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docs: backport to old versions
This commit is contained in:
Thomas Tendyck
Thomas Tendyck
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commit
712ff90ba0
@ -0,0 +1,11 @@
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# Impact of runtime encryption on compute performance
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All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
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## AMD and Azure benchmarking
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AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
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## AMD and Google benchmarking
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Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
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@ -1,18 +1,10 @@
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# Performance analysis of Constellation
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This section provides a comprehensive examination of the performance characteristics of Constellation, encompassing various aspects, including runtime encryption, I/O benchmarks, and real-world applications.
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This section provides a comprehensive examination of the performance characteristics of Constellation.
|
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## Impact of runtime encryption on performance
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## Runtime encryption
|
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All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
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|
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### AMD and Azure benchmarking
|
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|
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AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
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|
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### AMD and Google benchmarking
|
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|
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Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
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Runtime encryption affects compute performance. [Benchmarks by Azure and Google](compute.md) show that the performance degradation of Confidential VMs (CVMs) is small, ranging from 2% to 8% for compute-intensive workloads.
|
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## I/O performance benchmarks
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@ -33,6 +33,10 @@ You don't need to verify the Constellation node images. This is done automatical
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## Verify the signature
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:::info
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This guide assumes Linux on an amd64 processor. The exact steps for other platforms differ slightly.
|
||||
:::
|
||||
|
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First, [install the Cosign CLI](https://docs.sigstore.dev/system_config/installation). Next, [download](https://github.com/edgelesssys/constellation/releases) and verify the signature that accompanies your CLI executable, for example:
|
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```shell-session
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@ -0,0 +1,11 @@
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# Impact of runtime encryption on compute performance
|
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|
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All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
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## AMD and Azure benchmarking
|
||||
|
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AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
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## AMD and Google benchmarking
|
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|
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Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
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@ -1,18 +1,10 @@
|
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# Performance analysis of Constellation
|
||||
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation, encompassing various aspects, including runtime encryption, I/O benchmarks, and real-world applications.
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation.
|
||||
|
||||
## Impact of runtime encryption on performance
|
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## Runtime encryption
|
||||
|
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All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
||||
### AMD and Azure benchmarking
|
||||
|
||||
AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
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|
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### AMD and Google benchmarking
|
||||
|
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Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
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Runtime encryption affects compute performance. [Benchmarks by Azure and Google](compute.md) show that the performance degradation of Confidential VMs (CVMs) is small, ranging from 2% to 8% for compute-intensive workloads.
|
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|
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## I/O performance benchmarks
|
||||
|
||||
|
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@ -33,6 +33,10 @@ You don't need to verify the Constellation node images. This is done automatical
|
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|
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## Verify the signature
|
||||
|
||||
:::info
|
||||
This guide assumes Linux on an amd64 processor. The exact steps for other platforms differ slightly.
|
||||
:::
|
||||
|
||||
First, [install the Cosign CLI](https://docs.sigstore.dev/system_config/installation). Next, [download](https://github.com/edgelesssys/constellation/releases) and verify the signature that accompanies your CLI executable, for example:
|
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|
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```shell-session
|
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|
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@ -0,0 +1,11 @@
|
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# Impact of runtime encryption on compute performance
|
||||
|
||||
All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
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## AMD and Azure benchmarking
|
||||
|
||||
AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
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|
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## AMD and Google benchmarking
|
||||
|
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Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
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@ -1,18 +1,10 @@
|
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# Performance analysis of Constellation
|
||||
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation, encompassing various aspects, including runtime encryption, I/O benchmarks, and real-world applications.
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation.
|
||||
|
||||
## Impact of runtime encryption on performance
|
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## Runtime encryption
|
||||
|
||||
All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
||||
### AMD and Azure benchmarking
|
||||
|
||||
AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
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|
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### AMD and Google benchmarking
|
||||
|
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Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
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Runtime encryption affects compute performance. [Benchmarks by Azure and Google](compute.md) show that the performance degradation of Confidential VMs (CVMs) is small, ranging from 2% to 8% for compute-intensive workloads.
|
||||
|
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## I/O performance benchmarks
|
||||
|
||||
|
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@ -33,6 +33,10 @@ You don't need to verify the Constellation node images. This is done automatical
|
||||
|
||||
## Verify the signature
|
||||
|
||||
:::info
|
||||
This guide assumes Linux on an amd64 processor. The exact steps for other platforms differ slightly.
|
||||
:::
|
||||
|
||||
First, [install the Cosign CLI](https://docs.sigstore.dev/system_config/installation). Next, [download](https://github.com/edgelesssys/constellation/releases) and verify the signature that accompanies your CLI executable, for example:
|
||||
|
||||
```shell-session
|
||||
|
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@ -0,0 +1,11 @@
|
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# Impact of runtime encryption on compute performance
|
||||
|
||||
All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
||||
## AMD and Azure benchmarking
|
||||
|
||||
AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
||||
|
||||
## AMD and Google benchmarking
|
||||
|
||||
Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
||||
@ -1,18 +1,10 @@
|
||||
# Performance analysis of Constellation
|
||||
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation, encompassing various aspects, including runtime encryption, I/O benchmarks, and real-world applications.
