What Is a vCPU? The Advantages of vCPUs in Modern Computing

I admit — I was somewhat baffled the first time I encountered the term ‘vCPU’. The ‘v’ was a new and unexpected addition to my understanding of the computer’s brain. When you break down the acronym, vCPU stands for virtual central processing unit.

Now, you may be asking: “What do you mean it stands for ‘virtual’? How can a physical piece of a computer be ‘virtual’? Make it make sense!” The good news is that the concept is not complicated.

For now, all you need to know is that a vCPU is a software-based version of a physical CPU (the computer’s brain).

It’s a slice of a real processor that’s given to a virtual machine, which is a software-based version of a physical computer. That way, the virtual machine can act like it has its own brain power.

This is useful for giving many users or virtual servers access to part of a CPU. It is mainly used in hosting to run many different applications, each sharing the same CPU and accessing its resources as it needs them.

In this article, I’ll explore everything there is to know about vCPUs, from their mechanics down to their impact on virtual machine performance and technology overall.

At the center of it all is virtualization — the process of creating virtual representations of physical machines.

Essentially, some very smart people created a technology to connect physical machines over a network. These virtual environments are what allow us to have things like cloud computing, virtual private servers, and, you guessed it, vCPUs!

Each vCPU represents a virtual worker that can handle one job at a time.

They’re a part of the real processor, but not a whole one. See, CPUs are made up of cores, which can run a set of computer instructions called a thread. Consumer devices, like the one you’re reading this on, usually have two to ten cores, with really high-end computers having 20 or more. A server’s CPU can have hundreds of cores, each of which is usually capable of running multiple threads at a time.

These cores are all interchangeable, so if you have a CPU with a lot of cores, you can emulate a smaller CPU by dedicating a few of those threads to a virtual machine.

A vCPU is not a dedicated physical CPU core. Since cores are interchangeable, and memory is kept elsewhere on the server, it doesn’t really matter where threads are run. So, to maximize resources, vCPUs share time slots across multiple physical cores on a host machine, only using the threads they need at a given moment.

That’s how you can have lots of virtual machines running on one real computer, since each virtual machine gets at least one of these virtual workers, but can access additional workers when doing something more intense.

Ever hired someone from a temp agency? vCPUs work in a similar way. When you’ve got a job that needs to be done, you submit a request to the agency, and they send you a worker. It might not be the same worker from day to day, but as long as you have a contract with the agency, you’ll always have a worker on hand.

Now, imagine you’re facing a project that needs a lot of help. You can request that the agency send you more workers for as long as you need them. Once the project is finished, you can go back to just hiring one temp until the next time you have a big project.

The host CPU is like that temp agency, with each core being one of its employees and each thread being a specific job. You won’t necessarily get the same core each time you use it, even if you’re running the same process. When you need more processing power, you can just use more threads.

This time-sharing mechanism allows you to host multiple virtual machines on a smaller number of physical cores. Due to this distribution, a vCPU is often more efficient than a single, physical core.

Still, to make all of it work, you need a controller of sorts — that’s where the hypervisor steps in. Also known as a virtual machine monitor, a hypervisor is software that enables a single physical computer to host multiple VMs by virtually partitioning its resources.

It’s the brain of the operation since it smartly allocates memory, processing power, and other resources to these VMs. This allows you to create and manage environments with varying vCPU configurations.

Compared with their physical counterparts, virtual CPUs have some notable entries in their ‘pro’ column.

Resource Efficiency

Simply put, vCPUs offer more bang for your buck. The buck doesn’t stop there, though. The beauty of virtualization is that you can easily reallocate unused vCPUs to other virtual machines.

This adaptability is especially beneficial for hosting providers because it lets them efficiently spread resources around depending on who needs it.

Such a way of using physical CPU resources is wallet-friendly by design. Through virtualization, you can run more applications on less hardware and squeeze more work out of your equipment. This means you can save on operating costs and avoid buying more devices to beef up your setup.

Flexibility and Scalability

Another major “trick” of vCPUs is that they make your system more capable and your network more resilient.

Need more power for a big project? No problem. Need to slow things down to save money? Also not a problem.

You can easily scale up or down processor power for cloud and virtual private servers and adjust your vCPU usage as your business needs change.

The fact that vCPUs powering a virtual machine can be sourced from multiple physical hosts is a massive boon.

Not having a fixed hardware configuration simplifies the process of scaling processor power, allowing you to adjust your system based on what you need.

Enhanced Performance

If you’re like me and don’t like math, you’ll be pleased to hear that vCPU math is rather straightforward: the number of vCPUs assigned to a VM directly impacts its processing power.

More vCPUs typically enable a virtual machine to handle more tasks at the same time, boosting performance as a result. However, how effectively you manage them is also an important performance factor.

