Online Prediction: Four Case Studies

Current computing systems are becoming increasingly complex in nature and exhibit large variations in workloads. These changing environments create challenges to the design of systems that can adapt themselves while maintaining desired Quality of Service (QoS), security, dependability, availability and other non-functional requirements. The next generation of resilient systems will be highly distributed, componentbased and service-oriented. They will need to operate in unattended mode and possibly in hostile environments, will be composed of a large number of interchangeable components discoverable at run-time, and will have to run on a multitude of unknown and heterogeneous hardware and network platforms. These computer systems will adapt themselves to cope with changes in the operating conditions and to meet the service-level agreements with a minimum of resources. Changes in operating conditions include hardware and software failures, load variation and variations in user interaction with the system, including security attacks and overwhelming situations. This self adaptation of next resilient systems can be achieved by first online predicting how these situations would be by observation of the current environment. This chapter focuses on the use of online predicting methods, techniques and tools for resilient systems. Thus, we survey online QoS adaptive models in several environments as grid environments, service-oriented architectures and ambient intelligence using different approaches based on queueing networks, model checking, ontology engineering among others.