The Experts below are selected from a list of 7842 Experts worldwide ranked by ideXlab platform
Jeffrey O. Kephart - One of the best experts on this subject based on the ideXlab platform.
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Autonomic Computing: The First Decade
Proceedings of the 8th ACM international conference on Autonomic computing - ICAC '11, 2011Co-Authors: Jeffrey O. KephartAbstract:This talk provides a retrospective on the first decade of Autonomic Computing, an assessment of the extent to which the original vision has been realized, and some discussion and speculation about the the remaining research challenges. Nearly a decade has elapsed since Paul Horn, IBM's senior vice president of research, introduced the concept of Autonomic Computing during a keynote address to the National Academy of Engineers at Harvard University in October, 2001. Warning of a looming crisis arising from the ever-growing complexity of managing IT infrastructures, he enjoined the academic and industrial research communities to develop large-scale Computing systems analogous to the Autonomic nervous system, which subconsciously controls many of the body's muscles and organs. Horn envisioned that, just as the autonomous nervous system frees the conscious mind from the burden of increasing our heart rate and respiration rate when we exercise, an Autonomic Computing system would free system administrators from the burden of tuning database performance or mining multiple log files full of arcane messages to diagnose why an application is suddenly failing, enabling them instead to specify desired behavior through high-level policies. This vision was promoted and elucidated further in The Vision of Autonomic Computing, which appeared in IEEE Computer in January, 2003. That article described some of the capabilities that Autonomic Computing systems and their elements ought to possess, such as the ability to configure, heal, protect and optimize themselves, suggested a high-level multi-agent architecture, and laid out a number of key engineering and science research challenges that would require the combined efforts of experts from many different disciplines to solve. The worldwide research community quickly embraced Autonomic Computing. In June 2003, the Algorithms and Architectures for Self-managing Systems Workshop at the Federated Computing Research Conference and the Active Middleware Workshop on Autonomic Computing drew about 70 participants each. This strong showing of interest and enthusiasm prompted the organizers to combine forces and establish the International Conference on Autonomic Computing (ICAC), which is now in its eighth year. Since this time, dozens of workshops and conferences pertaining to the topic have been held. Several of the leading systems conferences explicitly call for papers on Autonomic Computing, including Eurosys, the Network Operations and Management Symposium, Integrated Management, the International Conference on Network and Service Management, and the International Symposium on Reliable Distributed Systems. Thousands of papers have been written on the topic, and Autonomic Computing has worked its way into course curricula at several universities. What has been the overall impact of this activity? In terms of the technical approach taken, few researchers have seriously attempted to exploit the original analogy to the Autonomic nervous system, and indeed only a handful of workshops have been devoted to exploring Autonomic Computing technologies that have any basis in biology. On the other hand, the community has made good progress in using utility functions to specify high-level policies, and using control-theoretic and machine learning approaches to achieve self-optimization or self-diagnosis in specific contexts. In terms of emphasis, it is disappointing but understandable that the preponderance of work in the field continues to focus on self-optimization, with self-healing and self-configuration receiving far less attention. In terms of the domain of application, the data center has emerged as one of the primary realms of interest to the community, especially the subset that tends to frequent ICAC, and in recent years that interest has expanded from just the IT infrastructure to include the physical infrastructure as well, as evidenced by the number of ICAC papers on data center energy management. Overall, it appears that some reasonable progress has been made on making elements of Computing systems more Autonomic, and in introducing them to the marketplace. However, fundamental scientific and engineering issues remain to be conquered, especially those pertaining to effective interactions among Autonomic elements. This talk will conclude with a discussion of what are some of the greatest remaining hurdles that must be cleared before we will see true data-center-scale Autonomic Computing systems.
