Autonomic Computing
Encyclopedia
Autonomic Computing refers to the self-managing characteristics of distributed computing resources, adapting to unpredictable changes whilst hiding intrinsic complexity to operators and users. Started by IBM
in 2001, this initiative's ultimate aim is to develop computer systems capable of self-management
, to overcome the rapidly growing complexity of computing systems management, and to reduce the barrier that complexity poses to further growth. An autonomic system makes decisions on its own, using high-level policies; it will constantly check and optimize its status and automatically adapt itself to changing conditions. As widely reported in literature, an autonomic computing framework might be seen composed by Autonomic Components (AC) interacting with each other http://sourceforge.net/project/showfiles.php?group_id=225956. An AC can be modeled in terms of two main control loops (local and global) with sensors (for self-monitoring), effectors (for self-adjustment), knowledge and planner/adapter for exploiting policies based on self- and environment awareness.
Driven by such vision, a variety of architectural frameworks based on “self-regulating” autonomic components has been recently proposed. A very similar trend has recently characterized significant research work in the area of multi-agent system
s. However, most of these approaches are typically conceived with centralized or cluster-based server architectures in mind and mostly address the need of reducing management costs rather than the need of enabling complex software systems or providing innovative services.
Autonomy-oriented computation is a paradigm proposed by Jiming Liu in 2001 that uses artificial systems imitating social animals' collective behaviours to solve hard computational problems. For example, ant colony optimization
could be studied in this paradigm.
Forecasts suggests that the number of computing devices in use will grow at 38% per annum and the average complexity of each device is increasing. Currently this volume and complexity is managed by highly skilled humans; but the demand for skilled IT personnel is already outstripping supply, with labour costs exceeding equipment costs by a ratio of up to 18:1. Computing systems have brought great benefits of speed and automation but there is now an overwhelming economic need to automate their maintenance.
In "The Vision of Autonomic Computing", Kephart and Chess warn that the dream of interconnectivity of computing systems and devices could become the “nightmare of pervasive computing” in which architects are unable to anticipate, design and maintain the complexity
of interactions. They state the essence of autonomic computing is system self-management, freeing administrators from low-level task management while delivering better system behavior.
A general problem of modern distributed computing systems is that their complexity
, and in particular the complexity of their management, is becoming a significant limiting factor in their further development. Large companies and institutions are employing large-scale computer network
s for communication and computation. The distributed applications running on these computer networks are diverse and deal with many different tasks, ranging from internal control processes to presenting web content and to customer support.
Additionally, Mobile computing
is pervading these networks at an increasing speed: employees need to communicate with their companies while they are not in their office. They do so by using laptop
s, PDAs
, or mobile phones with diverse forms of wireless
technologies to access their companies' data.
This creates an enormous complexity in the overall computer network which is hard to control manually by human operators. Manual control is time-consuming, expensive, and error-prone. The manual effort needed to control a growing networked computer-system tends to increase very quickly.
80% of such problems in infrastructure happen at the client specific application and database layer. Most 'autonomic' service providers guarantee only up to the basic plumbing layer (power, hardware, operating system, network and basic database parameters).
of the human body. This nervous system controls important bodily functions (e.g. respiration, heart rate, and blood pressure) without any conscious intervention.
In a self-managing
Autonomic System, the human operator takes on a new role: he or she does not control the system directly. Instead, he/she defines general policies and rules that serve as an input for the self-management process. For this process, IBM has defined the following four functional areas:
IBM defined five evolutionary levels, or the Autonomic deployment model, for its deployment: Level 1 is the basic level that presents the current situation where systems are essentially managed manually. Levels 2 - 4 introduce increasingly automated management functions, while level 5 represents the ultimate goal of autonomic, self-managing systems.
The design complexity of Autonomic Systems can be simplified by utilizing design patterns such as the Model View Controller (MVC) to improve concern separation by helping encapsulate functional concerns.
Theory. Essentially, a closed control loop in a self-managing system monitors some resource (software or hardware component) and autonomously tries to keep its parameters within a desired range.
According to IBM, hundreds or even thousands of these control loops are expected to work in a large-scale self-managing computer system.
A fundamental building block of an autonomic system is the sensing capability (Sensors Si), which enables the system to observe its external operational context. Inherent to an autonomic system is the knowledge of the Purpose (intention) and the Know-how to operate itself (e.g., bootstrapping, configuration knowledge, interpretation of sensory data, etc.) without external intervention. The actual operation of the autonomic system is dictated by the Logic, which is responsible for making the right decisions to serve its Purpose, and influence by the observation of the operational context (based on the sensor input).
This model highlights the fact that the operation of an autonomic system is purpose-driven. This includes its mission (e.g., the service it is supposed to offer), the policies (e.g., that define the basic behaviour), and the “survival instinct”. If seen as a control system this would be encoded as a feedback error function or in a heuristically assisted system as an algorithm combined with set of heuristics bounding its operational space.
Automatic: This essentially means being able to self-control its internal functions and operations. As such, an autonomic system must be self-contained and able to start-up and operate without any manual intervention or external help. Again, the knowledge required to bootstrap the system (Know-how) must be inherent to the system.
Adaptive: An autonomic system must be able to change its operation (i.e., its configuration, state and functions). This will allow the system to cope with temporal and spatial changes in its operational context either long term (environment customisation/optimisation) or short term (exceptional conditions such as malicious attacks, faults, etc.).
Aware: An autonomic system must be able to monitor (sense) its operational context as well as its internal state in order to be able to assess if its current operation serves its purpose. Awareness will control adaptation of its operational behaviour in response to context or state changes.
IBM
International Business Machines Corporation or IBM is an American multinational technology and consulting corporation headquartered in Armonk, New York, United States. IBM manufactures and sells computer hardware and software, and it offers infrastructure, hosting and consulting services in areas...
in 2001, this initiative's ultimate aim is to develop computer systems capable of self-management
Self-management (computer science)
Self-Management is the process by which computer systems shall manage their own operation without human intervention. Self-Management technologies are expected to pervade the next generation of network management systems....
, to overcome the rapidly growing complexity of computing systems management, and to reduce the barrier that complexity poses to further growth. An autonomic system makes decisions on its own, using high-level policies; it will constantly check and optimize its status and automatically adapt itself to changing conditions. As widely reported in literature, an autonomic computing framework might be seen composed by Autonomic Components (AC) interacting with each other http://sourceforge.net/project/showfiles.php?group_id=225956. An AC can be modeled in terms of two main control loops (local and global) with sensors (for self-monitoring), effectors (for self-adjustment), knowledge and planner/adapter for exploiting policies based on self- and environment awareness.
Driven by such vision, a variety of architectural frameworks based on “self-regulating” autonomic components has been recently proposed. A very similar trend has recently characterized significant research work in the area of multi-agent system
Multi-agent system
A multi-agent system is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems that are difficult or impossible for an individual agent or a monolithic system to solve...
s. However, most of these approaches are typically conceived with centralized or cluster-based server architectures in mind and mostly address the need of reducing management costs rather than the need of enabling complex software systems or providing innovative services.
Autonomy-oriented computation is a paradigm proposed by Jiming Liu in 2001 that uses artificial systems imitating social animals' collective behaviours to solve hard computational problems. For example, ant colony optimization
Ant colony optimization
In computer science and operations research, the ant colony optimization algorithm ' is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs....
could be studied in this paradigm.
The problem of growing complexity
Self-management means different things in different fields.Forecasts suggests that the number of computing devices in use will grow at 38% per annum and the average complexity of each device is increasing. Currently this volume and complexity is managed by highly skilled humans; but the demand for skilled IT personnel is already outstripping supply, with labour costs exceeding equipment costs by a ratio of up to 18:1. Computing systems have brought great benefits of speed and automation but there is now an overwhelming economic need to automate their maintenance.