Because you can pool your resources (combining vCPUs and a physical server), you can balance the load across multiple vCPUs and move workloads around as needed. If one part of the system is super busy, just send work to another.

This is great for handling demanding tasks or making sure your system stays up and running even when you’re doing maintenance.

To really see how different these two types of CPUs are, I made the following head-to-head table that should paint a clearer picture.

Virtual CPUs vs. Physical CPUs

Think of a physical CPU as the raw processing power, while a virtual CPU is a slice of that power shared among different users. As such, vCPUs offer a more economical and scalable solution that can easily address many different workloads by maximizing the hardware you have.

Use Cases

It wouldn’t be far off for me to say vCPUs have become so common to the point that it’s hard to imagine a situation where strictly leveraging a physical CPU for a workload would make more sense.

Scenarios where vCPUs are advantageous:

• Cloud computing
• Server/application hosting, such as database servers, cloud storage, and virtual private servers
• Testing and development environments
• Data center operations

Of course, it’s not all roses because there are certain vCPU limitations to take into account.

For starters, the maximum number of processor cores that can be assigned to a single VM is 768. A virtual machine can’t use more CPU cores than its host machine has.

And before you say it — no, it’s not a good idea to just amp up the number of vCPUs. Increasing the vCPU count doesn’t always translate to performance gains since it can lead to memory overhead — extra processing power required to manage the VM. The problem here is that the virtualization layer still has to manage all the vCPUs, even when they’re idle.

It’s also worth noting that there are possible performance issues with multi-threaded applications. A VM can assign a vCPU to each thread of a multi-threaded application. The kicker is that the number of vCPUs limits the number of concurrent threads a VM can handle.

When there are too many threads, some must sit and wait until one is available. This delay can significantly impact applications demanding high levels of parallel processing. So, optimizing vCPU allocation requires careful deliberation on your part about how many vCPUs you need for each task.

Now that I’ve gotten the fundamentals out of the way, it’s time to take a look under the hood and see what’s going on.

vCPU Architecture

As I mentioned, a vCPU is an emulation of a physical CPU core within the virtualized environment, created and managed by the hypervisor. When a VM is created, the hypervisor assigns it a specific number of vCPUs depending on the workload, with one vCPU being the bare minimum.

These vCPUs then handle computational tasks in much the same manner as physical cores would. In the ‘eyes’ of the VM’s operating system, each one of them is an individual CPU core, so it treats them like the real deal. The key thing here is to have enough vCPUs at your disposal to take care of business without causing delays due to the pesky memory overhead.

vCPUs have to play nice with physical hardware. When you split a VM across different sections called NUMA nodes (distinct CPU and memory regions), you create a virtual version of this setup called vNUMA. This virtual structure mirrors the underlying physical NUMA architecture, helping the VM work faster by knowing where to find information quickly.

When configuring a virtual machine, the “Cores per Socket” setting determines how vCPUs are presented to the guest operating system. By default, each vCPU is presented as a single-core processor.

You can adjust this setting to create multiple virtual sockets with multiple cores per socket. Let’s say you’re allocating 20 vCPUs with 10 cores per socket. This will result in a configuration with two sockets and ten cores each.

Setting cores per socket to the default setting of one used to be the best way to make virtual machines work smoothly across different parts of the computer. Now, because of how some software and operating systems work, it might cause problems.

Allocation and Scheduling

When you’re assigning vCPUs to virtual machines, you can try a couple of approaches.

The safe/conservative starting point (depending on how you look at it) is a small initial vCPU count, followed by a gradual increase over time. Also referred to as static allocation, this method can prevent overprovisioning and the performance bottlenecks that come as a byproduct. However, you’re going to have a little less flexibility with this approach.

Alternatively, you can give dynamic allocation a try. Here, how many vCPUs will be allocated depends on real-time insights into how much work each virtual machine is doing.

Just like Big Brother, the system is always watching, so by fine-tuning vCPU resources per workload demand, you can make sure each machine has just the right amount of power. The dynamic route provides greater flexibility but also requires more sophisticated hypervisor management.

Speaking of the hypervisor, it divides the physical CPU’s processing time among the vCPUs of multiple VMs since each vCPU represents a share of the physical CPU’s time. Unlike their physical cousins, vCPUs are not statically assigned to specific physical cores. The hypervisor dynamically assigns them to available physical cores based on workload and performance considerations.

It does this using something called CPU scheduling, a technique for efficiently distributing CPU resources among virtual machines. It determines which VMs receive CPU time based on factors like priority and available resources. The idea is that by giving each machine just the right amount of power, you can prevent VMs and processes from competing for resources (also known as CPU contention) and maintain system stability.

