Self-Managing Systems: A Control Theory Foundation (original) (raw)

A control theory foundation for self-managing computing systems

IEEE Journal on Selected Areas in Communications, 2000

The high cost of operating large computing installations has motivated a broad interest in reducing the need for human intervention by making systems self-managing. This paper explores the extent to which control theory can provide an architectural and analytic foundation for building self-managing systems. Control theory provides a rich set of methodologies for building automated self-diagnosis and self-repairing systems with properties such as stability, short settling times, and accurate regulation. However, there are challenges in applying control theory to computing systems, such as developing effective resource models, handling sensor delays, and addressing lead times in effector actions. We propose a deployable testbed for autonomic computing (DTAC) that we believe will reduce the barriers to addressing research problems in applying control theory to computing systems. The initial DTAC architecture is described along with several problems that it can be used to investigate.

A survey of autonomic computing - degrees, models, and applications

ACM Computing Surveys, 2008

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.

BASIC APPROACH TO AUTONOMIC COMPUTING

2023

The idea behind autonomic computing is to help solve software complexity crises which are the major impediment to advancement in the IT sector. Autonomic computing is a distributed computing resource that has self-managing capabilities [18]. The autonomic nervous system served as the inspiration for the self-management concept in autonomic computing and was first introduced in 2001 by IBM. The Autonomic computing system continuously checks and optimizes its status by adapting to changes and also makes use of high-level policies provided by humans to make adaptive decisions [20].

An architectural approach to autonomic computing

International Conference on Autonomic Computing, 2004. Proceedings., 2004

We describe an architectural approach to achieving the goals of autonomic computing. The architecture that we outline describes interfaces and behavioral requirements for individual system components, describes how interactions among components are established, and recommends design patterns that engender the desired system-level properties of selfconfiguration, self-optimization, self-healing and selfprotection. We have validated many of these ideas in two prototype autonomic computing systems.

Autonomic Computing Now You See It, Now You Don’t

Lecture Notes in Computer Science, 2009

With the rapid growth of web services and socio-technical ecosystems, the management complexity of these modern, decentralized, distributed computing systems presents significant challenges for businesses and often exceeds the capabilities of human operators. Autonomic computing is an effective set of technologies, models, architecture patterns, standards, and processes to cope with and reign in the management complexity of dynamic computing systems using feedback control, adaptation, and self-management. At the core of an autonomic system are control loops which sense their environment, model their behavior in that environment, and take action to change the environment or their own behavior. Computer science researchers often approach the design of such highly dynamical systems from a software architecture perspective whereas engineering researchers start with a feedback control perspective. In this article, we argue that both design perspectives are needed and necessary for autonomic system design.

Autonomic Computing Systems: Issues and Challenges

2008

— Nowadays, IT organizations have encountered growing challenges in the management and maintenance of large scale heterogeneous distributed computing systems because these systems attempt to be active and available at all hours. Moreover, current programming languages, methods, and management tools are inadequate to handle complexity, scale, dynamism, heterogeneity, and uncertainty as the most important challenges in such systems. Therefore, researchers investigate new ideas to address the problems created by IT complexity. One such idea is Autonomic Computing (AC). Autonomic Computing Systems (ACSs) are systems that manage themselves. This paper provides a thorough survey of autonomic computing systems, presenting their definitions, their characteristics and effects on quality factors, their architecture, issues, and challenges.

Towards autonomic computing systems

Engineering Applications of Artificial Intelligence, 2004

Firstly, an in-depth analysis is presented on the concept of autonomic computing and a generic architecture is put forward for autonomic computing systems. Then, a taxonomy is put forward for system adaptations and the concept and architecture are presented for each category of system adaptations. Thirdly, a brief survey is presented upon the states of the art of autonomic computing. Finally, a conclusion is drawn that the essence of autonomic computing is the automation of system adaptations for computing systems, which validates the synonymity between autonomic computing and adaptive computing, and comments are made upon the challenge of autonomic computing systems and the intrinsic differences between autonomic computing systems and conventional feedback control systems. r