The papers in this special issue represent an early view on different aspects of a new research field in computing science. They were presented as best papers in a series of European workshops called ‘Self-adaptive and autonomic systems’ (SAACS), which is co-located with the DEXA conference every year.

Like the autonomic responses in the human body, autonomic computing systems recognize their own health problems and, where possible, respond to correct them. Failing that, external help is required. The purpose of this paper is to consider how autonomic systems might be structured to facilitate health monitoring. The approach uses a ‘pulse’ monitor for each autonomic element, which provides a reflex reaction facility and basic information on the current state (health) of that element. The pulse mechanism extends the NASA beacon monitor concept. The different ways that pulse information might be communicated and used are examined. The discussion is illustrated with a personal computing example.

This paper presents an empirical investigation of policy-based self-management techniques for parallel applications executing in loosely-coupled environments. The dynamic and heterogeneous nature of these environments is discussed and the special considerations for parallel applications are identified. An adaptive strategy for the run-time deployment of tasks of parallel applications is presented. The strategy is based on embedding numerous policies which are informed by contextual and environmental inputs. The policies govern various aspects of behaviour, enhancing flexibility so that the goals of efficiency and performance are achieved despite high levels of environmental variability. A prototype self-managing parallel application is used as a vehicle to explore the feasibility and benefits of the strategy. In particular, several aspects of stability are investigated. The implementation and behaviour of three policies are discussed and sample results examined.

This paper introduces Rudder, a peer-to-peer agent framework for supporting autonomic applications in decentralized distributed environments. The framework provides agents to discover, select, and compose elements, and defines agent interaction and negotiation protocols to enable appropriate application behaviors to be negotiated and enacted dynamically. The implementations of these protocols as well as agent coordination and negotiation activities are supported by Comet, a scalable decentralized coordination substrate. The operation and experimental evaluation of Rudder is presented.

This paper presents Collaborative Reinforcement Learning (CRL), a coordination model for online system optimization in decentralized multi-agent systems. In CRL system optimization problems are represented as a set of discrete optimization problems, each of whose solution cost is minimized by model-based reinforcement learning agents collaborating on their solution. CRL systems can be built to provide autonomic behaviours such as optimizing system performance in an unpredictable environment and adaptation to partial failures. We evaluate CRL using an ad hoc routing protocol that optimizes system routing performance in an unpredictable network environment.

Self-managing or autonomic computing is a highly emerging field having been championed by industry and academia alike. Essentially the management of the system is being handed to the system itself, therefore the issue of trust in terms of the decisions the system makes regarding itself, is of paramount interest. This paper considers four aspects of trust in self-adaptive computing systems with the aim to identify trends and pinpoint areas that require more investigation.

Simulation has long played a part in testing new configurations and new functionality in a diverse range of software. Through such simulations, the boundaries of the system state are explored and the relationship of that state to other applications tested — sometimes to destruction. A critical differentiator between a simulation and a live, deployed application is that simulations are allowed to fail. As truly autonomous applications evolve, this capacity for simulation must be built in from the ground up or the benefits of experience — including the ability to tolerate failure — will be lost. This must be achieved without undermining the global correctness of visible application behaviour. We suggest an engineering approach to enable the introduction of such simulation with minimal or no recoding and we propose a composition architecture to allow for safe dynamic deployment in substantial autonomic systems. We have identified our approach as application Dreaming.

Today, management and tuning questions are approached using if… then… rules of thumb. This reactive approach requires expertise regarding system behavior, making it difficult to deal with unforeseen uses of a system’s resources and leading to system unpredictability and large system management overheads. We propose a What…if… approach that allows interactive exploration of the effects of system changes, thus converting complex tuning problem into simpler search problems. Through two concrete management problems, automating system upgrades and deciding on service migrations, we identify system design changes that enable a system to answer What…if… questions about itself.