Autonomic computing assignment help

Autonomic computing assignment


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.

Autonomic computing assignment help online

Autonomic computing assignment help 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.

Architecture of an autonomic computing element

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:

  • Managed element: this is the smallest functional unit of application and contains the executable code it also exports functional interfaces, its functional behavior attribute and constraints and its control mechanism
  • Environment: the environment represents all the factors that can impact the manage element. The environment can be viewed as two systems forming a stable system.
  • Control: Each autonomic element has its one manager that accepts user specified requirements, interrogates the element and characterizes its state, senses the state of overall applications and determines the state of environment.

Properties of autonomic computing system


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.

Autonomic computing assignment help online

Autonomic computing assignment help online

Autonomic computing research issues and challenges

Conceptual challenges

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.

Architecture challenges

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.

Middleware challenges

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.

Application challenges

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:

  • Autonomic manager engine: it demonstrates the MAPE control process in the architecture of autonomic computing
  • Log and trace analyzer: it demonstrates a partial implementation of control loops, including the part of monitoring and analyzing
  • Generic log adapter: it provides translation from log files into a common event format

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

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

Other applications

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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