Semantic-based policy engineering for autonomic systems

This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise

PAC-MEN: Personal Autonomic Computing Monitoring Environments

The overall goal of this research is to improve the `environment awareness' aspect of personal autonomic computing. Personal Computing offers unique challenges for self-management due to its multiequipment, multi-situation, and multi-user nature. The aim is to develop a support architecture for multiplatform working, based on autonomic computing concepts and techniques. Of particular interest is collaboration among personal systems to take a shared responsibility for environment awareness. Concepts mirroring human mechanisms, such as 'reflex reactions' and the use of 'vital signs' to assess operational health, are used in designing and implementing the personal computing architecture. A proof of concept self-healing tool is considered and lessons learned used for the requirements specification of the community-based environment awareness prototype environment---PACMEN (Personal Autonomic Computing Monitor ENvironment)

Autonomic Road Transport Support Systems

The work on Autonomic Road Transport Support (ARTS) presented here aims at meeting the challenge of engineering autonomic behavior in Intelligent Transportation Systems (ITS) by fusing research from the disciplines of traffic engineering and autonomic computing. Ideas and techniques from leading edge artificial intelligence research have been adapted for ITS over the last years. Examples include adaptive control embedded in real time traffic control systems, heuristic algorithms (e.g. in SAT-NAV systems), image processing and computer vision (e.g. in automated surveillance interpretation). Autonomic computing which is inspired from the biological example of the body’s autonomic nervous system is a more recent development. It allows for a more efficient management of heterogeneous distributed computing systems. In the area of computing, autonomic systems are endowed with a number of properties that are generally referred to as self-X properties, including self-configuration, self-healing, self-optimization, self-protection and more generally self-management. Some isolated examples of autonomic properties such as self-adaptation have found their way into ITS technology and have already proved beneficial. This edited volume provides a comprehensive introduction to Autonomic Road Transport Support (ARTS) and describes the development of ARTS systems. It starts out with the visions, opportunities and challenges, then presents the foundations of ARTS and the platforms and methods used and it closes with experiences from real-world applications and prototypes of emerging applications. This makes it suitable for researchers and practitioners in the fields of autonomic computing, traffic and transport management and engineering, AI, and software engineering. Graduate students will benefit from state-of-the-art description, the study of novel methods and the case studies provided

Autonomous platform for life-critical decision support in the ICU

Part 2: PhD Workshop: Autonomic Network and Service ManagementInternational audienceThe Intensive Care Unit is a complex, data-intensive and critical environment in which the adoption of Information Technology is growing. As physicians become more dependent on the computing technology to support decisions, raise real-time alerts and notifications of patient-specific conditions, this software has strong dependability requirements. The dependability challenges are expressed in terms of availability, reliability, performance, usability and maintenance of the system. Our research focuses on the design and development of a generic autonomous ICU service platform. COSARA is a computer-based platform for infection surveillance and antibiotic management in ICU. During its design, development and evaluation, we identified both technological and human factors that affect robustness. We presented the identified research questions that will be addressed in detail during PhD research

Autonomic Cloud Computing: A Review

The wide acceptability of cloud computing and its adoption, though remarkable, also gave the technology its greatest challenge in 'user expectation'. These challenges include; Reliability and availability, integration and interoperability, scalability, virtual machine migration policies, failure prediction, and resource management. In this paper, a general review of cloud computing was done highlighting its challenges. Autonomic Cloud computing was also reviewed. Future research areas were identified

Real-time transaction processing for autonomic grid application

The advances in computing and communication technologies and software have resulted in an explosive growth in computing systems and applications that impact all aspects of our life. Computing systems are expected to be effective and serve useful purpose when they are first introduced and continue to be useful as condition changes. With increase in complexity of systems and applications, their development, configuration, and management challenges are beyond the capabilities of existing tools and methodologies. So the system becomes unmanageable and insecure. So in order to make the systems selfmanageable and secure the concept of Autonomic computing is evolved. Autonomic computing offers a potential solution to these challenging research problems. It is inspired by nature and biological systems (such as the autonomic nervous system) that have evolved to cope with the challenges of scale, complexity, heterogeneity and unpredictability by being decentralized, context aware, adaptive and resilient. This new era of computing is driven by the convergence of biological and digital computing systems and is characterized by being self-defining, self-configuring, self-optimizing, self-protecting, self-healing, context aware and anticipatory. Autonomic computing is a new computing model to self manages computing systems with a minimal human interference. It provides an unprecedented level of self-regulation and hides complexity from Users. The Autonomic computing initiative is inspired by the human body’s autonomic nervous system. The autonomic nervous system monitors the heart- beats, checks blood sugar levels and maintains normal body temperature with out any conscious effort from the human. There is an important distinction between autonomic activity in the human body and autonomic responses in computer systems. Many of the decision made autonomic elements in computer systems make decisions based on tasks, which are chosen to be delegated to the technology. The influences of the autonomic nervous systems may imply that the autonomic computing initiative is concerned only with lowlevel self-managing capability such as reflex reaction. The basic application area of autonomic computing is grid computing. Both autonomic computing and grid computing are proposed as innovations of IT. Autonomic computing aims to present a solution to the rapidly increasing complexity crises in IT industry, as grid computing tries to share and integrate distributed computational resources and data resources. Basic aim is to implement the autonomic computing in grid related study like autonomic task distribution and handling in grids, and autonomic resource allocation. In this thesis paper we presents methods of calculating deadlines of global and local transaction And sub transaction by taking EDF algorithm and measure the performance by taking miss ratio in Different workload. We implement this work in an existing grid. The basic aim is to know autonomic computing better. It is a model to self manage computing Systems with minimal human interference. Self manage has properties like self-configuration, self-optimization, self-healing, self-protection. Autonomic grid computing combines autonomic computing with grid technologies to help companies to reduce the complexity associated with the grid system and hides the complexity from their grid user. Autonomic real-time transaction services incorporate fault tolerance into autonomic grid technology by automatically recovering systems from various failures. Here in this paper Deadlines of global transaction, sub transaction and local transaction are calculated by taking parameters arrival time, execution time, relative deadline, and slack time. We are taking a periodic transaction having λ (transaction arrival rate per second) Tasks are generated at different nodes with Poisson ratio with λ as workload. Miss ratio is the performance metrics. With increase in workload miss ratio first decreased and then rose. The reason was each sub transaction acted as a unit to compete for resources so that more workload the more system resource they consumed. So more transaction missed their deadlines, as they could not get enough resource in time. EDF algorithm has both less global and local miss ratios then other scheduling algorithm. If EDF is compare with FCFS or SJF or HPF it is apparent that both algorithms perform almost identically until no of transaction is low, then EDF misses fewer dead lines than other. Real-time transaction can handled by the grid in autonomic environment and satisfy properties of autonomic computing

A classification of emerging and traditional grid systems

The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings. However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area

Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator

Autonomic computing meets SCADA security

© 2017 IEEE. National assets such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks are critical infrastructures. The cyber threats to these infrastructures have increasingly become more sophisticated, extensive and numerous. Cyber security conventional measures have proved useful in the past but increasing sophistication of attacks dictates the need for newer measures. The autonomic computing paradigm mimics the autonomic nervous system and is promising to meet the latest challenges in the cyber threat landscape. This paper provides a brief review of autonomic computing applications for SCADA systems and proposes architecture for cyber security