PROJECT TITLE :

A hierarchical context dissemination framework for managing federated clouds

ABSTRACT:

The growing popularity of the Internet has caused the size and complexity of Communications and computing systems to greatly increase in recent years. To alleviate this increased management complexity, novel autonomic management architectures have emerged, in which many automated components manage the network's resources in a distributed fashion. However, in order to achieve effective collaboration between these management components, they need to be able to efficiently exchange information in a timely fashion. In this article, we propose a context dissemination framework that addresses this problem. To achieve scalability, the management components are structured in a hierarchy. The framework facilitates the aggregation and translation of information as it is propagated through the hierarchy. Additionally, by way of semantics, context is filtered based on meaning and is disseminated intelligently according to dynamically changing context requirements. This significantly reduces the exchange of superfluous context and thus further increases scalability. The large size of modern federated cloud computing infrastructures, makes the presented context dissemination framework ideally suited to improve their management efficiency and scalability. The specific context requirements for the management of a cloud data center are identified, and our context dissemination approach is applied to it. Additionally, an extensive evaluation of the framework in a large-scale cloud data center scenario was performed in order to characterize the benefits of our approach, in terms of scalability and reasoning time.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Exploiting Related and Unrelated Tasks for Hierarchical Metric Learning and Image Classification ABSTRACT: Multiple connected activities are taught together in order to improve performance in multi-task learning.
PROJECT TITLE : DeepCrack Learning Hierarchical Convolutional Features for Crack Detection ABSTRACT: Many computer-vision programmes are attracted to the usual line formations known as cracks. Image-based fracture detection using
PROJECT TITLE : Hierarchical Features Driven Residual Learning for Depth Map Super-Resolution ABSTRACT: Computer vision activities such as intelligent cars and 3D reconstruction can be made easier by the rapid development of inexpensive
PROJECT TITLE : Hierarchical Tracking by Reinforcement Learning-Based Searching and Coarse-to-Fine Verifying ABSTRACT: A class-agnostic tracker typically has three main components, namely its motion model, its target appearance
PROJECT TITLE : Moving Object Detection in Video via Hierarchical Modeling and Alternating Optimization ABSTRACT: Traditionally, video modelling experts believe that the background is the primary focus, and the foreground is created

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry