Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a "hype topic" or a rebadging of some Multi Agent Systems work. In this survey, we hope to show that this was not indeed 'hype' and that, though it draws on much work already carried out by the Computer Science and Control communities, its innovation is strong and lies in its robust application to the specific self-management of computing systems. To this end, we first provide an introduction to the motivation and concepts of autonomic computing and describe some research that has been seen as seminal in influencing a large proportion of early work. Taking the components of an established reference model in turn, we discuss the works that have provided significant contributions to that area. We then look at larger scaled systems that compose autonomic systems illustrating the hierarchical nature of their architectures. Autonomicity is not a well defined subject and as such different systems adhere to different degrees of Autonomicity, therefore we cross-slice the body of work in terms of these degrees. From this we list the key applications of autonomic computing and discuss the research work that is missing and what we believe the community should be considering. ACM Reference Format:Huebscher, M. C. and McCann, J. A. 2008. A survey of autonomic computing-degrees, models, and applications.

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Abstract. Autonomic computing is a concept that brings together many fields of computing with the purpose of creating computing systems that are reflective and self-adaptive. In this paper we draw upon our experience of this field to discuss how we can attempt to evaluate autonomic systems. By looking at the diverse systems that describe themselves as autonomic, we provide an introduction to the concepts of autonomic computing and describe some achievements that have already been made. We then discuss this work in terms of what is necessary to evaluate and compare such systems. We conclude with a set of metrics, which we believe are useful to evaluate autonomicity.

When ubiquitous computing devices access a contextawareness service, such as a location service, they need some assurance that the quality of the information received is trustworthy. However, the trustworthiness of a service cannot be determined by the service itself, but must be decided externally to the service. Furthermore, the trustworthiness of a service provider may be dynamic, depending on current environmental conditions.We propose a learning model that uses binary positive/negative feedback from service consumers and crossvalidation with other service providers to adjust the dynamic trustworthiness of a service provider.

In Autonomic Computing, an application needs to be aware of its environment. While the term "environment" is not normally understood as being a physical environment, in Pervasive Computing many applications do actually need to monitor the physical environment in which they are deployed. Monitoring the environment often includes gathering information about the people working or living in this environment. Applications that self-adapt to changes in the monitored environment are known as context-aware. The environment is monitored using sensors, such as temperature, humidity, location sensors, etc., and use some form of logic to abduce a context. As the input of this context logic is environment sensor data, testing these applications usually requires deployment at a physical test location, often in a research laboratory. Our project aims to design a simulation model of contexts as a means to test the context logic of a context-aware application, by allowing sensor data to be produced from a description of contexts, i.e. the location and activities of people in this location, thereby allowing initial testing of a context-aware application without requiring physical deployment.