Vmware, Inc.

Wmware, Inc.

Haoqiang Zheng

Xunjia Lu

Garrett Cale Smith

Lenin Singaravelu

Shilpi Agarwal

Daniel Michael Hecht

Zhelong Pan

Fei Guo

Carl Alan Waldspurger

Bi Wu

Puneet Zaroo

Sabareesh Subramaniam

Badhrinath Sampathkumar

Rajesh Venkatasubramanian

Petr Vandrovec

Seongbeom Kim

David A Dunn

Qasim Ali

Timothy P Mann

Richard West

Xiao Zhang

Fred Jacobs

Yifan Hao

Techniques for optimizing virtual machine (VM) scheduling on a non-uniform cache access (NUCA) system are provided. In one set of embodiments, a hypervisor of the NUCA system can partition the virtual CPUs of each VM running on the system into logical constructs referred to as last level cache (LLC) groups, where each LLC group is sized to match (or at least not exceed) the LLC domain size of the system. The hypervisor can then place/load balance the virtual CPUs of each VM on the system's cores in a manner that attempts to keep virtual CPUs which are part of the same LLC group within the same LLC domain, subject to various factors such as compute load, cache contention, and so on.

Optimizing virtual machine scheduling on non-uniform cache access (NUCA) systems

Techniques for concurrently supporting virtual non-uniform memory access (virtual NUMA) and CPU/memory hot-add in a virtual machine (VM) are provided. In one set of embodiments, a hypervisor of a host system can compute a node size for a virtual NUMA topology of the VM, where the node size indicates a maximum number of virtual central processing units (vCPUs) and a maximum amount of memory to be included in each virtual NUMA node. The hypervisor can further build and expose the virtual NUMA topology to the VM. Then, at a time of receiving a request to hot-add a new vCPU or memory region to the VM, the hypervisor can check whether all existing nodes in the virtual NUMA topology have reached the maximum number of vCPUs or maximum amount of memory, per the computed node size. If so, the hypervisor can create a new node with the new vCPU or memory region and add the new node to the virtual NUMA topology.

Techniques for concurrently supporting virtual NUMA and CPU/memory hot-add in a virtual machine

An example method of managing exclusive affinity for threads executing in a virtualized computing system includes: determining, by an exclusive affinity monitor executing in a hypervisor of the virtualized computing system, a set of threads eligible for exclusive affinity; determining, by the exclusive affinity monitor, for each thread in the set of threads, impact on performance of the threads for granting each thread exclusive affinity; and granting, for each thread of the set of threads having an impact on performance of the threads less than a threshold, exclusive affinity to respective physical central processing units (PCPUs) of the virtualized computing system.

Opportunistic exclusive affinity for threads in a virtualized computing system

A technique is described for managing processor (CPU) resources in a host having virtual machines (VMs) executed thereon. A target size of a VM is determined based on its demand and CPU entitlement. If the VM's current size exceeds the target size, the technique dynamically changes the size of a VM in the host by increasing or decreasing the number of virtual CPUs available to the VM. To 'deactivate' virtual CPUs, a high-priority balloon thread is launched and pinned to one of the virtual CPUs targeted for deactivation, and the underlying hypervisor deschedules execution of the virtual CPU accordingly. To 'activate' virtual CPUs, the number of virtual CPUs, the launched balloon thread may be killed.

Dynamic virtual machine sizing

A method of selectively assigning virtual CPUs (vCPUs) of a virtual machine (VM) to physical CPUs (pCPUs), where execution of the VM is supported by a hypervisor running on a hardware platform including the pCPUs, includes determining that a first vCPU of the vCPUs is scheduled to execute a latency-sensitive workload of the VM and a second vCPU of the vCPUs is scheduled to execute a non-latency-sensitive workload of the VM and assigning the first vCPU to a first pCPU of the pCPUs and the second vCPU to a second pCPU of the pCPUs. A kernel component of the hypervisor pins the assignment of the first vCPU to the first pCPU and does not pin the assignment of the second vCPU to the second pCPU. The method further comprises selectively tagging or not tagging by a user or an automated tool, a plurality of workloads of the VM as latency-sensitive.

Fine-grained application-aware latency optimization for virtual machines at runtime

Load balancing across hosts in a computer system is triggered based on pairwise comparisons of resource utilization at different host. A method for load balancing across hosts includes the steps of determining a resource utilization difference between first and second hosts, wherein the first host has a higher resource utilization than the second host, comparing the resource utilization difference against a threshold difference, and upon determining that the resource utilization difference exceeds the threshold difference, selecting a workload executing in the first host for migration to the second host.