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation.
|
||||
|
||||
## Impact of runtime encryption on performance
|
||||
## Runtime encryption
|
||||
|
||||
All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
||||
### AMD and Azure benchmarking
|
||||
|
||||
AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
||||
|
||||
### AMD and Google benchmarking
|
||||
|
||||
Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
||||
Runtime encryption affects compute performance. [Benchmarks by Azure and Google](compute.md) show that the performance degradation of Confidential VMs (CVMs) is small, ranging from 2% to 8% for compute-intensive workloads.
|
||||
|
||||
## I/O performance benchmarks
|
||||
|
||||
|
||||
@ -33,6 +33,10 @@ You don't need to verify the Constellation node images. This is done automatical
|
||||
|
||||
## Verify the signature
|
||||
|
||||
:::info
|
||||
This guide assumes Linux on an amd64 processor. The exact steps for other platforms differ slightly.
|
||||
:::
|
||||
|
||||
First, [install the Cosign CLI](https://docs.sigstore.dev/system_config/installation). Next, [download](https://github.com/edgelesssys/constellation/releases) and verify the signature that accompanies your CLI executable, for example:
|
||||
|
||||
```shell-session
|
||||
|
||||
@ -0,0 +1,11 @@
|
||||
# Impact of runtime encryption on compute performance
|
||||
|
||||
All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
||||
## AMD and Azure benchmarking
|
||||
|
||||
AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
||||
|
||||
## AMD and Google benchmarking
|
||||
|
||||
Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
||||
@ -1,18 +1,10 @@
|
||||
# Performance analysis of Constellation
|
||||
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation, encompassing various aspects, including runtime encryption, I/O benchmarks, and real-world applications.
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation.
|
||||
|
||||
## Impact of runtime encryption on performance
|
||||
## Runtime encryption
|
||||
|
||||
All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
||||
### AMD and Azure benchmarking
|
||||
|
||||
AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
||||
|
||||
### AMD and Google benchmarking
|
||||
|
||||
Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
||||
Runtime encryption affects compute performance. [Benchmarks by Azure and Google](compute.md) show that the performance degradation of Confidential VMs (CVMs) is small, ranging from 2% to 8% for compute-intensive workloads.
|
||||
|
||||
## I/O performance benchmarks
|
||||
|
||||
|
||||
@ -33,6 +33,10 @@ You don't need to verify the Constellation node images. This is done automatical
|
||||
|
||||
## Verify the signature
|
||||
|
||||
:::info
|
||||
This guide assumes Linux on an amd64 processor. The exact steps for other platforms differ slightly.
|
||||
:::
|
||||
|
||||
First, [install the Cosign CLI](https://docs.sigstore.dev/system_config/installation). Next, [download](https://github.com/edgelesssys/constellation/releases) and verify the signature that accompanies your CLI executable, for example:
|
||||
|
||||
```shell-session
|
||||
|
||||
@ -0,0 +1,11 @@
|
||||
# Impact of runtime encryption on compute performance
|
||||
|
||||
All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
||||
## AMD and Azure benchmarking
|
||||
|
||||
AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
||||
|
||||
## AMD and Google benchmarking
|
||||
|
||||
Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
||||
@ -1,18 +1,10 @@
|
||||
# Performance analysis of Constellation
|
||||
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation, encompassing various aspects, including runtime encryption, I/O benchmarks, and real-world applications.
