Abstract:
The current document is directed to methods and systems for efficiently executing OSL-virtualization containers within the execution environments provided by virtual machines that execute above traditional virtualization layers within large, virtualized, distributed computing systems. The currently disclosed methods and systems anticipate the need for additional virtual machines in order to meet anticipated demands for one or more computational resources by the containers. In addition, the methods and systems provision and launch virtual machines with computational-resource allocations that minimize overhead and computational-resource wastage. In one implementation, computational-resource utilization of ATMs and containers within the virtualized, distributed computer system are periodically monitored in order to estimate future demand for the computational resource and, when necessary, to launch additional virtual machines to meet the estimated future demand for the computational resource.
Abstract:
Methods and apparatus to select virtualization environments are disclosed. An example method includes determining, via a processor, characteristics of a virtualized application that is deployed in an existing virtualization environment, analyzing, via the processor, the characteristics of the virtualized application to select a subset of virtualization environments that are capable of executing the virtualized application, the subset of virtualization environments selected from a set of virtualization environments of different virtualization environment types used in the datacenter, comparing, via the processor, the characteristics of the virtualized application to the virtualization environments of the subset of virtualization environments to determine scores for the virtualization environments, and migrate the virtualized application from the existing virtualization environment to a new virtualization environment based on the scores.
Abstract:
The present disclosure describes methods and systems that allocate costs of deploying and operating a virtual network to tenants that use the virtual network. In one implementation, costs are allocated to tenant virtual machines (“VMs”) by determining a network bandwidth of a virtual network, determining a common cost of operating the virtual network, determining a service capacity for each network service provided by the virtual network, and determining a service cost for each network service. A portion of the common cost is allocated to each VM based on the proportion of network bandwidth used by each VM, and a portion of the service cost is allocated to each VM based on the proportion of the service capacity used by each VM.
Abstract:
Methods and apparatus to select virtualization environments are disclosed. An example apparatus includes a logic circuit, a workload analyzer to determine characteristics of a virtualized application, a score generator to compare the characteristics of the virtualized application to a plurality of virtualization environment types to determine scores for each of the plurality of virtualization environment types, the scores based on rules that identify different scores for combinations of characteristics and virtualization environment types, and a workload deployer to deploy the virtualized application using one of the plurality of virtualization environment types based on the scores.
Abstract:
Techniques for migrating a VM in a hybrid cloud computing system are provided. The techniques include identifying a similar VM at the destination, comparing dictionaries for the VM to be transmitted and the similar VM, and compressing the VM based on the comparison. After transmitting the compressed VM, the destination decompresses the VM using the dictionary of the similar VM. Dictionaries associate chunks of VM data with hashes of those chunks. This allows replacement of chunks with the hashes, thereby compressing the VM for transmission.
Abstract:
Methods and systems to compute cost efficiency of virtual machines (“VMs”) running in a private cloud are described. Methods and systems compute a cost efficiency value for each VM in the private cloud based on cost of the VM in the private cloud, cost of similar VMs in the private cloud, price of similar VM running in the public cloud, and the cost of similar VMs running in one or more private clouds in the same geographical area. When the cost efficiency of a VM is greater than a cost efficiency threshold, the methods generate an alert and recommendations for moving the VM to a lower cost public cloud.
Abstract:
A method for scheduling computing resources without container migration includes determining a resource availability for one or more hosts, a resource allocation for one or more virtual machines (VMs), and a resource usage for one or more containers. The method further includes calculating a target resource configuration for one or more VMs, wherein calculating a target resource configuration comprises determining an upper limit of resource demand on a VM from one or more containers allocated on the VM, based at least in part on the resource usage. The method also includes removing or adding resources to each of the one or more VMs for which a target resource configuration was calculated to achieve the target resource configuration for each VM. The method further includes allocating the one or more VMs on the one or more hosts based on the resource availability of the one or more hosts.
Abstract:
A power distribution unit having a power supply inputs including mains, secondary, generator, and renewable can be configured to measure how much power is drawn from each of the power supply inputs over a time period and to provide data to a unit rate of power engine indicating the same. A cost information engine can be configured to provide cost information, applicable over the time period, for each of the power supply inputs to the unit rate of power engine. The unit rate of power engine can be configured to compute the unit rate of power consumed in the data center over the time period based on the power drawn from each of the power supply inputs and the cost information.
Abstract:
Techniques for minimizing guest OS licensing costs in a volume based licensing model in a virtual datacenter are described. In one example embodiment, a virtual machine (VM) that requires a license key for a type of guest OS installed in the VM is identified. A license key is then assigned to the VM by first attempting to reassign a license key from an inactive VM, and only if a license key is not available for reassignment, obtaining a new license key for the VM.