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Karan Singh Karan is a highly experienced DevOps Engineer with over 13 years of experience in the IT industry. Throughout his career, he has developed a deep understanding of the principles of DevOps, including continuous integration and deployment, automated testing, and infrastructure as code.

Bare Metal vs. VMs for Kubernetes: Performance Benchmarks

1 min read

In the realm of container orchestration, Kubernetes reigns supreme, but the question of whether to deploy it on bare metal or virtual machines (VMs) remains a topic of debate. While some favor bare metal’s raw performance, others prefer the flexibility and ease of management offered by VMs. To shed light on this ongoing debate, Gcore conducted a comprehensive performance comparison of Kubernetes clusters deployed on both bare metal and VMs.

Unveiling the Architectural Difference

When deploying Kubernetes on VMs, an additional layer of infrastructure, encompassing a hypervisor and a guest operating system, is introduced compared to bare metal (BM) deployments. These layers consume physical CPU and RAM resources, reducing the compute capacity available to workloads. Additionally, virtualization impacts network and storage performance, as virtual networks and storages are inherently slower than their physical counterparts.

In contrast, bare metal deployments eliminate these intermediary layers, allowing containerized applications to directly access the server’s physical resources. This translates to enhanced performance and responsiveness for demanding workloads.

Benchmarking Bare Metal vs. VM Kubernetes Performance

To gain a comprehensive understanding of the performance differences between VM and bare metal Kubernetes clusters, Gcore evaluated the following metrics:

  • CPU: Speed and utilization
  • RAM: Latency
  • Storage: Transactions per second (TPS) and latency
  • Network: Bandwidth and latency

To ensure a fair comparison, all test applications were containerized and deployed on the respective worker nodes.

Test Conditions and Cluster Configurations

The test utilized Kubernetes clusters running on Gcore Managed Kubernetes, ensuring that the results remain relevant to vanilla Kubernetes, as managed Kubernetes doesn’t affect worker node performance. To maintain consistent conditions for workloads, similar configurations were chosen for both VM and bare metal worker nodes. Here’s an example of such comparative configurations:

  • Bare metal worker node: 1x Intel Xeon E-2388 8C/16T 3.2 GHz / 64 GB / Ubuntu 22.04
  • Virtual machine worker node: 16 vCPU / 64 GiB Memory / Ubuntu 22.04

Test Results: Unveiling Bare Metal Superiority

Gcore’s testing revealed a clear advantage of bare metal Kubernetes over VM Kubernetes in terms of performance. The bare metal cluster consistently outperformed its VM counterpart across all test metrics. Specifically, the bare metal cluster demonstrated:

  • Twice the CPU speed
  • Three times faster RAM latency
  • Approximately double the TPS and significantly lower storage latency
  • Five times greater network bandwidth

Implications for Your Workloads

The superior performance of bare metal Kubernetes translates to several benefits for workloads:

  • Reduced latency for real-time applications, such as IoT devices, gaming, and real-time analytics
  • Improved responsiveness for applications requiring faster processing and data retrieval
  • Enhanced scalability to handle increasing workload demands

Conclusion: Choosing the Right Deployment Approach

The choice between bare metal and VM Kubernetes depends on the specific needs and requirements of an organization. If raw performance and low latency are paramount, bare metal emerges as the clear winner. However, if flexibility, ease of management, and cost-efficiency are priorities, VMs may be a better fit. Ultimately, the decision should be guided by a thorough assessment of workload requirements and organizational goals.

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Karan Singh Karan is a highly experienced DevOps Engineer with over 13 years of experience in the IT industry. Throughout his career, he has developed a deep understanding of the principles of DevOps, including continuous integration and deployment, automated testing, and infrastructure as code.

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