What is the main disadvantage of VM?

A virtual machine (VM) is an emulation of a computer system implemented in software. VMs provide many advantages like portability, isolation, and the ability to run multiple operating systems simultaneously on a single physical machine. However, VMs also come with certain disadvantages that must be considered before deployment.

What is the main disadvantage of VM?

The main disadvantage of using a VM is reduced performance compared to running an application natively on the host hardware.

VMs share physical resources like CPU, memory, storage, and network bandwidth with other VMs and the host machine. This can limit the performance of applications running inside the VM. The degree of impact varies based on multiple factors outlined ahead.

How VMs Impact Performance

Several elements contribute to the performance overheads with virtualization:

  • Additional Software Layers – Hypervisors and emulated hardware create additional software abstraction layers. These extra layers can cause latency and processing overheads.
  • Resource Competition – Competition for physical resources like CPU time slices and memory pages amongst multiple VMs can limit performance.
  • Limited Access to Physical Hardware – VMs get virtualized hardware rather than direct access to advanced physical hardware features. This can constrain performance for hardware-dependent apps.
  • Storage/Network Overheads – Virtual storage devices and virtual networks have additional delays compared to native hardware. This causes storage/network I/O bottlenecks.

However, the degree of performance impact varies significantly based on factors like:

  • VM configuration parameters
  • Workload characteristics
  • Host hardware specs
  • Hypervisor overhead
  • Number of VMs contending for resources

Mitigating the Performance Impact

While VMs have slower performance than native hardware, various techniques can help minimize the overheads:

  • Carefully size VMs with adequate vCPU, memory, storage, and network bandwidth as per application demands. Overprovisioning leads to resource contention.
  • Leverage paravirtualization and SR-IOV to give VMs better access to host hardware.
  • Use newer generation hypervisors like KVM which have lower overheads.
  • Choose host hardware judiciously. Servers with fast multi-core CPUs, SSD storage and high network bandwidth work better.
  • Keep the number of VMs on a host in check to reduce resource competition.
  • Profile workloads inside VMs to identify and eliminate bottlenecks.

So while performance impact is the major downside, careful planning, sizing and optimizations where possible can minimize the overheads for many workloads.

When Performance Becomes Critical

For certain types of applications, small reductions in performance are acceptable. But for workloads where performance is critical, the overheads imposed by VMs may become deal-breakers:

  • Latency Sensitive Workloads – High frequency trading systems, gaming servers supporting online multiplayer games need very low latency. Even minor lags or jitter can disrupt these applications.
  • Data and Computation Intensive Apps – Workloads like batch processing big data pipelines, scientific modeling/simulation apps require maximum CPU, storage and network throughput to operate smoothly.
  • Real-Time and Embedded Systems – Industrial equipment, automotive systems, IoT edge devices often run advanced algorithms needing unhindered access to specialized hardware.

For such use-cases, either a bare-metal deployment or use of Linux containers may work better by eliminating the hypervisor layer and offering native hardware access.

Conclusion

In conclusion, while VMs provide immense flexibility, security and isolation benefits, the key disadvantage is reduced performance vs bare metal. However savvy resource allocation and sizing can help mediate the overheads for less demanding workloads. But for applications where performance reigns supreme, containers or bare-metal may be better deployment options. Carefully evaluating your app’s workload patterns and performance needs is crucial before opting for virtualization.

Keywords: virtual machine disadvantage, VM performance overhead, VMs vs bare metal, mitigating VM slowness.

Frequently Asked Questions

Q1: Why are VMs slower than physical servers?
A1: VMs incur performance overheads due to extra software layers for virtualization like the hypervisor, emulated hardware, plus sharing physical host resources like CPU, memory and storage with other VMs. This added overhead makes them slower than dedicated physical servers.

Q2: When should you not use VMs?
A2: Avoid VMs for highly latency-sensitive systems like gaming servers, high-frequency stock trading apps. Also for data/compute intensive big data and scientific workloads needing maximum hardware utilization. And embedded systems needing specialized hardware access.

