Efficient Resource Provisioning for Virtual Cluster Deployment Based on Network WorkloadNetwork Workload

• Main Authors: Hsiang-Lun Lu, 呂享倫
• Other Authors: Wei-Jen Wang
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/26686770108100547044

碩士 === 國立中央大學 === 資訊工程學系 === 102 === Infrastructure-as-a-Service (IaaS) is an important type of cloud service. It uses server virtualization technology to provide users the software-based virtual machines as the computing power over the physical computing resources in a datacenter, such that the physical computing resources can achieve better utilization rate and manageability. The network virtualization technology is another key technology of IaaS, which can create virtual network environments on top of the physical network environment. With server virtualization and network virtualization, the IaaS users are able to create user-defined virtual clusters/datacenter. The problem is that, manual resources allocation and management for virtual clusters/datacenters is not practical because setting up virtual clusters/datacenters is time-consuming. In addition, the virtual cluster/datacenter placement problem is challenging because we need to consider the constraints of physical resources as well as the relationship among the virtual resources. To solve these problems, we have developed an IaaS management system, SAMEVEDStackV2, based on an open-source software package, OpenStack. The SAMEVEDStackV2 supports a high-level API for virtual clusters/datacenters management, and uses a monitoring/profiling mechanism for tracking and classifying virtual clusters/datacenters. The scheduler uses the information of virtual clusters/datacenters to reduce unnecessary network bandwidth consumption while placing virtual cluster/datacenters on physical computing resources. Our preliminary experimental results show that, the proposed placement strategy can reduce 37% of the physical network bandwidth consumption when comparing with the default OpenStack scheduler.