The concept of autonomic computing seeks to reduce the complexity of pervasively ubiquitous system management and maintenance by shifting the responsibility for low-level tasks from humans to the system while allowing humans to concentrate on high-level tasks. This is achieved by building self-managing systems that are generally capable of self-configuring, self-healing, self-optimising, and self-protecting. Trustworthy autonomic computing technologies are being applied in datacentre and cloud management, smart cities and autonomous systems including driverless cars.
However, there are still significant challenges to achieving trustworthiness. This book covers challenges and solutions in autonomic computing trustworthiness from methods and techniques to achieve consistent and reliable system self-management. Researchers, developers and users need to be confident that an autonomic self-managing system will remain correct in the face of any possible contexts and environmental inputs.
The book is aimed at researchers in autonomic computing, autonomics and trustworthy autonomics. This will be a go-to book for foundational knowledge, proof of concepts and novel trustworthy autonomic techniques and approaches. It will be useful to lecturers and students of autonomic computing, autonomics and multi-agent systems who need an easy-to-use text with sample codes, exercises, use-case demonstrations. This is also an ideal tutorial guide for independent study with simple and well documented diagrams to explain techniques and processes.
Author(s): Thaddeus Eze
Series: IET Computing Series, 30
Publisher: The Institution of Engineering and Technology
Year: 2023
Language: English
Pages: 263
City: London
Contents
About the Author
Preface
Acknowledgments
1 Trustworthy autonomics primer
1.1 Introduction to autonomic computing
1.1.1 Autonomic computing definitions
1.1.2 Autonomic functionalities
1.1.3 The autonomic computing system
1.2 Foundations of trustworthy autonomics
1.2.1 Towards trustworthy autonomics
1.2.2 Pillars of trustworthy autonomic systems
1.3 Conclusion
2 Evolution of autonomic computing
2.1 Importance of understanding the evolution of autonomic computing
2.2 Autonomic architecture
2.3 Autonomic computing: trends and direction
2.3.1 Background
2.3.2 Autonomic computing in the first decade
2.3.3 Autonomic computing in the second decade
2.3.4 First and second decades of autonomic computing research at a glance
2.4 Trends, direction and open challenges
2.4.1 Trends and direction
2.4.2 Open challenges
2.5 Conclusion
3 Autonomic enabling techniques
3.1 About autonomic enabling techniques
3.2 Simple exponential smoothing
3.2.1 Implementing an SES using python
3.2.2 Basic implementation of an SES using microsoft excel sheet
3.2.3 Implementing SES in autonomic computing
3.3 Dead-zone logic
3.3.1 Implementing dead-zone logic in autonomic computing
3.4 Stigmergy
3.4.1 Natural stigmergy: wildlife
3.4.2 Natural stigmergy: humans
3.4.3 Stigmergy in autonomic systems
3.5 Policy autonomics
3.5.1 Policy-based networking
3.5.2 Policy-based autonomics
3.6 Utility function
3.6.1 UF in autonomic systems
3.7 Fuzzy logic
3.7.1 Moving vehicle case example
3.7.2 Fuzzy logic controller
3.7.3 Fuzzy logic in autonomic system
3.8 Autonomic nervous system
3.9 Combining autonomic techniques
3.10 Conclusion
4 Trustworthy autonomic computing
4.1 About trustworthy autonomic computing
4.2 Trustworthy autonomic computing vs trusted computing
4.3 Trustworthy autonomic architecture
4.3.1 TrAArch framework
4.3.2 Overview of the TrAArch architecture components
4.3.3 Other relevant [early] architectures
4.4 Conclusion
5 Trustworthy autonomic architecture implementations
5.1 Case example scenario 1: autonomic marketing system
5.1.1 Experimental environment
5.1.2 Results and evaluation
5.2 Case example scenario 2: self-adapting resource allocation
5.2.1 TrAArch simulator
5.2.2 Experimental environment
5.2.3 Simulation
5.2.4 Results and Analysis
5.3 Stability versus optimality
5.4 Conclusion
6 Multi-agent interoperability
6.1 Introduction to multi-agent interoperability
6.2 Multi-agent systems and multi-agent coordination
6.3 A review of autonomic interoperability solutions
6.4 The architecture-based interoperability
6.4.1 Scheduling and resource allocation
6.5 Complex interactions in multi-manager scenario
6.5.1 Simulation design
6.5.2 Autonomic manager logic
6.5.3 Simulation scenarios and metrics
6.5.4 Results analysis
6.6 Conclusion
7 Level of autonomicity
7.1 Introduction to level of autonomicity
7.2 Measuring LoA
7.2.1 Autonomic measuring metrics
7.2.2 Normalisation and scaling of autonomic metrics dimensions
7.3 Methodology for measuring LoA
7.3.1 A specific case method
7.3.2 A generic case method
7.4 Evaluating autonomic systems
7.5 Conclusion
8 Conclusions and future work
8.1 A case for trustworthy autonomics
8.2 The autonomic computing state of the art
8.3 Techniques that power autonomic computing
8.4 Trustworthy autonomic architecture
8.5 Interoperability
8.6 Level of autonomicity (LoA)
8.7 Future work
References
Index
