StarCube: An On-Demand and Cost-Effective Framework for Cloud Data Center Networks with Performance Guarantee - 2018 PROJECT TITLE :StarCube: An On-Demand and Cost-Effective Framework for Cloud Data Center Networks with Performance Guarantee - 2018ABSTRACT:In this Project, we have a tendency to propose a resource management framework referred to as StarCube, which guarantees non-blocking resource allocation and topology-preserving reallocation for fat-tree based mostly multi-tenant cloud information centers. With StarCube, each cloud service is allocated an isolated non-blocking virtual network topology, and also the topology provisioned to each service is guaranteed logically unchanged throughout and once virtual machine reallocation. This resource management downside is formulated and proved to be NP-complete. To achieve high resource efficiency in acceptable time, we propose a cost-effective algorithm with polynomial-time complexity based on StarCube for on-demand resource allocation and reallocation. We tend to demonstrate via in depth simulations that the server resources in StarCube-primarily based cloud knowledge centers can be nearly absolutely utilised with negligible reallocation price. The results conjointly show that StarCube supports a giant selection of service provisioning feasibly and efficiently for cloud data centers of numerous scales and with dynamic demands. To the most effective of our knowledge, StarCube is the first answer to allocating and reallocating cloud services for fat-tree networks with guarantee on non-blocking properties. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Space-Efficient Verifiable Secret Sharing Using Polynomial Interpolation - 2018 Statistical Learning for Anomaly Detection in Cloud Server Systems: A Multi-Order Markov Chain Framework - 2018