In "The Vision of Autonomic Computing", Kephart and Chess warn that the dream of interconnectivity of computing systems and devices could become the “nightmare of pervasive computing” in which architects are unable to anticipate, design and maintain the complexity
Complexity
In general usage, complexity tends to be used to characterize something with many parts in intricate arrangement. The study of these complex linkages is the main goal of complex systems theory. In science there are at this time a number of approaches to characterizing complexity, many of which are...
of interactions. They state the essence of autonomic computing is system self-management, freeing administrators from low-level task management while delivering better system behavior.
A general problem of modern distributed computing systems is that their complexity
Complexity
In general usage, complexity tends to be used to characterize something with many parts in intricate arrangement. The study of these complex linkages is the main goal of complex systems theory. In science there are at this time a number of approaches to characterizing complexity, many of which are...
, and in particular the complexity of their management, is becoming a significant limiting factor in their further development. Large companies and institutions are employing large-scale computer network
Computer network
A computer network, often simply referred to as a network, is a collection of hardware components and computers interconnected by communication channels that allow sharing of resources and information....
s for communication and computation. The distributed applications running on these computer networks are diverse and deal with many different tasks, ranging from internal control processes to presenting web content and to customer support.
Additionally, Mobile computing
Mobile computing
Mobile computing is a form of human–computer interaction by which a computer is expected to be transported during normal usage. Mobile computing has three aspects: mobile communication, mobile hardware, and mobile software...
is pervading these networks at an increasing speed: employees need to communicate with their companies while they are not in their office. They do so by using laptop
Laptop
A laptop, also called a notebook, is a personal computer for mobile use. A laptop integrates most of the typical components of a desktop computer, including a display, a keyboard, a pointing device and speakers into a single unit...
s, PDAs
Personal digital assistant
A personal digital assistant , also known as a palmtop computer, or personal data assistant, is a mobile device that functions as a personal information manager. Current PDAs often have the ability to connect to the Internet...
, or mobile phones with diverse forms of wireless
Wireless
Wireless telecommunications is the transfer of information between two or more points that are not physically connected. Distances can be short, such as a few meters for television remote control, or as far as thousands or even millions of kilometers for deep-space radio communications...
technologies to access their companies' data.
This creates an enormous complexity in the overall computer network which is hard to control manually by human operators. Manual control is time-consuming, expensive, and error-prone. The manual effort needed to control a growing networked computer-system tends to increase very quickly.
80% of such problems in infrastructure happen at the client specific application and database layer. Most 'autonomic' service providers guarantee only up to the basic plumbing layer (power, hardware, operating system, network and basic database parameters).
Autonomic systems
A possible solution could be to enable modern, networked computing systems to manage themselves without direct human intervention. The Autonomic Computing Initiative (ACI) aims at providing the foundation for autonomic systems. It is inspired by the autonomic nervous systemAutonomic nervous system
The autonomic nervous system is the part of the peripheral nervous system that acts as a control system functioning largely below the level of consciousness, and controls visceral functions. The ANS affects heart rate, digestion, respiration rate, salivation, perspiration, diameter of the pupils,...
of the human body. This nervous system controls important bodily functions (e.g. respiration, heart rate, and blood pressure) without any conscious intervention.
In a self-managing
Self-management
Self-management means different things in different fields:* In business, education, and psychology, self-management refers to methods, skills, and strategies by which individuals can effectively direct their own activities toward the achievement of objectives, and includes goal setting, decision...
Autonomic System, the human operator takes on a new role: he or she does not control the system directly. Instead, he/she defines general policies and rules that serve as an input for the self-management process. For this process, IBM has defined the following four functional areas:
- Self-Configuration: Automatic configuration of components;
- Self-Healing: Automatic discovery, and correction of faults ;
- Self-Optimization: Automatic monitoring and control of resources to ensure the optimal functioning with respect to the defined requirements;
- Self-Protection: Proactive identification and protection from arbitrary attacks.
