Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time o...Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time of data intensive tasks. How- ever, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task comple- tion time and keep good load balance of physical hosts at the same time.展开更多
In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the...In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.展开更多
针对虚拟机(virtual machine,VM)在不同子网间进行在线迁移时,因网络接入点发生改变而无法继续通信的问题,设计和实现了基于代理移动IP(proxy mobile IP,PMIP)的网络连接重定向系统。当VM迁移到外部子网时,将其看做移动节点,不改变IP地...针对虚拟机(virtual machine,VM)在不同子网间进行在线迁移时,因网络接入点发生改变而无法继续通信的问题,设计和实现了基于代理移动IP(proxy mobile IP,PMIP)的网络连接重定向系统。当VM迁移到外部子网时,将其看做移动节点,不改变IP地址,通过外部网络和家乡网络之间的双向隧道传输所有来自和发往该VM的网络数据。此方案中跨子网的在线迁移行为对VM以及与VM通信的节点透明。实验证明了该系统可以保证VM较快的网络切换速度和较好的网络通信性能。展开更多
基金supported by the National Natural Science Foundation of China(6120235461272422)the Scientific and Technological Support Project(Industry)of Jiangsu Province(BE2011189)
文摘Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time of data intensive tasks. How- ever, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task comple- tion time and keep good load balance of physical hosts at the same time.
基金Project(61272148) supported by the National Natural Science Foundation of ChinaProject(20120162110061) supported by the Doctoral Programs of Ministry of Education of China+1 种基金Project(CX2014B066) supported by the Hunan Provincial Innovation Foundation for Postgraduate,ChinaProject(2014zzts044) supported by the Fundamental Research Funds for the Central Universities,China
文摘In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.
文摘针对虚拟机(virtual machine,VM)在不同子网间进行在线迁移时,因网络接入点发生改变而无法继续通信的问题,设计和实现了基于代理移动IP(proxy mobile IP,PMIP)的网络连接重定向系统。当VM迁移到外部子网时,将其看做移动节点,不改变IP地址,通过外部网络和家乡网络之间的双向隧道传输所有来自和发往该VM的网络数据。此方案中跨子网的在线迁移行为对VM以及与VM通信的节点透明。实验证明了该系统可以保证VM较快的网络切换速度和较好的网络通信性能。
