Abstract: A virtual machine's (VM's) usage of a resource over a first time period may be monitored to determine a load pattern for the VM. A time series analysis of the load pattern may be performed to generate a predictive resource usage model, the predictive resource usage model indicating one or more predicted variations in the usage of the resource by the VM over a second time period. A predicted resource usage of the VM at a future time that is within the second time period may be determined based, at least in part, on the predictive resource usage model. An amount of the resource to allocate to the VM at a current time may be determined based, at least in part, on the predicted resource usage of the VM at the future time and the actual resource usage of the VM at the current time.

Abstract: A virtual machine's (VM's) usage of a resource over a first time period may be monitored to determine a load pattern for the VM. A time series analysis of the load pattern may be performed to generate a predictive resource usage model, the predictive resource usage model indicating one or more predicted variations in the usage of the resource by the VM over a second time period. A predicted resource usage of the VM at a future time that is within the second time period may be determined based, at least in part, on the predictive resource usage model. An amount of the resource to allocate to the VM at a current time may be determined based, at least in part, on the predicted resource usage of the VM at the future time and the actual resource usage of the VM at the current time.

Abstract: A virtual machine's (VM's) usage of a resource over a first time period may be monitored to determine a load pattern for the VM. A time series analysis of the load pattern may be performed to generate a predictive resource usage model, the predictive resource usage model indicating one or more predicted variations in the usage of the resource by the VM over a second time period. A predicted resource usage of the VM at a future time that is within the second time period may be determined based, at least in part, on the predictive resource usage model. An amount of the resource to allocate to the VM at a current time may be determined based, at least in part, on the predicted resource usage of the VM at the future time and the actual resource usage of the VM at the current time.

Abstract: A virtual machine's (VM's) usage of a resource over a first time period may be monitored to determine a load pattern for the VM. A time series analysis of the load pattern may be performed to generate a predictive resource usage model, the predictive resource usage model indicating one or more predicted variations in the usage of the resource by the VM over a second time period. A predicted resource usage of the VM at a future time that is within the second time period may be determined based, at least in part, on the predictive resource usage model. An amount of the resource to allocate to the VM at a current time may be determined based, at least in part, on the predicted resource usage of the VM at the future time and the actual resource usage of the VM at the current time.

Abstract: Systems and methods for optimizing a virtual machine cluster. An example method may comprise receiving, by a processing device, an information characterizing a virtual machine cluster, the information comprising at least one of: values of one or more cluster configuration parameters, values of one or more cluster state parameters, or values of one or more user request parameters; and producing, in view of the received information, an ordered list of cluster configuration operations to be performed on virtual machines of the virtual machine cluster, the cluster configuration operations designed to yield a resulting configuration of the virtual machine cluster, wherein the resulting configuration is characterized by a quasi-optimal configuration score among configuration scores of two or more candidate configurations, the configuration score determined by applying one or more virtual machine scheduling policy rules to parameters of a candidate configuration.

Abstract: Systems and methods for optimizing a virtual machine cluster. An example method may comprise receiving, by a processing device, an information characterizing a virtual machine cluster, the information comprising at least one of: values of one or more cluster configuration parameters, values of one or more cluster state parameters, or values of one or more user request parameters; and producing, in view of the received information, an ordered list of cluster configuration operations to be performed on virtual machines of the virtual machine cluster, the cluster configuration operations designed to yield a resulting configuration of the virtual machine cluster, wherein the resulting configuration is characterized by a quasi-optimal configuration score among configuration scores of two or more candidate configurations, the configuration score determined by applying one or more virtual machine scheduling policy rules to parameters of a candidate configuration.