The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the runtime reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions. In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximized when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organizes into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimize any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes.

This report describes the tools developed by IT Innovation for quantitative comparison of preservation strategies. The tools have been open sourced and are publicly available on a website which includes documentation and features for bug reporting and new functionality requests.

Long-term retention and access to audiovisual (AV) assets as part of a preservation strategy inevitably involve some form of compromise in order to achieve acceptable levels of cost, throughput, quality, and many other parameters. Examples include quality control and throughput in media transfer chains; data safety and accessibility in digital storage systems; and service levels for ingest and access for archive functions delivered as services. We present new software tools and frameworks developed in the PrestoPRIME project that allow these compromises to be quantitatively assessed, planned, and managed for file-based AV assets. Our focus is how to give an archive an assurance that when they design and operate a preservation strategy as a set of services, it will function as expected and will cope with the inevitable and often unpredictable variations that happen in operation. This includes being able to do cost projections, sensitivity analysis, simulation of "disaster scenarios," and to govern preservation services using service-level agreements and policies.

This article describes the challenges of selecting a storage strategy for long-term retention and access of digital content and how some of the tools developed in the PrestoPRIME project are designed to help.

This report addresses risk representation, identification, assessment and forecasting in online communities. The goal is to set the corner stone for a framework that enables proactive management of risks and opportunities. The research results shown in this document cover risk evaluation techniques as well as risk management principles. This report is deliverable D1.1.

A major challenge within open markets is the ability to satisfy service demand with an adequate supply of service providers, especially when such demand may be volatile due to changing requirements, or fluctuations in the availability of services. Ideally, this supply and demand should be balanced; however, when consumer demand changes over time, and providers independently choose which services they provide, a coordination problem known as 'herding' can arise bringing instability to the market. This behavior can emerge when consumers share similar preferences for the same providers, and thus compete for the same resources. Likewise, providers which share estimates of fluctuating demand may respond in unison, withdrawing some services to introduce others, and thus oscillate the available supply around some ideal equilibrium. One approach to avoid this unstable behavior is to limit the flow of information between agents, such that they possess an incomplete and subjective view of the local service availability. We propose a model of an adaptive service-offering mechanism, in which providers adapt their choice of services offered to consumers, based on perceived demand. By varying the volume of information shared by agents, we demonstrate that a co-adaptive equilibrium can be achieved, thus avoiding the herding problem. As the knowledge that agents possess is limited, they self-organise into community structures that support locally shared information. We demonstrate that such a model is capable of reducing instability in service demand and thus increase utility (based on successful service provision) by up to 59%, when compared to the use of globally available information.

In this paper we describe a model of a decentralised system in which there may be no global information repository and the demand for particular resources changes over time. Agents must therefore organise the information that is available to them locally in order to adaptively respond to changes in demand in an efficient manner. Where previous studies have explored the role of system heterogeneity brought about by limiting knowledge or using decision procedures that diversify agent behaviour, here we focus specifically on how varying the amount of information available to agents affects the flow of information. By conducting a thorough evaluation of our model, the results demonstrated that when agents possessed only a limited awareness of their peers within their neighborhood, they exhibited self-organising behaviour resulting in the emergence of community structures that support locally shared information. By limiting the quantity of information shared by constraining an agent's memory size, we found that a stable local community behavior emerged that was robust and efficient, and also adaptive when the whole agent population was exposed to global fluctuations in service demand.

In this paper we explore the relationship between local and global behaviour in a simple model of utility com- puting infrastructure as the system heterogeneity, load an d re- liability are varied. To do this, we implement minimally com- plex agent strategies for which we can identify the fundamen- tal generic feedback underlying system behaviour. Such feed- back must be balanced by any utility computing infrastructure if decentralised control is to become an effective techniqu e for preserving stable functionality.

Recently, computer scientists have begun to build computational ecosystems in which multiple autonomous agents interact locally to achieve globally efficient organised behaviour. Here we present a thermodynamic interpretation of these systems. We highlight the difference between the regular use of terms such as energy and work, and their use within a thermodynamic framework. We explore the way in which this perspective might influence the design and management of such systems.

In response to the advent of new computational infrastructures, a number of initiatives, such as autonomic computing [5] and utility computing [8], have been announced by major IT vendors sharing the same underlying principles of provisioning distributed computational resources to a large number of users "on demand". Since, by their nature, such systems are large, open and dynamic, allocation of resources to users presents unique challenges that threaten to overwhelm existing centralised management approaches [2].

As the technical infrastructure to support Grid environments matures, attention must be focused on integrating such technical infrastructure with technologies to support more dynamic access to services, and ensuring that such access is appropriately monitored and secured. Current approaches for securing organisations through conventional firewalls are insufficient; access is either enabled or disabled for a given port, whereas access to Grid services may be conditional on dynamic factors. This paper reports on the Semantic Firewall (SFW) project, which investigated a policy-based security mechanism responsible for mediating interactions with protected services given a set of dynamic access policies, which define the conditions in which access may be granted to services. The aims of the project are presented, and results and contributions described.

This paper introduces the concept of services with ancillary behav- iour and illustrates the use of OWL-S to semantically describe them. The OWL- S syntax used reflects the dynamic and core-function independent nature of an- cillary behaviour. The approach is illustrated on the case of a ubiquitous com- puting system designed to offer care in the home of a cardiac patient. Here one of the challenges is to ensure service availability, team awareness and transac- tion atomicity. The concept of commitment is discussed as an example of ancil- lary behaviour that can achieve these requirements.

This paper introduces the concept of services with ancillary behaviour and illustrates the use of OWL-S to semantically describe them. The OWL-S syntax used reflects the dynamic and core-function independent nature of ancillary behaviour. The approach is illustrated on the case of a ubiquitous computing system designed to offer care in the home of a cardiac patient. Here one of the challenges is to ensure service availability, team awareness and transaction atomicity. The concept of commitment is discussed as an example of ancillary behaviour that can achieve these requirements