Pairwise comparison and migration of workloads for load balancing

Disclosed are various embodiments for distributing the load of a plurality of virtual machines across a plurality of hosts. A potential new host for a virtual machine executing on a current host is identified. A gain rate associated with migration of the virtual machine from the current host to the potential new host is calculated. A gain duration associated with migration of the virtual machine from the current host to the potential new host is also calculated. A migration cost for migration of the virtual machine from the current host to the potential new host, the migration cost being based on the gain rate and the gain duration is determined. It is then determined whether the migration cost is below a predefined threshold cost. Migration of the virtual machine from the current host to the optimal host is initiated in response to a determination that the migration cost is below the predefined threshold.

Resource optimization for virtualization environments

Disclosed are various embodiments that utilize conflict cost for workload placements in datacenter environments. In some examples, a protected memory level is identified for a computing environment. The computing environment includes a number of processor resources. Incompatible processor workloads are prohibited from concurrently executing on parallel processor resources. Parallel processor resources share memory at the protected memory level. A number of conflict costs are determined for a processor workload. Each conflict cost is determined based on a measure of compatibility between the processor workload and a parallel processor resource that shares a particular memory with the respective processor resource. The processor workload is assigned to execute on a processor resource associated with a minimum conflict cost.

Workload placement using conflict cost

Disclosed are various embodiments for distributing the load of a plurality of virtual machines across a plurality of hosts. A first plurality of efficiency ratings for a current host of a virtual machine are calculated. A second plurality of efficiency ratings for a potential new host of the virtual machine are also calculated. The first plurality of efficiency ratings are compared to the second plurality of efficiency ratings to determine that the potential new host for the virtual machine is an optimal host for the virtual machine. Then migration of the virtual machine from the current host to the optimal host is initiated.

Performance modeling for virtualization environments

The disclosure provides a method of performing a workload on a virtual machine (VM) executing on a host comprising one or more physical central processing units (pCPUs) is provided. The method further includes setting a quality of service (QoS) metric associated with the VM, the QoS metric indicating a time period. The method further includes setting a bandwidth metric associated with the VM, the bandwidth metric indicating a percentage. The method further includes allocating by a central processing unit (CPU) scheduler to a virtual CPU (vCPU) of the VM one of the one or more pCPUs periodically every time period, wherein for every time period the one of the one or more pCPUs is allocated to the vCPU for a duration that is the percentage of the time period based on the QoS metric and the bandwidth metric. The method further includes executing the workload on the virtual machine with the vCPU according to the allocation of the one or more pCPUs.

Virtual processor allocation with execution guarantee

Pairwise comparison for load balancing

A host computer has a plurality of containers including a first container executing therein, where the host also includes a physical network interface controller (NIC). A packet handling interrupt is detected upon receipt of a first data packet associated with the first container. If the first virtual machine is latency sensitive, then the packet handling interrupt is processed. If the first virtual machine is not latency sensitive, then the first data packet is queued and and processing of the packet handling interrupt is delayed.

Networking stack of virtualization software configured to support latency sensitive virtual machines

Systems and methods for performing selection of non-uniform memory access (NUMA) nodes for mapping of virtual central processing unit (vCPU) operations to physical processors are provided. A CPU scheduler evaluates the latency between various candidate processors and the memory associated with the vCPU, and the size of the working set of the associated memory, and the vCPU scheduler selects an optimal processor for execution of a vCPU based on the expected memory access latency and the characteristics of the vCPU and the processors. The systems and methods further provide for monitoring system characteristics and rescheduling the vCPUs when other placements provide improved performance and efficiency.

Adaptive CPU NUMA scheduling

The disclosure provides techniques for scheduling a jitterless workload on a virtual machine (VM) executing on a host comprising one or more pCPUs comprising a first subset of the one or more pCPUs and a second subset of the one or more pCPUs. The techniques further include creating a jitterless zone, wherein the jitterless zone includes the first subset of the one or more pCPUs. The techniques further include determining whether a vCPU of the VM is used to execute a jitterless workload or a non-jitterless workload. The techniques further include allocating by a CPU scheduler to the vCPU at least one of the pCPUs in the jitterless zone when the vCPU of the VM is used to execute a jitterless workload. The techniques further include scheduling the jitterless workload for execution by the vCPU on the allocated at least one of the pCPUs in the jitterless zone.

Growing community of inventors

Mountain View, CA, United States of America

Haoqiang Zheng