Relationship Between Multicore CPUs and vCPUs

1. Overview

In today’s digital landscape, virtualization and cloud computing form the backbone of scalable infrastructures. Consequently, understanding the relationship between multicore CPUs and virtual CPUs (vCPUs) is more important than before.

Simply put, multicore CPUs serve as the physical engines that drive the virtual machines (VMs) in a virtualized environment, while vCPUs operate as the virtual components assigned to these VMs.

In this tutorial, we’ll explore how these elements interact. In addition, we gain insights into efficient resource allocation, system performance optimization, and the balancing act between over-provisioning and under-provisioning.

2. Understanding Multicore CPUs and vCPUs

In this section, we’ll explain a multicore CPU and a virtual CPU.

2.1. Multicore CPUs

To begin with, a multicore CPU consists of multiple processing cores within a single physical processor. Each core functions like an individual CPU, handling separate threads and tasks. Early CPUs typically had one core, handling one task at a time. However, with the growing demand for higher processing power, manufacturers introduced multicore CPUs to increase parallel processing capabilities without significantly increasing the physical size of processors.

In a typical multicore CPU, each core has its own execution unit but shares access to memory and other resources. This configuration allows the CPU to execute multiple processes simultaneously, thereby improving performance for applications that can take advantage of parallelism. The more cores a CPU has, the more it can handle, making multicore CPUs ideal for high-demand workloads and virtualized environments.

2.2. Virtual CPUs

A vCPU represents a logical CPU core within a virtual machine. When a VM is assigned a certain number of vCPUs, it appears to have that number of processing units available for executing tasks. The virtualization software, or hypervisor, creates these vCPUs by allocating physical CPU resources to each VM. However, these vCPUs do not exist as independent hardware; rather, they are a virtualized representation of the available physical cores.

The relationship between physical CPU cores and vCPUs forms the foundation of resource allocation in virtualized systems. For example, in a system with a quad-core CPU, a hypervisor might allocate four vCPUs across various VMs, depending on the workloads and requirements of each VM.

3. Mapping Multicore CPUs to Virtual CPUs

Mapping physical CPU cores to vCPUs depends on the hypervisor’s strategy and the performance requirements of each VM. In general, a hypervisor can map multiple vCPUs to a single physical core, a technique commonly referred to as over-provisioning.

Conversely, under-provisioning occurs when the number of vCPUs assigned to VMs is lower than the physical core count.

Let’s check out an example of a vCPU-to-core mapping strategy:

Physical Core 1 -> vCPU 1, vCPU 2
Physical Core 2 -> vCPU 3, vCPU 4
Physical Core 3 -> vCPU 5, vCPU 6
Physical Core 4 -> vCPU 7, vCPU 8

In this example, each physical core supports two vCPUs, resulting in an over-provisioned environment. This strategy allows the hypervisor to maximize the use of available CPU resources, but it may lead to performance degradation if multiple vCPUs compete for processing time on the same core.

4. Comparing Over-Provisioning to Under-Provisioning

In essence, over-provisioning this approach allows more VMs to run on a single physical server, which increases system utilization and maximizes cost-effectiveness. However, over-provisioning can lead to contention for CPU time if too many vCPUs try to execute tasks on a limited number of physical cores. This situation can degrade performance and introduce latency, especially under heavy workloads.

On the other hand, under-provisioning occurs when fewer vCPUs are assigned than the number of physical cores available. Particularly, this approach minimizes resource contention and may enhance individual VM performance, as each vCPU has less competition for CPU time. However, under-provisioning can lead to inefficient resource utilization since some CPU cores may remain idle or underused.

Both provisioning strategies come with trade-offs, and the ideal choice often depends on various factors:

• workload demands
• budget
• performance goals

Although balancing the above constraints, often, can be tricky, it’s important for us to prioritize one over the other to be able to achieve what is best for our environment. Next, we’ll be discussing some important best practices to help us find the optimal recipe among the factors above.

5. Balancing Performance: Best Practices

Effective provisioning of CPU resources ensures that virtualized environments operate optimally. To achieve a balance between resource efficiency and performance, several best practices can be followed:

• Analyze workloads: Not all VMs require the same processing power. Therefore, it’s a good practice for resource-intensive applications to benefit from dedicated vCPUs, while lightweight applications can share cores more easily
• Leverage hypervisor features: Many modern hypervisors offer tools to control vCPU allocation and mitigate performance bottlenecks. For instance, using dynamic CPU allocation allows the hypervisor to adjust vCPU resources based on demand
• Monitor system performance: Consistent monitoring of CPU usage, latency, and contention rates helps identify whether a system is over-provisioned or under-provisioned. Accordingly, metrics such as CPU ready time (the time a VM waits for CPU resources) can reveal performance issues
• Consider CPU affinity: CPU affinity allows specific vCPUs to be “pinned” to particular physical cores, which can improve cache efficiency and reduce latency for critical applications
• Utilize NUMA-aware allocation: In systems with Non-Uniform Memory Access (NUMA) architecture, each CPU socket has its own memory. Moreover, NUMA-aware allocation helps ensure that vCPUs access memory on the same socket, optimizing performance

Finally, not all of the above best practices can fit every architecture or design. Hence, we should tailor our needs and select what suits us from the list above.

6. Advanced Considerations for CPU Provisioning

In this section, we’ll discuss hyper-threading, vCPU allocation, and CPU affinity in virtualized environments.

6.1. Hyper-Threading and vCPU Allocation

Theoretically, hyper-threading technology enables each physical core to operate as two logical processors, effectively doubling the number of threads a CPU can handle. In addition, hyper-threading allows the hypervisor to allocate more vCPUs per core, providing flexibility in provisioning. However, over-reliance on hyper-threading can lead to performance issues, especially in high-demand environments, as hyper-threaded cores cannot achieve the same performance level as true physical cores.

Let’s have a better illustration with an example:

Physical Core 1 (2 threads) -> vCPU 1, vCPU 2
Physical Core 2 (2 threads) -> vCPU 3, vCPU 4
...
Physical Core 8 (2 threads) -> vCPU 15, vCPU 16

In this configuration, we utilize hyper-threading to allocate 16 vCPUs on 8 physical cores. Each physical core can handle two vCPUs due to hyper-threading. Finally, this setup can work well for workloads with light or moderate processing demands but may not suit intensive computational tasks.

6.2. CPU Affinity and NUMA in Virtualized Environments

For highly performance-sensitive applications, configuring CPU affinity can help. By tying specific vCPUs to particular physical cores, CPU affinity reduces context-switching and cache misses. Accordingly, this approach minimizes the “noisy neighbor” effect, where other VMs compete for the same core resources.

In addition, Non-Uniform Memory Access (NUMA) considerations play a significant role in CPU allocation. In theory, NUMA divides memory and CPU resources across multiple sockets, with each socket having its own local memory. VMs accessing memory within the same NUMA node (that is, the same socket) experience lower latency. Thus, NUMA-aware allocation helps maintain high performance by reducing memory access times for critical VMs.

7. Conclusion

The interplay between multicore CPUs and vCPUs in virtualized environments involves careful management of CPU provisioning.

In this article, we understood that multicore CPUs enable scalable processing by supporting multiple vCPUs. However, efficient provisioning strategies are essential to optimize performance and resource utilization.

Finally, in the rapidly evolving landscape of CPU technology, advanced strategies like hyper-threading, CPU affinity, and NUMA-aware allocation offer further optimization options. Therefore, balancing the benefits of over-provisioning with the risks of resource contention is crucial.