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation.
|
||||
|
||||
## Impact of runtime encryption on performance
|
||||
## Runtime encryption
|
||||
|
||||
All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
||||
### AMD and Azure benchmarking
|
||||
|
||||
AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
||||
|
||||
### AMD and Google benchmarking
|
||||
|
||||
Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
||||
Runtime encryption affects compute performance. [Benchmarks by Azure and Google](compute.md) show that the performance degradation of Confidential VMs (CVMs) is small, ranging from 2% to 8% for compute-intensive workloads.
|
||||
|
||||
## I/O performance benchmarks
|
||||
|
||||
|
||||
@ -33,6 +33,10 @@ You don't need to verify the Constellation node images. This is done automatical
|
||||
|
||||
## Verify the signature
|
||||
|
||||
:::info
|
||||
This guide assumes Linux on an amd64 processor. The exact steps for other platforms differ slightly.
|
||||
:::
|
||||
|
||||
First, [install the Cosign CLI](https://docs.sigstore.dev/system_config/installation). Next, [download](https://github.com/edgelesssys/constellation/releases) and verify the signature that accompanies your CLI executable, for example:
|
||||
|
||||
```shell-session
|
||||
|
||||
@ -0,0 +1,11 @@
|
||||
# Impact of runtime encryption on compute performance
|
||||
|
||||
All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
||||
## AMD and Azure benchmarking
|
||||
|
||||
AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
||||
|
||||
## AMD and Google benchmarking
|
||||
|
||||
Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
||||
@ -1,18 +1,10 @@
|
||||
# Performance analysis of Constellation
|
||||
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation, encompassing various aspects, including runtime encryption, I/O benchmarks, and real-world applications.
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation.
|
||||
|
||||
## Impact of runtime encryption on performance
|
||||
## Runtime encryption
|
||||
|
||||
All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
||||
### AMD and Azure benchmarking
|
||||
|
||||
AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
||||
|
||||
### AMD and Google benchmarking
|
||||
|
||||
Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
||||
Runtime encryption affects compute performance. [Benchmarks by Azure and Google](compute.md) show that the performance degradation of Confidential VMs (CVMs) is small, ranging from 2% to 8% for compute-intensive workloads.
|
||||
|
||||
## I/O performance benchmarks
|
||||
|
||||
|
||||
@ -4,12 +4,25 @@ Constellation integrates the native load balancers of each CSP. Therefore, to ex
|
||||
|
||||
## Internet-facing LB service on AWS
|
||||
|
||||
To expose your application service externally you might want to use a Kubernetes Service of type `LoadBalancer`. On AWS, load-balancing is achieved through the [AWS Load Balancing Controller](https://kubernetes-sigs.github.io/aws-load-balancer-controller) as in the managed EKS.
|
||||
To expose your application service externally you might want to use a Kubernetes Service of type `LoadBalancer`. On AWS, load-balancing is achieved through the [AWS Load Balancer Controller](https://kubernetes-sigs.github.io/aws-load-balancer-controller) as in the managed EKS.
|
||||
|
||||
Since recent versions, the controller deploy an internal LB by default requiring to set an annotation `service.beta.kubernetes.io/aws-load-balancer-scheme: internet-facing` to have an internet-facing LB. For more details, see the [official docs](https://kubernetes-sigs.github.io/aws-load-balancer-controller/v2.2/guide/service/nlb/).
|
||||
Since recent versions, the controller deploy an internal LB by default requiring to set an annotation `service.beta.kubernetes.io/aws-load-balancer-scheme: internet-facing` to have an internet-facing LB. For more details, see the [official docs](https://kubernetes-sigs.github.io/aws-load-balancer-controller/v2.7/guide/service/nlb/).
|
||||
|
||||
For general information on LB with AWS see [Network load balancing on Amazon EKS](https://docs.aws.amazon.com/eks/latest/userguide/network-load-balancing.html).
|
||||
|
||||
:::caution
|
||||
Before terminating the cluster, all LB backed services should be deleted, so that the controller can cleanup the related resources.
|
||||
:::
|
||||
|
||||
## Ingress on AWS
|
||||
|
||||
The AWS Load Balancer Controller also provisions `Ingress` resources of class `alb`.
|
||||
AWS Application Load Balancers (ALBs) can be configured with a [`target-type`](https://kubernetes-sigs.github.io/aws-load-balancer-controller/v2.7/guide/ingress/annotations/#target-type).
|
||||
The target type `ip` requires using the EKS container network solution, which makes it incompatible with Constellation.
|
||||
If a service can be exposed on a `NodePort`, the target type `instance` can be used.
|
||||
|
||||
See [Application load balancing on Amazon EKS](https://docs.aws.amazon.com/eks/latest/userguide/alb-ingress.html) for more information.
|
||||
|
||||
:::caution
|
||||
Ingress handlers backed by AWS ALBs reside outside the Constellation cluster, so they shouldn't be handling sensitive traffic!