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ICAC - Autonomic Computing: the first decade
Proceedings of the 8th ACM international conference on Autonomic computing - ICAC '11, 2011Co-Authors: Jeffrey O. KephartAbstract:This talk provides a retrospective on the first decade of Autonomic Computing, an assessment of the extent to which the original vision has been realized, and some discussion and speculation about the the remaining research challenges. Nearly a decade has elapsed since Paul Horn, IBM's senior vice president of research, introduced the concept of Autonomic Computing during a keynote address to the National Academy of Engineers at Harvard University in October, 2001. Warning of a looming crisis arising from the ever-growing complexity of managing IT infrastructures, he enjoined the academic and industrial research communities to develop large-scale Computing systems analogous to the Autonomic nervous system, which subconsciously controls many of the body's muscles and organs. Horn envisioned that, just as the autonomous nervous system frees the conscious mind from the burden of increasing our heart rate and respiration rate when we exercise, an Autonomic Computing system would free system administrators from the burden of tuning database performance or mining multiple log files full of arcane messages to diagnose why an application is suddenly failing, enabling them instead to specify desired behavior through high-level policies. This vision was promoted and elucidated further in The Vision of Autonomic Computing, which appeared in IEEE Computer in January, 2003. That article described some of the capabilities that Autonomic Computing systems and their elements ought to possess, such as the ability to configure, heal, protect and optimize themselves, suggested a high-level multi-agent architecture, and laid out a number of key engineering and science research challenges that would require the combined efforts of experts from many different disciplines to solve. The worldwide research community quickly embraced Autonomic Computing. In June 2003, the Algorithms and Architectures for Self-managing Systems Workshop at the Federated Computing Research Conference and the Active Middleware Workshop on Autonomic Computing drew about 70 participants each. This strong showing of interest and enthusiasm prompted the organizers to combine forces and establish the International Conference on Autonomic Computing (ICAC), which is now in its eighth year. Since this time, dozens of workshops and conferences pertaining to the topic have been held. Several of the leading systems conferences explicitly call for papers on Autonomic Computing, including Eurosys, the Network Operations and Management Symposium, Integrated Management, the International Conference on Network and Service Management, and the International Symposium on Reliable Distributed Systems. Thousands of papers have been written on the topic, and Autonomic Computing has worked its way into course curricula at several universities. What has been the overall impact of this activity? In terms of the technical approach taken, few researchers have seriously attempted to exploit the original analogy to the Autonomic nervous system, and indeed only a handful of workshops have been devoted to exploring Autonomic Computing technologies that have any basis in biology. On the other hand, the community has made good progress in using utility functions to specify high-level policies, and using control-theoretic and machine learning approaches to achieve self-optimization or self-diagnosis in specific contexts. In terms of emphasis, it is disappointing but understandable that the preponderance of work in the field continues to focus on self-optimization, with self-healing and self-configuration receiving far less attention. In terms of the domain of application, the data center has emerged as one of the primary realms of interest to the community, especially the subset that tends to frequent ICAC, and in recent years that interest has expanded from just the IT infrastructure to include the physical infrastructure as well, as evidenced by the number of ICAC papers on data center energy management. Overall, it appears that some reasonable progress has been made on making elements of Computing systems more Autonomic, and in introducing them to the marketplace. However, fundamental scientific and engineering issues remain to be conquered, especially those pertaining to effective interactions among Autonomic elements. This talk will conclude with a discussion of what are some of the greatest remaining hurdles that must be cleared before we will see true data-center-scale Autonomic Computing systems.
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Engineering decentralized Autonomic Computing systems
Proceeding of the second international workshop on Self-organizing architectures - SOAR '10, 2010Co-Authors: Jeffrey O. KephartAbstract:A central challenge of Autonomic Computing is to enable large-scale Computing systems--and the self-managing elements of which they are composed--to manage themselves in accordance with high-level objectives specified by people [2]. From the earliest days of Autonomic Computing, utility functions have been promoted by many authors [1, 4, 5, 6, 7, 9] as a powerful and principled means for representing high-level objectives. Once a utility function has been elicited, a combination of modeling, optimization, and (sometimes) learning techniques may be applied to this explicit mathematical representation of the objectives to determine an optimal solution for the control variables [3]. Since Autonomic systems are by their nature large, complex, and decentralized [8], issues of decentralization deeply affect every phase of the utility function approach: elicitation, modeling, optimization, learning, and sensing and actuation. I will describe several Autonomic systems that my colleagues and I have built at IBM Research, some of which have been commercialized. Each has employed a different mechanism for coordinating the actions of multiple auto-nomic managers to realize a system-wide objective, or a collection of individual objectives. Our most recent efforts have centered around reducing energy consumption in data centers, a problem that requires us to consider not just energy, but performance, availability, and other management issues as well. While from a purely academic perspective it is very tempting to formulate data center management as a single centralized optimization, the reality is that the scale and heterogeneity of the physical and IT infrastructure, the wide range of relevant spatial and temporal scales, and the mixture of several types of management concerns renders a centralized approach completely impractical. I will detail two distinct decentralized approaches that we have explored, one based on a federation of semi-autonomous managers with some centralized computation, and the other based on a more strongly decentralized market economy of resource consumers and producers. The ultimate grand unified theory of Autonomic Computing still eludes us, but I will offer insights gleaned from our experiences, and conclude with thoughts about the many remaining challenges of engineering decentralized Autonomic Computing systems.
