Advances in networking and computing technology and software tools have resulted in an explosive growth in networked application and information service that cover all aspects of our life. These sophisticated application and services are extremely complex, heterogeneous and dynamic. Furthermore, the underlying information infrastructure globally aggregates large numbers of independent computing and communication resource, data stores and sensor networks, and itself similarly large, dynamic heterogeneous and complex. HIRE WRITERS ONLINE.
The combination has resulted in application development, configuration and management complexities that break current computing paradigms based on static requirement, behaviors, interaction and compositions. As a result, applications, programming environments and information infrastructure are rapidly becoming brittle, unmanageable and insecure. This has necessitated the investigation of an alternate paradigm for systems and application design, which is based on strategies by biological systems to deal with similar challenges of scale, complexity, heterogeneity and uncertainty a vision that has been referred to as autonomic computing.
An autonomic element is the smallest unit of an autonomic application or system. It is a self-contained software or system module with specified input/output interface and explicit context dependencies it also has embedded mechanism for self-management, which are responsible for implementing its functionalities, exporting constraints, managing its behavior in accordance with contact and policies and interacting with other elements. Key parts of an autonomic element are:
Autonomic computing systems should have the ability to adapt automatically to the dynamically changing environment. The growing complexity brings the operating environment of computing systems unpredictable, and makes the computing systems brittle, uncertain. To address these problems an automatic computing system should be aware of this operating conditions, have the ability to predict trends and adapt itself to this changing environment.
Autonomic computing systems should have the ability to discover diagnose and repair failed components without the disruption of systems services. Systems and critical services must be crashed, disrupted even under extreme conditions. To achieve it, autonomic computing systems must have the ability to recovery from an unexpected situation automatically.
Autonomic computing systems should have the ability to monitor and tune resources automatically. The above properties self-configuration and self-healing will make autonomic computing powerful. To keep autonomic computing always powerful, an autonomic computing system need to continually maximize resources utilization efficiently.
Autonomic computing systems should have the ability to anticipate, detect, identify and protect against attacks from anywhere. The security issue nowadays is a key issue for all systems, autonomic computing will not be an excerpt. Systems components should be able to incorporate themselves to defend as whole against attacks. They should define and manage user access to all sharing and their own resources in order to protect itself from unauthorized access.
Conceptual research issues and challenges include: Adapting classical model and theories for machine learning, optimization and control dynamic and multi agent systems; providing effective model for negotiation that autonomic elements can use to establish multilateral relationships among themselves; defining appropriate abstraction and models for specifying, understanding, controlling and implementing and autonomic behaviors.
Autonomic application and systems will be constructed from autonomic elements that manage their internal behavior and their relationships with other autonomic elements that manage their internal behavior and their relationships with other autonomic elements in accordance with the policies that human or other elements have established. System and software architecture in which local as well as global autonomic behaviors can be specified, implemented and controlled on robust and predictable manner remains a key challenge.
The primary middleware level research challenge is providing the core services required to realize autonomic behavior in robust, reliable and scalable manner, in spite of dynamism and uncertainty of the system and applications. These includes discovery messaging, security, privacy, trust, etc. The middleware itself should be secure, reliable and robust against new and insidious forms of attack that use self-management based on high level policies to their own advantage.
The key challenges at the application level is the formulation and development of systems and application that are capable of managing configuring, adapting, optimizing, protecting and healing themselves. This includes programming models, frameworks and middleware services that support the definition of autonomic elements, the developments of autonomic applications as the dynamic and opportunistic composition of these elements and the policy.
Applications of autonomic computing
Autonomic computing toolkit
Autonomic computing toolkit gives us some technologies and tools which are closely refer to as properties this toolkits are:
Dynamic systems initiative
Dynamic systems initiative (DSI) is a Microsoft approach to reducing systems complexity. As we seen in the general architecture of autonomic computing the role of knowledge in system management is also emphasized in DSI once this software is created it can be capture in system runtime, so that system is manageable autonomously.
Ocean store is a global-scale persistent data storage system from the University of California at Berkeley. It uses an introspection layer to monitor and analyze network information in order to improve performances and fault management ocean store has its own GUID and is stored in distributed data location
OptimoGrid provides a solution of the problem a large-scale application by implementing runtime management and dynamic rebalancing. Policy management for autonomic computing implements an autonomic policy management. The Adaptive Enterprise provides an enterprise infrastructure used to manage enterprise knowledge in real-time.
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