|
||||
:::
|
||||
|
||||
@ -0,0 +1,11 @@
|
||||
# Impact of runtime encryption on compute performance
|
||||
|
||||
All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
||||
## AMD and Azure benchmarking
|
||||
|
||||
AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
||||
|
||||
## AMD and Google benchmarking
|
||||
|
||||
Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
||||
@ -1,18 +1,10 @@
|
||||
# Performance analysis of Constellation
|
||||
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation, encompassing various aspects, including runtime encryption, I/O benchmarks, and real-world applications.
|
||||
This section provides a comprehensive examination of the performance characteristics of Constellation.
|
||||
|
||||
## Impact of runtime encryption on performance
|
||||
## Runtime encryption
|
||||
|
||||
All nodes in a Constellation cluster are executed inside Confidential VMs (CVMs). Consequently, the performance of Constellation is inherently linked to the performance of these CVMs.
|
||||
|
||||
### AMD and Azure benchmarking
|
||||
|
||||
AMD and Azure have collectively released a [performance benchmark](https://community.amd.com/t5/business/microsoft-azure-confidential-computing-powered-by-3rd-gen-epyc/ba-p/497796) for CVMs that utilize 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. This benchmark, which included a variety of mostly compute-intensive tests such as SPEC CPU 2017 and CoreMark, demonstrated that CVMs experience only minor performance degradation (ranging from 2% to 8%) when compared to standard VMs. Such results are indicative of the performance that can be expected from compute-intensive workloads running with Constellation on Azure.
|
||||
|
||||
### AMD and Google benchmarking
|
||||
|
||||
Similarly, AMD and Google have jointly released a [performance benchmark](https://www.amd.com/system/files/documents/3rd-gen-epyc-gcp-c2d-conf-compute-perf-brief.pdf) for CVMs employing 3rd Gen AMD EPYC processors (Milan) with SEV-SNP. With high-performance computing workloads such as WRF, NAMD, Ansys CFS, and Ansys LS_DYNA, they observed analogous findings, with only minor performance degradation (between 2% and 4%) compared to standard VMs. These outcomes are reflective of the performance that can be expected for compute-intensive workloads running with Constellation on GCP.
|
||||
Runtime encryption affects compute performance. [Benchmarks by Azure and Google](compute.md) show that the performance degradation of Confidential VMs (CVMs) is small, ranging from 2% to 8% for compute-intensive workloads.
|
||||
|
||||
## I/O performance benchmarks
|
||||
|
||||
|
||||
@ -40,6 +40,11 @@
|
||||
"id": "overview/performance/performance"
|
||||
},
|
||||
"items": [
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "Compute benchmarks",
|
||||
"id": "overview/performance/compute"
|
||||
},
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "I/O benchmarks",
|
||||
|
||||
@ -40,6 +40,11 @@
|
||||
"id": "overview/performance/performance"
|
||||
},
|
||||
"items": [
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "Compute benchmarks",
|
||||
"id": "overview/performance/compute"
|
||||
},
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "I/O benchmarks",
|
||||
|
||||
@ -40,6 +40,11 @@
|
||||
"id": "overview/performance/performance"
|
||||
},
|
||||
"items": [
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "Compute benchmarks",
|
||||
"id": "overview/performance/compute"
|
||||
},
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "I/O benchmarks",
|
||||
|
||||
@ -40,6 +40,11 @@
|
||||
"id": "overview/performance/performance"
|
||||
},
|
||||
"items": [
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "Compute benchmarks",
|
||||
"id": "overview/performance/compute"
|
||||
},
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "I/O benchmarks",
|
||||
|
||||
@ -40,6 +40,11 @@
|
||||
"id": "overview/performance/performance"
|
||||
},
|
||||
"items": [
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "Compute benchmarks",
|
||||
"id": "overview/performance/compute"
|
||||
},
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "I/O benchmarks",
|
||||
|
||||
@ -40,6 +40,11 @@
|
||||
"id": "overview/performance/performance"
|
||||
},
|
||||
"items": [
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "Compute benchmarks",
|
||||
"id": "overview/performance/compute"
|
||||
},
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "I/O benchmarks",
|
||||
|
||||
@ -40,6 +40,11 @@
|
||||
"id": "overview/performance/performance"
|
||||
},
|
||||
"items": [
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "Compute benchmarks",
|
||||
"id": "overview/performance/compute"
|
||||
},
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "I/O benchmarks",
|
||||
|
||||
@ -40,6 +40,11 @@
|
||||
"id": "overview/performance/performance"
|
||||
},
|
||||
"items": [
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "Compute benchmarks",
|
||||
"id": "overview/performance/compute"
|
||||
},
|
||||
{
|
||||
"type": "doc",
|
||||
"label": "I/O benchmarks",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user