QoS Control of Real-Time Data Services under Uncertain Workload

Real-time systems comprise computers that must generate correct results in a timely manner. This involves a wide spectrum of computing systems found in our everyday life ranging from computers in rockets to our mobile phones. The criticality of producing timely results defines the different types of realtime systems. On one hand, we have the so-called hard real-time systems, where failing to meet deadlines may result in a catastrophe. In this thesis we are, however, concerned with firm and soft real-time systems, where missing deadlines is acceptable at the expense of degraded system performance. The usage of firm and soft real-time systems has increased rapidly during the last years, mainly due to the advent of applications in multimedia, telecommunication, and e-commerce. These systems are typically data-intensive, with the data normally spanning from low-level control data, typically acquired from sensors, to high-level management and business data. In contrast to hard real-time systems, the environments in which firm and soft real-time systems operate in are typically open and highly unpredictable. For example, the workload applied on a web server or base station in telecommunication systems varies according to the needs of the users, which is hard to foresee. In this thesis we are concerned with quality of service (QoS) management of data services for firm and soft real-time systems. The approaches and solutions presented aim at providing a general understanding of how the QoS can be guaranteed according to a given specification, even if the workload varies unpredictably. The QoS specification determines the desired QoS during normal system operation, and the worst-case system performance and convergence rate toward the desired setting in the face of transient overloads. Feedback control theory is used to control QoS since little is known about the workload applied on the system. Using feedback control the difference between the measured QoS and the desired QoS is formed and fed into a controller, which computes a change to the operation of the real-time system. Experimental evaluation shows that using feedback control is highly effective in managing QoS such that a given QoS specification is satisfied. This is a key step toward automatic management of intricate systems providing real-time data services.