I/O Workload in Virtualized Data Center Using Hypervisor

Cloud computing [10] is gaining popularity as it’s the way to virtualize the datacenter and increase flexibility in the use of computation resources. This virtual machine approach can dramatically improve the efficiency, power utilization and availability of costly hardware resources, such as CPU and memory. Virtualization in datacenter had been done in the back end of Eucalyptus software and Front end was installed on another CPU. The operation of performance measurement had been done in network I/O applications environment of virtualized cloud. Then measurement was analyzed based on performance impact of co-locating applications in a virtualized cloud in terms of throughput and resource sharing effectiveness, including the impact of idle instances on applications that are running concurrently on the same physical host. This project proposes the virtualization technology which uses the hypervisor to install the Eucalyptus software in single physical machine for setting up a cloud computing environment. By using the hypervisor, the front end and back end of eucalyptus software will be installed in the same machine. The performance will be measured based on the interference in parallel processing of CPU and network intensive workloads by using the Xen Virtual Machine Monitors. The main motivation of this project is to provide the scalable virtualized datacenter

CloudScope: diagnosing and managing performance interference in multi-tenant clouds

© 2015 IEEE.Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%

SDN-based virtual machine management for cloud data centers

Software-Defined Networking (SDN) is an emerging paradigm to logically centralize the network control plane and automate the configuration of individual network elements. At the same time, in Cloud Data Centers (DCs), even though network and server resources converge over the same infrastructure and typically over a single administrative entity, disjoint control mechanisms are used for their respective management. In this paper, we propose a unified server-network control mechanism for converged ICT environments. We present a SDN-based orchestration framework for live Virtual Machine (VM) management where server hypervisors exploit temporal network information to migrate VMs and minimize the network-wide communication cost of the resulting traffic dynamics. A prototype implementation is presented and Mininet is used to evaluate the impact of diverse orchestration algorithms