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Agents and Service-Oriented Computing for Autonomic Computing: A Research Agenda
IEEE Internet Computing, 2009Co-Authors: Frances M. T. Brazier, Jeffrey O. Kephart, H. Van Dyke Parunak, Michael N. HuhnsAbstract:Autonomic Computing is the solution proposed to cope with the complexity of today's Computing environments. Self-management, an important element of Autonomic Computing, is also characteristic of single and multiagent systems, as well as systems based on service-oriented architectures. Combining these technologies can be profitable for all - in particular, for the development of Autonomic Computing systems.
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Guest Editors' Introduction: Autonomic Computing
IEEE Internet Computing, 2007Co-Authors: Daniel A. Menascé, Jeffrey O. KephartAbstract:Autonomic Computing systems are self-monitoring, self-tuning, self-organizing, self-optimizing, self-healing, and self-protecting, and they can address quality of-service, failure-recovery, and security issues with minimal human intervention.
John Leaney - One of the best experts on this subject based on the ideXlab platform.
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defining Autonomic Computing a software engineering perspective
Australian Software Engineering Conference, 2005Co-Authors: P Lin, A Macarthur, John LeaneyAbstract:As a rapidly growing field, Autonomic Computing is a promising new approach for developing large scale distributed systems. However, while the vision of achieving self-management in Computing systems is well established, the field still lacks a commonly accepted definition of 'what' an Autonomic Computing system is. Without a common definition to dictate the direction of development, it is not possible to know whether a system or technology is a part of Autonomic Computing, or if in fact an Autonomic Computing system has already been built. The purpose of this paper is to establish a standardised and quantitative definition of Autonomic Computing through the application of the quality metrics framework described in IEEE Std 1061-1998. Through the application of this methodology, stakeholders were systematically analysed and evaluated to obtain a balanced and structured definition of Autonomic Computing. This definition allows for further development and implementation of quality metrics, which are project-specific, quantitative measurements that can be used to validate the success of future Autonomic Computing projects.
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Australian Software Engineering Conference - Defining Autonomic Computing: a software engineering perspective
2005 Australian Software Engineering Conference, 1Co-Authors: P Lin, A Macarthur, John LeaneyAbstract:As a rapidly growing field, Autonomic Computing is a promising new approach for developing large scale distributed systems. However, while the vision of achieving self-management in Computing systems is well established, the field still lacks a commonly accepted definition of 'what' an Autonomic Computing system is. Without a common definition to dictate the direction of development, it is not possible to know whether a system or technology is a part of Autonomic Computing, or if in fact an Autonomic Computing system has already been built. The purpose of this paper is to establish a standardised and quantitative definition of Autonomic Computing through the application of the quality metrics framework described in IEEE Std 1061-1998. Through the application of this methodology, stakeholders were systematically analysed and evaluated to obtain a balanced and structured definition of Autonomic Computing. This definition allows for further development and implementation of quality metrics, which are project-specific, quantitative measurements that can be used to validate the success of future Autonomic Computing projects.
Ada Diaconescu - One of the best experts on this subject based on the ideXlab platform.
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Autonomic Computing - Principles, Design and Implementation
2013Co-Authors: Philippe Lalanda, Mccann Julie, Ada DiaconescuAbstract: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.
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Autonomic Computing: Principles, Design and Implementation
2013Co-Authors: Philippe Lalanda, Julie A Mccann, Ada DiaconescuAbstract:This textbook 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. 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 systems; reviews the most relevant sources of inspiration for Autonomic Computing, with pointers towards more extensive specialty literature.
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Sources of Inspiration for Autonomic Computing
Undergraduate Topics in Computer Science, 2013Co-Authors: Philippe Lalanda, Julie A Mccann, Ada DiaconescuAbstract:Autonomic Computing can capitalise on advancements available from several scientific fields, both within and beyond the computer science domain. This chapter provides an overview of such fields and highlights their possible contributions to Autonomic Computing systems. The manner in which concepts, mechanisms and processes can be adopted and reused as software engineering approaches is highlighted across this chapter.
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Future of Autonomic Computing and Conclusions
Autonomic Computing, 2013Co-Authors: Philippe Lalanda, Julie A Mccann, Ada DiaconescuAbstract:The purpose of this last chapter is twofold. First, it draws together the lessons we have learned about Autonomic Computing and the techniques that are used, at the time of writing, to design and implement self-managed software systems. Our purpose is clearly to help readers to understand, develop and maintain Autonomic systems.The second objective of this concluding chapter is to look ahead and foresee the future of Autonomic Computing, while also attempting to point out some of the most important challenges to address in order to attain the full Autonomic Computing vision. To achieve this risky exercise, we view the topic from the perspective of how Autonomic systems will be engineered and how assurances regarding their behaviours can be made. We acknowledge that targeting system-level autonomy will presumably necessitate integrated solutions, incorporating multiple Autonomic elements, each one dealing with different management concerns and operating at various granularity levels. In this context, we provide some examples of the more specialised fields of Autonomic networking and Autonomic machines. We also have a discussion about next-generation software engineering techniques, approaches and tools that would be required to meet future Computing system requirements.
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Autonomic Computing
Undergraduate Topics in Computer Science, 2013Co-Authors: Philippe Lalanda, Julie A Mccann, Ada DiaconescuAbstract:International audienceAutonomic 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
Salim Hariri - One of the best experts on this subject based on the ideXlab platform.
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Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
2013Co-Authors: Salim Hariri, Alan SillAbstract:On behalf of the organizing and program committees, we welcome you to the International Conference on Cloud and Autonomic Computing (CAC 2013). The initial conference in this new series is being held in Miami, Florida, a city rich in culture, history, finance, commerce, technology, and fourth-largest urban area in the United States. The CAC 2013 Conference, organized in cooperation with the Association for Computing Machinery (ACM), is a spin-off of the previous and ongoing International Conference on Autonomic Computing with an additional emphasis on cloud topics to address the emerging cyberspace and cloud technology aspects of Autonomic Computing that we strongly believe will become pervasive and ubiquitous and that will eventually grow to touch all aspects of life and economic activity. As these new technologies start to be developed and deployed, we are experiencing major research and technological challenges in how we manage and secure cyberspace resources and services. The Cloud and Autonomic Computing Conference series will be the main international forum to present the latest research on the design, implementation, evaluation, and use of cloud and Autonomic systems and services. This conference, the kickoff event of the new series, will focus on four important areas: Autonomic Cloud Computing, Autonomics for Extreme Scales, Autonomic Cybersecurity, and Autonomic Computing Tools and Applications. We are looking forward to lively discussions about CAC emerging technologies, applications and their challenges.
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Autonomic Computing: Concepts, Infrastructure, and Applications - Autonomic Computing : Concepts, Infrastructure, and Applications
2006Co-Authors: Manish Parashar, Salim HaririAbstract:THE Autonomic Computing PARADIGM Overview of Autonomic Computing: Origins, Evolution, Direction Alan Ganek A Requirements Engineering Perspective on Autonomic Systems Development David W. Bustard and Roy Sterritt Autonomic Computing: A System-Wide Perspective Robbert van Renesse and Kenneth P. Birman Autonomic Grid Computing: Concepts, Requirements, and Infrastructure Manish Parashar Architecture Overview for Autonomic Computing John W. Sweitzer and Christine Draper SELF-* PROPERTIES - APPROACHES AND INFRASTRUCTURES A Taxonomy for Self-* Properties in Decentralized Autonomic Computing Tom De Wolf and Tom Holvoet Exploiting Emergence in Autonomic Systems Richard Anthony, Alun Butler, and Mohammed Ibrahim A Control-Based Approach to Autonomic Performance Management in Computing Systems Sherif Abdelwahed and Nagarajan Kandasamy Transparent Autonomization in Composite Systems S. Masoud Sadjadi and Philip K. McKinley Recipe-Based Service Configuration and Adaptation Peter Steenkiste and An-Cheng Huang ACHIEVING SELF-* PROPERTIES - ENABLING SYSTEMS, TECHNOLOGIES, AND SERVICES A Programming System for Autonomic