Q3: Are Docker containers better than VMs performance-wise?
A3: Yes, containers have much lower resource overheads than VMs since they share the host OS kernel and don’t need an extra hypervisor layer or virtual hardware, giving near-native performance.

Q4: How can you make VMs faster?
A4: Using newer generation hypervisors, choosing host hardware judiciously, minimizing resource overprovisioning, and profiling workloads thoroughly helps optimize VM performance significantly.

Q5: What causes storage bottlenecks with virtualization?
A5: Virtual storage devices emulated via hypervisor share underlying physical storage with extra software layers. This causes I/O overheads, contention and thus hurts storage throughput for VMs.

Q6: Why does network performance reduce with VMs?
A6: Virtual networks present emulated NICs to VMs. The network traffic goes through extra software layers & virtual switches causing CPU load & latency vs direct hardware NIC access on bare-metal.

Q7: Why are real-time applications unsuitable for VMs?
A7: Real-time and embedded systems often use ISA/hardware-specific optimizations, specialized I/O etc. VMs abstract access to underlying hardware which causes performance issues for such workloads.

Q8: Which hypervisors have the least performance overheads?
A8: KVM and VMware ESXi tend to have lower resource overheads than platforms like VirtualBox, mainly due to bare-metal hypervisors and hardware acceleration support.

Q9: How does paravirtualization improve VM performance?
A9: Paravirtualization provides guest OSes awareness of the hypervisor and ability to directly interface with host hardware, avoiding emulated devices, thus lowering CPU and I/O overheads.

Q10: Why overprovisioning resources hurts VM performance?
A10: Allocating more vCPUs, memory & storage than needed oversubscribes host resources, leading to contention amongst VMs which lowers performance due to resource starvation & caching inefficiencies.

Q11: How does SR-IOV boost network performance for VMs?
A11: SR-IOV allows near-native access to host network adapters for VMs bypassing the virtual switch layer, thus reducing I/O overhead & latency for higher throughput.

Q12: What hardware considerations maximize VM performance?
A12: Servers with modern multi-core CPUs, PCIe SSD storage, faster/bigger RAM and high bandwidth NICs translate to better performance for VMs via reduced hypervisor overhead & faster resource access.

Q13: Why consolidate multiple VMs onto a single server?
A13: Consolidating multiple VMs onto a powerful server increases resource utilization while reducing hardware costs. However, keep the VM density in check to avoid resource contention issues.

Q14: How can you profile VM workloads to fix bottlenecks?
A14: Hypervisor, OS and application monitoring tools help deeply analyze resource utilization, contention, and workload patterns inside VMs to identify and eliminate performance hotspots.

Q15: Which applications are less affected by VM overheads?
A15: Applications like web servers, lightly loaded databases, dev/test environments, microservices etc. which aren’t latency sensitive can comfortably run inside VMs without significant performance issues.

Q16: How frequently should you resize and tune VMs?
A16: Continuously monitor VM workloads and resize resource allocations accordingly every few months. Also tune configurations to align with evolving workload patterns for optimal performance.

Q17: Can changing the hypervisor improve VM performance?
A17: Yes, switching to a Type 1 bare-metal hypervisor like ESXi with a smaller software footprint than hosted hypervisors lessens overhead and may improve performance.

Q18: What techniques reduce VM storage bottlenecks?
A18: Leveraging faster storage media like SSDs, distributed virtual SANs, enabling caching, using paravirtualized devices and optimizing I/O workload patterns helps mitigate VM storage issues.

Q19: When is bare-metal deployment a better choice over VMs?
A19: For maximum performance with heavy, complex workloads without flexibility needs, running apps directly on dedicated physical servers avoids virtualization overhead.

Q20: How to choose between containers and VMs?
A20: Evaluate app portability needs, resource efficiency, hardware access necessity and security requirements. Containers excel on efficiency while VMs suit apps needing total isolation or full hardware virtualization.

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