Here are some common scheduling techniques used by hypervisors:

• Time-Sharing: Each VM receives a fixed time slice of the CPU.
• Round-Robin: Cyclically allocates fixed time slices to VMs, ensuring equitable distribution but potentially leading to subpar performance for varying workloads.
• Weighted Fair Scheduling (WFS): By assigning weights based on VM importance or resource needs, WFS allocates CPU time proportionally, prioritizing critical workloads.
• Priority-Based: VMs are assigned priorities, with higher-priority ones receiving a larger share of CPU time for critical tasks.
• Credit-Based: Each VM is granted a credit pool. When the VM runs, its credit is depleted. VMs with more remaining credits receive more CPU time, allowing for dynamic allocation based on workload demands.
• Real-Time Scheduling: Guarantees a minimum CPU time allocation for critical applications, ensuring consistent performance for high-priority tasks.

It’s worth knowing you can combine and adjust these techniques based on the specific hypervisor technology you’re using.

Performance Metrics

Performance metrics are important in any industry. Heck, even kindergarteners take standardized tests to determine performance. But with vCPUs, they’re extremely critical; they’re the lifeblood of understanding and improving your entire system’s performance. You can group them according to numerous factors, such as usage, demand, latency, and resource utilization.

Key performance indicators for vCPUs you should know:

• CPU Usage: Percentage of CPU time consumed by the VM.
• CPU Ready Time: Time spent waiting for CPU resources.
• CPU Wait Time: Time the vCPU spends waiting for I/O operations to complete, such as disk or network operations.
• CPU Overhead: Amount of CPU resources used by the hypervisor to manage the virtual machine.
• CPU Utilization: Percentage of allocated vCPU time that is actually used by the VM.
• CPU Throttling: Reduction in CPU performance due to factors like thermal limits, power-saving modes, or hypervisor-imposed limits.
• CPU Demand: Amount of CPU resources requested by the VM.
• CPU Reservation: Guaranteed CPU resources allocated to the VM.
• CPU Shares: Relative weight assigned to a VM for CPU allocation.
• CPU Latency: Time taken for a CPU request to be serviced.
• Interrupts per Second: Frequency of hardware or software interrupts.
• Context Switches: Number of times the CPU switches between different processes or threads.

These will do the trick when it comes to ensuring optimal VM performance. Feel free to explore deeper and analyze other metrics (there are loads of them) to fine-tune everything.

Monitoring and Optimization Techniques

It’s no secret that monitoring any kind of technology is crucial for maintaining the best possible performance — vCPUs are no different. By identifying VMs with excessive CPU consumption, you can optimize hypervisor settings to get the most out of your setup.

I know, this sounds annoying and complicated, but fret not. You have a range of tools to aid you in this endeavor, including hypervisor-native options like the Windows Server built-in Hyper V Manager and VMware vSphere.

If you prefer or just want to explore other solutions, SolarWinds Virtualization Manager, Nagios, System Center Virtual Machine Manager, and XenServer are among those worth looking into.

There are a few things you can do on your end. Expert admins (i.e., people smarter than me) agree that it’s good to start lean with a single vCPU and closely monitor its usage. If consumption consistently exceeds 80%, incrementally add vCPUs while keeping an eye on things.

You can also use CPU affinity, which tells your computer to make specific programs run on certain parts of the processor. Usually, the computer decides this (the OS’s scheduler, to be more precise) based on factors like load balancing and priority, but you can take control and choose where each program goes to make it run faster.

Moreover, consider grouping different virtual machines or programs together and giving them specific amounts of processor power. This helps keep everything running smoothly, especially if some programs need more power than others, particularly critical VMs.

Another option is to turn on hardware-assisted virtualization features (such as Intel VT-x or AMD-V). These will enhance the efficiency of virtualization and reduce the overhead of virtual machine operations.

Because virtualization is really good at sharing physical hardware resources, vCPUs play a crucial role in cloud computing. They check the three arguably biggest boxes every cloud provider holds in the highest regard: scalability, flexibility, and cost-effectiveness.

Role in Cloud Environments

Look no further than AWS, Azure, Google Cloud, and other cloud platforms where VMs are employed to virtualize the server resources. That way, cloud service providers are able to facilitate a multi-tenant architecture where customers can share these resources.

The inherent nature of VMs allows you to quickly provision or de-provision them to meet fluctuating workloads. Since each VM has its own OS, memory, and other resources that are isolated from the other VMs on the same physical computer, it’s also a secure environment for different applications and users (although hypervisor vulnerabilities remain a problem).

As for pricing, the short answer would be that hourly or per-second billing is the general rule of thumb. I’m afraid I can’t offer a clear-cut answer due to the sheer number of VM instance types. There are general-purpose ones, compute-optimized, accelerator-optimized, memory-optimized, and loads of others — each with varying vCPU-to-memory ratios and pricing structures.