Self-Managing Applications Hua Liu and Manish Parashar A Self-Configuring Service Composition Engine Thomas Heinis, Cesare Pautasso, and Gustavo Alonso Dynamic Collaboration in Autonomic Computing David M. Chess, James E. Hanson, Jeffrey O. Kephart, Ian Whalley, and Steve R. White AutoFlow: Autonomic Information Flows for Critical Information Systems Karsten Schwan, Brian F. Cooper, Greg Eisenhauer, Ada Gavrilovska, Matt Wolf, Hasan Abbasi, Sandip Agarwala, Zhongtang Cai, Vibhore Kumar, Jay Lofstead, Mohamed Mansour, Balasubramanian Seshasayee, and Patrick Widener Scalable Management - Technologies for Management of Large-Scale, Distributed Systems Robert Adams, Paul Brett, Subu Iyer, Dejan Milojicic, Sandro Rafaeli, and Vanish Talwar Platform Support for Autonomic Computing: A Research Vehicle Lenitra Durham, Milan Milenkovic, Phil Cayton, and Mazin Yousif REALIZATION OF SELF-* PROPERTIES Dynamic Server Allocation for Autonomic Service Centers in the Presence of Failures Daniel A. Menasce and Mohamed N. Bennani Effecting Runtime Reconfiguration in Managed Execution Environments Rean Griffith, Giuseppe Valetto, and Gail Kaiser Self-Organizing Scheduling on the Organic Grid Arjav Chakravarti, Gerald Baumgartner, and Mario Lauria Autonomic Data Streaming for High-Performance Scientific Applications Viraj Bhat, Manish Parashar, and Nagarajan Kandasamy Autonomic Power and Performance Management of Internet Data Bithika Khargharia and Salim Hariri Trace Analysis for Fault Detection in Application Servers Guofei Jiang, Haifeng Chen, Cristian Ungureanu, and Kenji Yoshihira Anomaly-Based Self Protection against Network Attacks Guangzhi Qu and Salim Hariri Index
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Autonomic Computing concepts infrastructure and applications
2006Co-Authors: Manish Parashar, Salim HaririAbstract:THE Autonomic Computing PARADIGM Overview of Autonomic Computing: Origins, Evolution, Direction Alan Ganek A Requirements Engineering Perspective on Autonomic Systems Development David W. Bustard and Roy Sterritt Autonomic Computing: A System-Wide Perspective Robbert van Renesse and Kenneth P. Birman Autonomic Grid Computing: Concepts, Requirements, and Infrastructure Manish Parashar Architecture Overview for Autonomic Computing John W. Sweitzer and Christine Draper SELF-* PROPERTIES - APPROACHES AND INFRASTRUCTURES A Taxonomy for Self-* Properties in Decentralized Autonomic Computing Tom De Wolf and Tom Holvoet Exploiting Emergence in Autonomic Systems Richard Anthony, Alun Butler, and Mohammed Ibrahim A Control-Based Approach to Autonomic Performance Management in Computing Systems Sherif Abdelwahed and Nagarajan Kandasamy Transparent Autonomization in Composite Systems S. Masoud Sadjadi and Philip K. McKinley Recipe-Based Service Configuration and Adaptation Peter Steenkiste and An-Cheng Huang ACHIEVING SELF-* PROPERTIES - ENABLING SYSTEMS, TECHNOLOGIES, AND SERVICES A Programming System for Autonomic Self-Managing Applications Hua Liu and Manish Parashar A Self-Configuring Service Composition Engine Thomas Heinis, Cesare Pautasso, and Gustavo Alonso Dynamic Collaboration in Autonomic Computing David M. Chess, James E. Hanson, Jeffrey O. Kephart, Ian Whalley, and Steve R. White AutoFlow: Autonomic Information Flows for Critical Information Systems Karsten Schwan, Brian F. Cooper, Greg Eisenhauer, Ada Gavrilovska, Matt Wolf, Hasan Abbasi, Sandip Agarwala, Zhongtang Cai, Vibhore Kumar, Jay Lofstead, Mohamed Mansour, Balasubramanian Seshasayee, and Patrick Widener Scalable Management - Technologies for Management of Large-Scale, Distributed Systems Robert Adams, Paul Brett, Subu Iyer, Dejan Milojicic, Sandro Rafaeli, and Vanish Talwar Platform Support for Autonomic Computing: A Research Vehicle Lenitra Durham, Milan Milenkovic, Phil Cayton, and Mazin Yousif REALIZATION OF SELF-* PROPERTIES Dynamic Server Allocation for Autonomic Service Centers in the Presence of Failures Daniel A. Menasce and Mohamed N. Bennani Effecting Runtime Reconfiguration in Managed Execution Environments Rean Griffith, Giuseppe Valetto, and Gail Kaiser Self-Organizing Scheduling on the Organic Grid Arjav Chakravarti, Gerald Baumgartner, and Mario Lauria Autonomic Data Streaming for High-Performance Scientific Applications Viraj Bhat, Manish Parashar, and Nagarajan Kandasamy Autonomic Power and Performance Management of Internet Data Bithika Khargharia and Salim Hariri Trace Analysis for Fault Detection in Application Servers Guofei Jiang, Haifeng Chen, Cristian Ungureanu, and Kenji Yoshihira Anomaly-Based Self Protection against Network Attacks Guangzhi Qu and Salim Hariri Index
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UPP - Autonomic Computing: an overview
