3/29/2011

Future directions of Autonomic computing



By reading this paper you can gain the knowledge about Autonomic Computing. I detailed it under the following sub topics.

 1.0 Introduction
 2.0 Why need Autonomic?
 3.0 Major Researches 
           3.1 Research projects in Autonomic computing.
           3.2 University research projects in autonomic computing
4.0 Four basic elements of autonomic computing
           4.1self-configuring
           4.2self-healing
           4.3self-optimizing
           4.4 self-protecting
           AutonomicComputing Vs Current Computing
5.0 Autonomic computing architecture
6.0. Autonomic computing today
           6.1. Initiatives of autonomic computing 
           6.2. Benefits
           6.3. Applications
7.0 What happen if does not have autonomic in the future?
8.0 Autonomic Computing Research Issues and Challenges
9.0 Future direction of Autonomic computing
10.0 Conclusion

9.0 Future direction of Autonomic computing

Realistically, 100 percent autonomic systems will be very difficult to build and will require significant exploration of new technologies and innovations. That’s why researches view this as a Grand Challenge for the entire IT industry. People will need to make progress along two tracks:
First, making individual system components autonomic and achieving autonomic behavior at the level of global enterprise IT systems.

That second track may prove to be extremely challenging. Unless each component in a system can share information with every other part and contribute to some overall system awareness and regulation, the goal of autonomic computing will not really be reached. So one huge technical challenge entails figuring how to create this “global” system awareness and management. Or to put it another way, how do we optimize the entire stack of computing layers as a whole? It’s not something we currently know how to do.

We know there are also many interim challenges: how to create the proper “adaptive algorithms”—sets of rules that can take previous system experience and use that information to improve the rules or how to balance what these algorithms “remember” with what they ignore. We humans tend to be very good at the latter—we call it “forgetting”—and at times it can be a good thing: we can retain only significant information and not be distracted by extraneous data.

Still another problem to solve: how to design an architecture for autonomic systems that provides consistent interfaces and points of control while allowing for a heterogeneous environment. We could go on, as the list of problems is actually quite long, but it is not so daunting as to render autonomic computing another dream of science fiction. In fact, we’re beginning to make progress in key areas.

Many established fields of scientific study will contribute to autonomic computing.
What we’ve learned in artificial intelligence, control theory, complex adaptive systems and catastrophe theory, as well as some of the early work done in cybernetics, will give us a variety of approaches to explore. Current research projects at laboratories and universities include self-evolving systems that can monitor themselves and adjust to some changes, “cellular” chips capable of recovering from failures to keep long-term applications running, heterogeneous workload management that can balance and adjust workloads of many applications over various servers, and traditional control theory applied to the realm of computer science, to name just a few.

The following list is a select number of recommendations and observations that have come to light during the research and writing of this book. These recommendations are not in any specific order—rather they are a list of thoughts, suggestions, and recommendations that may make autonomic computing more functional.

Develop autonomic tools and technologies on top of existing standards.
Develop autonomic-based systems using multivendor approaches.
Develop metrics to assess the relative strengths and weakness of different approaches. Provide mature software development methodologies and tools for autonomic-based systems.
Develop sophisticated yet easy-to-use autonomic environments to include support for design, test, maintenance, and visualization of autonomic-oriented systems.
Develop libraries of interaction protocols designed for specific autonomic behavior interactions.

Develop the ability for autonomics to collectively evolve languages and protocols specific to the application domain and the autonomics involved.
Work toward autonomic-enabled semantic Web services.
Develop tools for effective sharing and negotiation strategies.
Develop computational models of norms and social structure.Develop sophisticated organizational views of autonomic systems.
Advance the state of the art in the theory and practice of negotiation strategies.Develop an enhanced understanding of autonomic society dynamics. Advance the state of the art in the theory and practice of argumentation strategies.
  • Develop autonomic-based eScience systems for the scientific community.
  • Develop techniques for allowing users to specify their preference and desired outcome of negotiation in complex environments.
  • Develop techniques to enable autonomic to identify, create, and dissolve coalitions in multiautonomic negotiation and argumentation contexts.
  • Develop techniques for autonomic personalization.
  • Develop distributed learning mechanisms.
  • Develop techniques to enable automatic runtime reconfiguration and redesign of autonomic systems.Develop techniques for testing the reliability of autonomics. 
  • Undertake research on methods for ensuring security and verifiability of autonomic systems. 
  • Develop and implement trust and reputation mechanisms.Engage in related-research standardization activities (e.g., UDDI, WDL, WSFL, XLANG, OMG, CORBA, and other widely used industrial-strength open standards).
  • Build autonomic prototypes spanning organizational boundaries (potentially conflicting). 
  • Encourage early adopters of autonomic technology, especially those who take some risk. Provide incentives.
  • Develop a catalog of early adopter case studies, both successful and unsuccessful.  
  • Provide analysis and publish reasons for success and failure cases.
  • Identify and publish best practices for autonomic-oriented development and deployment.Support open standardization efforts.

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