing Java-based systems. It is a generic configurable framework based on standards and well-established principles, and provides the required features and facilities for the development of such systems. It extensively supports Java Management Extensions (JMX) and is capable of integrating with different policy engines. This allows the developer to incorporate and use these techniques in the design of a control loop in a flexible manner. The control loop is created as a chain of entities, called processes, such that each process represents one or more functions of the loop (monitoring, analyzing, planning, and executing). A process is implemented by either a policy language or the Java language. At runtime, the framework invokes the chain of processes in the control loop, providing each one with the required set of objects for monitoring and effecting. An open source Java-based Voice over IP system, called CC2, is selected as the case study used in a set of experiments that aim to capture a solid understanding of the framework suitability for developing adaptive systems and to improve its feature set. The experiments are also used to evaluate the performance overhead incurred by the framework at runtime. The performance analysis results show the execution time spent in different components, including the framework itself, the policy engine, and the sensors/effectors. The results also reveal that the time spent in the framework is negligible, and it has no considerable impact on the system's overall performance.

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

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Automatic physical database design tools rely on ``what-if'' interfaces to the query optimizer to estimate the execution time of the training query workload under different candidate physical designs. The tools use these what-if interfaces to recommend physical designs that minimize the estimated execution time of the input training workload. Minimizing estimated execution time alone can lead to designs that are not robust to query optimizer errors and workload changes. In particular, if the optimizer makes errors in estimating the execution time of the workload queries, then the recommended physical design may actually degrade the performance of these queries. In this sense, the physical design is risky. Furthermore, if the production queries are slightly different from the training queries, the recommended physical design may not benefit them at all. In this sense, the physical design is not general. We define Risk and Generality as two new measures aimed at evaluating the robustness of a proposed physical database design, and we show how to extend the objective function being optimized by a generic physical design tool to take these measures into account. We have implemented a physical design advisor in PostqreSQL, and we use it to experimentally demonstrate the usefulness of our approach. We show that our two new metrics result in physical designs that are more robust, which means that the user can implement them with a higher degree of confidence. This is particularly important as we move towards truly zero-administration database systems in which there is not the possibility for a DBA to vet the recommendations of the physical design tool before applying them.