Autonomic Computing
Lalanda, Philippe.
Autonomic Computing Principles, Design and Implementation / [electronic resource] : by Philippe Lalanda, Julie A. McCann, Ada Diaconescu. - XV, 288 p. 88 illus. online resource. - Undergraduate Topics in Computer Science, 1863-7310 . - Undergraduate Topics in Computer Science, .
Software Engineering to Autonomic Computing -- Autonomic Systems -- Sources of Inspiration for Autonomic Computing -- Autonomic Computing Architectures -- The Monitoring Function -- The Adaptation Function -- The Decision Function -- Evaluation Issues -- Autonomic Mediation in Cilia -- Future of Autonomic Computing and Conclusions -- Learning Environment.
Autonomic computing is changing the way software systems are being developed, introducing the goal of self-managed computing systems with minimal need for human input. This easy-to-follow, classroom-tested textbook/reference provides a practical perspective on autonomic computing. Through the combined use of examples and hands-on projects, the book enables the reader to rapidly gain an understanding of the theories, models, design principles and challenges of this subject while building upon their current knowledge; thus reinforcing the concepts of autonomic computing and self-management. Topics and features: Provides a structured and comprehensive introduction to autonomic computing with a software engineering perspective Supported by a downloadable learning environment and source code that allows students to develop, execute, and test autonomic applications at an associated website Presents the latest information on techniques implementing self-monitoring, self-knowledge, decision-making and self-adaptation Discusses the challenges to evaluating an autonomic system, aiding the reader in designing tests and metrics that can be used to compare autonomic computing systems Reviews the most relevant sources of inspiration for autonomic computing, with pointers towards more extensive specialty literature Ideal for a 10-week lecture programme This concise primer and practical guide will be of great use to students, researchers and practitioners alike, demonstrating how to better architect robust yet flexible software systems capable of meeting the computing demands for today and in the future.
9781447150077
10.1007/978-1-4471-5007-7 doi
Computer science.
Computer system failures.
Software engineering.
Artificial intelligence.
Computer Science.
Software Engineering/Programming and Operating Systems.
Artificial Intelligence (incl. Robotics).
System Performance and Evaluation.
QA76.758
005.1
Autonomic Computing Principles, Design and Implementation / [electronic resource] : by Philippe Lalanda, Julie A. McCann, Ada Diaconescu. - XV, 288 p. 88 illus. online resource. - Undergraduate Topics in Computer Science, 1863-7310 . - Undergraduate Topics in Computer Science, .
Software Engineering to Autonomic Computing -- Autonomic Systems -- Sources of Inspiration for Autonomic Computing -- Autonomic Computing Architectures -- The Monitoring Function -- The Adaptation Function -- The Decision Function -- Evaluation Issues -- Autonomic Mediation in Cilia -- Future of Autonomic Computing and Conclusions -- Learning Environment.
Autonomic computing is changing the way software systems are being developed, introducing the goal of self-managed computing systems with minimal need for human input. This easy-to-follow, classroom-tested textbook/reference provides a practical perspective on autonomic computing. Through the combined use of examples and hands-on projects, the book enables the reader to rapidly gain an understanding of the theories, models, design principles and challenges of this subject while building upon their current knowledge; thus reinforcing the concepts of autonomic computing and self-management. Topics and features: Provides a structured and comprehensive introduction to autonomic computing with a software engineering perspective Supported by a downloadable learning environment and source code that allows students to develop, execute, and test autonomic applications at an associated website Presents the latest information on techniques implementing self-monitoring, self-knowledge, decision-making and self-adaptation Discusses the challenges to evaluating an autonomic system, aiding the reader in designing tests and metrics that can be used to compare autonomic computing systems Reviews the most relevant sources of inspiration for autonomic computing, with pointers towards more extensive specialty literature Ideal for a 10-week lecture programme This concise primer and practical guide will be of great use to students, researchers and practitioners alike, demonstrating how to better architect robust yet flexible software systems capable of meeting the computing demands for today and in the future.
9781447150077
10.1007/978-1-4471-5007-7 doi
Computer science.
Computer system failures.
Software engineering.
Artificial intelligence.
Computer Science.
Software Engineering/Programming and Operating Systems.
Artificial Intelligence (incl. Robotics).
System Performance and Evaluation.
QA76.758
005.1