IBM defined five evolutionary levels, or the Autonomic deployment model, for its deployment: Level 1 is the basic level that presents the current situation where systems are essentially managed manually. Levels 2 - 4 introduce increasingly automated management functions, while level 5 represents the ultimate goal of autonomic, self-managing systems.
The design complexity of Autonomic Systems can be simplified by utilizing design patterns such as the Model View Controller (MVC) to improve concern separation by helping encapsulate functional concerns.
Control loops
A basic concept that will be applied in Autonomic Systems are closed control loops. This well-known concept stems from Process ControlProcess control
Process control is a statistics and engineering discipline that deals with architectures, mechanisms and algorithms for maintaining the output of a specific process within a desired range...
Theory. Essentially, a closed control loop in a self-managing system monitors some resource (software or hardware component) and autonomously tries to keep its parameters within a desired range.
According to IBM, hundreds or even thousands of these control loops are expected to work in a large-scale self-managing computer system.
Conceptual model
This model highlights the fact that the operation of an autonomic system is purpose-driven. This includes its mission (e.g., the service it is supposed to offer), the policies (e.g., that define the basic behaviour), and the “survival instinct”. If seen as a control system this would be encoded as a feedback error function or in a heuristically assisted system as an algorithm combined with set of heuristics bounding its operational space.
Characteristics
Even though the purpose and thus the behaviour of autonomic systems vary from system to system, every autonomic system should be able to exhibit a minimum set of properties to achieve its purpose:Automatic: This essentially means being able to self-control its internal functions and operations. As such, an autonomic system must be self-contained and able to start-up and operate without any manual intervention or external help. Again, the knowledge required to bootstrap the system (Know-how) must be inherent to the system.
Adaptive: An autonomic system must be able to change its operation (i.e., its configuration, state and functions). This will allow the system to cope with temporal and spatial changes in its operational context either long term (environment customisation/optimisation) or short term (exceptional conditions such as malicious attacks, faults, etc.).
Aware: An autonomic system must be able to monitor (sense) its operational context as well as its internal state in order to be able to assess if its current operation serves its purpose. Awareness will control adaptation of its operational behaviour in response to context or state changes.
External links
- Autonomic Computing by Richard Murch published by IBM Press
- IBM Autonomic Computing Website
- Autonomic Computing articles and tutorials
- Practical Autonomic Computing - Roadmap to Self Managing Technology
- Autonomic computing blog
- Whitestein Technologies - provider of development and integration environment for autonomic computing software
- Applied Autonomics provides Autonomic Web Services
- IPsoft service providers delivering Autonomic Computing
- Enigmatec Website - providers of autonomic computing software
- Handsfree Networks - providers of autonomic computing software
- CASCADAS Project: Component-ware for Autonomic, Situation-aware Communications And Dynamically Adaptable, funded by the European Union
- CASCADAS Autonomic Tool-Kit in Open Source
- ANA Project: Autonomic Network Architecture Research Project, funded by the European Union
- JADE - A framework for developing autonomic administration software
- Barcelona Supercomputing Center - Autonomic Systems and eBusiness Platforms
- SOCRATES: Self-Optimization and Self-Configuration in Wireless Networks
- Dynamically Self Configuring Automotive Systems
- ASSL (Autonomic System Specification Language) : A Framework for Specification, Validation and Generation of Autonomic Systems
- [ftp://ftp.informatik.uni-stuttgart.de/pub/library/medoc.ustuttgart_fi/DIP-2787/DIP-2787.pdf Explanation of Autonomic Computing and its usage for business processes (IBM)- in German]
- Autonomic Computing Architecture in the RKBExplorer
- International Journal of Autonomic Computing
- BiSNET/e: A Cognitive Sensor Networking Architecture with Evolutionary Multiobjective Optimization