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eLiza

INTRODUCTION
In a self-configuring transformation of historical proportions, Project eLiza of IBM self-managing IT infrastructure fame, is now known as the IBM autonomic computing initiative. This remarkable display of self-optimization comes at a time when all of IBM, in collaboration with customers, the open community and other vendors is taking the lead in delivering on the promise of next generation e-business computing. Rather than taking the narrow self-protecting, self-preserving view, Eliza is taking the ultimate step in dynamically adapting to changes in business policies by surrendering its initial code name to one that is more universally understood and appreciated. In moving on, Eliza leaves a self-healing legacy for devoted fans, demonstrating how to take corrective action and move on without disrupting ongoing processes and selflessly showing what the IBM autonomic computing initiative is all about!
Self-healing technology in computers is not a new concept. Notable examples of this technology are ECC (Error-Correcting Code) memory, SMART (Self-Monitoring, Analysis and Reporting Technology) for hard disks, and fault-tolerant servers. Research institutions are working towards making such technologies more autonomous. In this sense, there will be minimal human intervention, and computing sub-systems will be able to proactively detect and rectify potential faults before any failure occurs. A fully autonomous computing system does not exist today, but such systems could make the concept of 24 x 7 x 365, or 99.999 percent uptime possible.
Various computer vendors and research institutions are involved in autonomic computing, which is also referred to as ‘self-healing technology’, ‘holistic computing’ or ‘introspective computing’. The technology is not only applicable to servers, but also extends to databases, software applications, and Grid Computing networks.
Perhaps the first elements of autonomous computing were ‘software agents’ that made waves around 1999. A prime example is Computer Associates’ Nugent’s. According to CA, Nugent’s look for patterns in data and can extrapolate from the patterns to predict future events. Nugent’s, which are included in CA’s Unicenter systems management software and its Jasmine object-oriented database, can look at up to 1,200 variables and make sense of it all. Business data is one area in which CA is pushing this technology.

CHALLENGES FOR AUTONOMIC COMPUTING

Analysts say the days of widespread autonomic computing usage are still way off. However, we are beginning to see elements of it in business systems (See box: An update on IBM’s Project eLiza). Meanwhile, the challenges faced in developing such systems are mainly those dealing with the management of complex systems operating in heterogeneous environments.

The other challenge is to convince customers that autonomic computing actually simplifies systems management and can cut costs in a manner described earlier in this article. IT managers and administrators may be reluctant to give up control of the systems they manage.

The transition to the new self-healing systems must cause minimal or no disruption. Teething problems will only shatter an IT manager’s faith in these systems.

Systems with autonomic capabilities (such as IBM’s servers) are already available in the Indian market. The next few days will determine the acceptance of autonomic computing as such systems begin to be deployed in enterprises. But we can look forward to the days of highly simplified network management and rapid systems deployment, thanks to the self-healing, self-configuring, and self-tuning characteristics of the next generation of computing systems.

BASIC CRITERIA FOR DEFINING AUTONOMIC COMPUTING

IBM sees eight basic criteria defining a pervasive autonomic computing system. In short, they are as follows:
• The system must be capable of taking continual stock of itself, its connections, devices and resources, and know which are to be shared or protected.
• It must be able to configure and reconfigure itself dynamically as needs dictate.
• It must constantly search for ways to optimize performance.
• It must perform self-healing by redistributing resources and reconfiguring itself to work around any dysfunctional elements.
• It must be able to monitor security and protect itself from attack.
• It must be able to recognize and adapt to the needs of coexisting systems within its environment.
• It must work with shared technologies. Proprietary solutions are not compatible with autonomic computing ideology.
• It must accomplish these goals seamlessly without intervention.
While these are the eight proposed ingredients of an autonomic computing system, IBM hopes they will result in three goals for the end user: flexibility, accessibility and transparency. In short, the ability to extract data seamlessly from home, office or field, hassle free and regardless of the device, network, or connectivity methodology.
Several universities and companies, such as Sun Microsystems and Hewlett Packard, are developing similar systems, but IBM claims their plans for autonomic computing are more far reaching. As this plan relies on a cooperative evolution of hardware and software, autonomic computing is to be implemented in stages over a period of several years.

FUNCTIONAL CHARACTERISTICS OF AUTONOMIC COMPUTING

In a self-managing Autonomic System, the human operator takes on a new role: He does not control the system directly. Instead, he 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 characteristics:
• 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
The seamless integration of new hardware resources and the cooperative yielding of resources by the operating system is an important element of self-configuring systems. Hardware subsystems and resources can configure and re-configure autonomously both at boot time and during run time. This action may be initiated by the need to adjust the allocation of resources based on the current optimization criteria or in response to hardware or firmware faults. Self-configuring also includes the ability to concurrently add or remove hardware resources in response to commands from administrators, service personnel, or hardware resource management software. BENEFITS OF AUTONOMIC COMPUTING
Short-term I/T related benefits
• Simplified user experience through a more responsive, real-time system.
• Cost-savings - scale to use.
• Scaled power, storage and costs that optimize usage across both hardware and software.
• Full use of idle processing power, including home PC's, through networked system.
• Natural language queries allow deeper and more accurate returns.
• Seamless access to multiple file types. Open standards will allow users to pull data from all potential sources by re-formatting on the fly.
• Stability. High availability. High security system. Fewer system or network errors due to self-healing.

Long-term, Higher Order Benefits
• Realize the vision of enablement by shifting available resources to higher-order business.
• Embedding autonomic capabilities in client or access devices, servers, storage systems, middleware, and the network itself. Constructing autonomic federated systems.
• Achieving end-to-end service level management.
• Collaboration and global problem-solving. Distributed computing allows for more immediate sharing of information and processing power to use complex mathematics to solve problems.