Lecture Notes in Computer Science, 2005Co-Authors: Manish Parashar, Salim HaririAbstract:The increasing scale complexity, heterogeneity and dynamism of networks, systems and applications have made our computational and information infrastructure brittle, unmanageable and insecure. This has necessitated the investigation of an alternate paradigm for system and application design, which is based on strategies used by biological systems to deal with similar challenges – a vision that has been referred to as Autonomic Computing. The overarching goal of Autonomic Computing is to realize computer and software systems and applications that can manage themselves in accordance with high-level guidance from humans. Meeting the grand challenges of Autonomic Computing requires scientific and technological advances in a wide variety of fields, as well as new software and system architectures that support the effective integration of the constituent technologies. This paper presents an introduction to Autonomic Computing, its challenges, and opportunities.
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Autonomic Computing an overview
Lecture Notes in Computer Science, 2004Co-Authors: Manish Parashar, Salim HaririAbstract:The increasing scale complexity, heterogeneity and dynamism of networks, systems and applications have made our computational and information infrastructure brittle, unmanageable and insecure. This has necessitated the investigation of an alternate paradigm for system and application design, which is based on strategies used by biological systems to deal with similar challenges – a vision that has been referred to as Autonomic Computing. The overarching goal of Autonomic Computing is to realize computer and software systems and applications that can manage themselves in accordance with high-level guidance from humans. Meeting the grand challenges of Autonomic Computing requires scientific and technological advances in a wide variety of fields, as well as new software and system architectures that support the effective integration of the constituent technologies. This paper presents an introduction to Autonomic Computing, its challenges, and opportunities.
P Lin - One of the best experts on this subject based on the ideXlab platform.
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defining Autonomic Computing a software engineering perspective
Australian Software Engineering Conference, 2005Co-Authors: P Lin, A Macarthur, John LeaneyAbstract:As a rapidly growing field, Autonomic Computing is a promising new approach for developing large scale distributed systems. However, while the vision of achieving self-management in Computing systems is well established, the field still lacks a commonly accepted definition of 'what' an Autonomic Computing system is. Without a common definition to dictate the direction of development, it is not possible to know whether a system or technology is a part of Autonomic Computing, or if in fact an Autonomic Computing system has already been built. The purpose of this paper is to establish a standardised and quantitative definition of Autonomic Computing through the application of the quality metrics framework described in IEEE Std 1061-1998. Through the application of this methodology, stakeholders were systematically analysed and evaluated to obtain a balanced and structured definition of Autonomic Computing. This definition allows for further development and implementation of quality metrics, which are project-specific, quantitative measurements that can be used to validate the success of future Autonomic Computing projects.
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Australian Software Engineering Conference - Defining Autonomic Computing: a software engineering perspective
2005 Australian Software Engineering Conference, 1Co-Authors: P Lin, A Macarthur, John LeaneyAbstract:As a rapidly growing field, Autonomic Computing is a promising new approach for developing large scale distributed systems. However, while the vision of achieving self-management in Computing systems is well established, the field still lacks a commonly accepted definition of 'what' an Autonomic Computing system is. Without a common definition to dictate the direction of development, it is not possible to know whether a system or technology is a part of Autonomic Computing, or if in fact an Autonomic Computing system has already been built. The purpose of this paper is to establish a standardised and quantitative definition of Autonomic Computing through the application of the quality metrics framework described in IEEE Std 1061-1998. Through the application of this methodology, stakeholders were systematically analysed and evaluated to obtain a balanced and structured definition of Autonomic Computing. This definition allows for further development and implementation of quality metrics, which are project-specific, quantitative measurements that can be used to validate the success of future Autonomic Computing projects.