Case Studies

Fancy some practical examples of vCPU usage in cloud computing? I’ve got the goods from different industries.

• Bank of America runs a proprietary private cloud that operates the majority of its technology workloads. In fact, you’d be hard-pressed to find a financial company not involved with the cloud in one way or another.

• AWS offers Virtual Desktop Infrastructure (VDI) solutions for a variety of users, including developers, students, teachers, and more. One such is FOX Corporation, which transitioned nearly its entire workforce to Amazon WorkSpaces and its flexible remote desktop environment amidst the COVID-19 pandemic.

• In healthcare, businesses are putting vCPUs to work in myriad ways. For example, Canary Speech uses Azure services to process voice samples from conversations between patients and clinicians within seconds, leading to actionable real-time information used to evaluate and monitor for early disease detection.

• Similarly, Mount Sinai is using an integrated cloud-based medical system that is used to pinpoint risks to patients, such as malnutrition, delirium, and falls. As a result, hospital stays have decreased, and mortality rates are slowly but surely dropping.

• On a more abstract level, Google has recently launched Google Axion Processors, the company’s first custom Arm-based CPUs designed for the data center. A bunch of Google’s services will use cloud processing that leverages the new CPU, with Google Earth Engine and the YouTube Ads platform among the first ones to already do so.

These are just a fraction of instances where vCPUs are powering cloud services. It comes as no surprise that most cloud providers are building industry-specific cloud platforms and capabilities to better address the demands and concerns affecting each industry.

Keeping up with the demands of today’s complex workloads is a real challenge. Technology is changing so fast, it’s hard to stay ahead. Even so, there are some exciting trends worth following.

Advances in Virtualization Technology

Dynamic vCPU allocation has been a major development recently, thanks to ongoing improvements to existing hypervisors. The more advanced scheduling algorithms are powering the adjustment of vCPU resources on the fly based on workload fluctuations, further pushing resource management and profitability.

On the security front, hardware-based isolation and encryption are providing tighter protection for vCPUs and sensitive data.

For instance, Intel SGX protects data actively being used in the processor and memory by creating a trusted execution environment called an enclave. AMD’s SEV protects Linux KVM virtual machines by transparently encrypting the memory of each VM with a unique key.

As a form of hardware-assisted virtualization, advanced features such as nested virtualization are becoming more common, allowing for an Inception-like possibility of virtualization within virtualized environments. This includes integrating specialized hardware accelerators (GPUs, field-programmable gate arrays) into virtual environments to enhance performance for AI and data-intensive applications.

While I’m on the subject of AI, leveraging machine learning to automate vCPU provisioning is gaining traction. It’s a safe bet that AI will make use of predictive analytics to optimize resource allocation by anticipating demand fluctuations while also tracking real-time workload patterns and performance metrics to allocate vCPUs on the go.

Sophisticated provisioning algorithms aimed at balancing between performance and cost are another area of development that might rise in prominence. By considering a wider range of factors like logs and machine-level metrics, these algorithms could provide more precise vCPU management, directly impacting the bottom line.

Finally, there’s quantum computing (the next tech buzzword, it seems). While still a nascent technology, research into merging it with traditional vCPUs holds promise for creating hybrid systems capable of harnessing both classical and quantum processing power.

Impact on Businesses

How evolving vCPU technologies will affect business strategies is anyone’s guess. Even so, it’s reasonable to assume we’ll see more businesses adopt flexible pricing models, such as pay-per-use, enabled by efficient and scalable vCPU allocations that will cut down on operational costs.

I’d wager that improved security and encryption capabilities will help businesses not only comply with stringent regulations but also expand their product/service line, catering to more customers.

For my part, I feel a lot will depend on industry developments. Here’s what I mean by that: Broadcom’s acquisition of VMware is already garnering talks of revirtualization (migrating to new hypervisors) and devirtualization due to increased expenses with on-premises virtualization.

That’s a radically different direction.

Gartner anticipates that it could take between five to ten years for devirtualization to reach widespread adoption and reach the “plateau of productivity,” where the technology becomes mature and widely accepted.

If anything, this is yet another proof of the ever-changing nature of virtualization technology and the importance of tracking developments to stay afloat.

vCPUs are the foundational building blocks of modern computing, serving as the virtual workhorses that power applications and services. The more you understand them, the more it becomes clear that embracing vCPUs is not just about keeping up with the latest gadgets and gizmos — it’s about positioning your business to be successful.

From cutting down on costs to taking the productivity of IT infrastructure to new heights, the benefits are plentiful and undeniable. The need for better performance, efficiency, and adaptability are always going to be recurring themes, but hey, that’s what makes all of this exciting!