diff --git a/content/blog/adventures-in-dc-measurement.md b/content/blog/adventures-in-dc-measurement.md index 0667fad..5cd5b2d 100644 --- a/content/blog/adventures-in-dc-measurement.md +++ b/content/blog/adventures-in-dc-measurement.md @@ -18,7 +18,7 @@ If you wann get the details of our journey, why we are integrating it into our t ## Why? Current energy measurement in software that works in a small time resolution is mostly done in with -[RAPL](https://01.org/blogs/2014/running-average-power-limit-%E2%80%93-rapl) interfaces. +[RAPL](http://web.archive.org/web/20190220011235/https://01.org/blogs/2014/running-average-power-limit-%E2%80%93-rapl) interfaces. Typically either [directly](https://web.eece.maine.edu/~vweaver/projects/rapl/) or through tools like [Scaphandre](https://github.com/hubblo-org/scaphandre). @@ -95,7 +95,7 @@ The linux measurement client is a breeze and signals looked directly accurate wi For inital testing we opted for a simple 0.5 Ohms inline shunt resistor that is measured in the differential measurement mode. -The code for importing the measurements is very simple Python. If you want to have a look at it, you can find it in our [dev branch](https://github.com/green-coding-solutions/green-metrics-tool/blob/dev/tools/dc_converter.py). +The code for importing the measurements is very simple Python. If you want to have a look at it, you can find it in our [dev branch](https://github.com/green-coding-solutions/green-metrics-tool/blob/1c085bf4/tools/dc_converter.py). ## Judgment of the measurement quality diff --git a/content/blog/blauer-engel-green-metrics-tool.md b/content/blog/blauer-engel-green-metrics-tool.md index 6a76c0e..5cef338 100644 --- a/content/blog/blauer-engel-green-metrics-tool.md +++ b/content/blog/blauer-engel-green-metrics-tool.md @@ -11,13 +11,13 @@ socialmedia_preview: "/img/social-media-previews/blue_angel_certification_300.we *Please find the english version below* -Wir freuen uns, dass unser Green Metrics Tool nun mit dem renommierten **Blauer Engel** für Ressourcen- und Energieeffiziente Softwareprodukte ausgezeichnet wurde. +Wir freuen uns, dass unser Green Metrics Tool nun mit dem renommierten **Blauer Engel** für Ressourcen- und Energieeffiziente Softwareprodukte ausgezeichnet wurde. Diese Zertifizierung unterstreicht unser Engagement für Nachhaltigkeit und Innovation in der Softwareentwicklung. ### Was ist die Blauer Engel Zertifizierung? -Der Blaue Engel ist das offizielle deutsche Umweltzeichen, das seit 1978 vergeben wird. -Es legt strenge Standards für Produkte und Dienstleistungen fest und fördert umweltfreundliche Praktiken. +Der Blaue Engel ist das offizielle deutsche Umweltzeichen, das seit 1978 vergeben wird. +Es legt strenge Standards für Produkte und Dienstleistungen fest und fördert umweltfreundliche Praktiken. Für Software stellen die Richtlinien DE UZ-215 sicher, dass zertifizierte Produkte: - Energie- und Datensparsam sind @@ -35,7 +35,7 @@ Weitere Informationen über die Zertifizierung und den Zertifizierungsprozess fi ### Über das Green Metrics Tool -Unser Green Metrics Tool wurde entwickelt, um Organisationen verwertbare Einblicke in ihre Umweltauswirkungen zu ermöglichen. +Unser Green Metrics Tool wurde entwickelt, um Organisationen verwertbare Einblicke in ihre Umweltauswirkungen zu ermöglichen. Durch die Analyse des Ressourcenverbrauchs, des Energieverbrauchs und des CO2-Fußabdrucks hilft das Tool Unternehmen, datengestützte Entscheidungen zu treffen, um ihren ökologischen Fußabdruck zu verringern. Zu den wichtigsten Funktionen des Green Metrics Tool gehören: @@ -46,12 +46,12 @@ Zu den wichtigsten Funktionen des Green Metrics Tool gehören: Die Zertifizierung bestätigt, dass das Green Metrics Tool nicht nur die Bemühungen um Nachhaltigkeit unterstützt, sondern auch umweltfreundliche Designprinzipien verkörpert. -Weitere Informationen über das Green Metrics Tool finden Sie auf der Produktseite des Tools: [Green Metrics Tool Produktseite](/prudcts/green-metrics-tool/) +Weitere Informationen über das Green Metrics Tool finden Sie auf der Produktseite des Tools: [Green Metrics Tool Produktseite](/products/green-metrics-tool/) ## Warum ressourcen- und energieeffiziente Software wichtig ist -Der Energieverbrauch und die Kohlendioxidemissionen des globalen IT-Sektors steigen rapide an, so dass die Effizienz von Software ein wichtiger Teil der Lösung ist. +Der Energieverbrauch und die Kohlendioxidemissionen des globalen IT-Sektors steigen rapide an, so dass die Effizienz von Software ein wichtiger Teil der Lösung ist. Nachhaltige Software, wie das Green Metrics Tool, hilft dabei: - Senkung des Energieverbrauchs für Software @@ -68,15 +68,15 @@ Die Förderung einer breiteren Akzeptanz umweltfreundlicher Praktiken in der Tec ---- +--- *English version of the blog article* -We are thrilled to announce that our Green Metrics Tool has now been certified with the prestigious **Blauer Engel** certification for resource- and energy-efficient software (DE UZ-215). +We are thrilled to announce that our Green Metrics Tool has now been certified with the prestigious **Blauer Engel** certification for resource- and energy-efficient software (DE UZ-215). This certification underscores our commitment to sustainability and innovation in software development. ### What is the Blauer Engel Certification? -The Blauer Engel (Blue Angel) is Germany's official environmental label, established in 1978. +The Blauer Engel (Blue Angel) is Germany's official environmental label, established in 1978. It sets standards for products and services, promoting environmentally friendly practices. For software, the DE UZ-215 guidelines ensure that certified products: - are energy and data efficient @@ -88,7 +88,7 @@ Achieving this certification means that a product meets the requirements for sus Please find some more details about the certification and it's process in our [Services page for the Blue Angel](/services/blauer-engel/) ### About the Green Metrics Tool -Our Green Metrics Tool is designed to empower organizations with actionable insights into their environmental impact. +Our Green Metrics Tool is designed to empower organizations with actionable insights into their environmental impact. By analyzing resource usage, energy consumption, and carbon footprint, the tool helps businesses make data-driven decisions to reduce their ecological footprint. Key features of the Green Metrics Tool include: @@ -99,12 +99,12 @@ Key features of the Green Metrics Tool include: The certification validates that the Green Metrics Tool not only supports sustainability efforts but also embodies eco-friendly design principles. -Please find some more details about the Green Metrics Tool on it's product page: [Green Metrics Tool product page](/prudcts/green-metrics-tool/) +Please find some more details about the Green Metrics Tool on it's product page: [Green Metrics Tool product page](/products/green-metrics-tool/) ## Why Resource- and Energy-Efficient Software Matters -The global IT sector's energy consumption and carbon emissions are growing rapidly, making software efficiency a critical part of the solution. +The global IT sector's energy consumption and carbon emissions are growing rapidly, making software efficiency a critical part of the solution. Sustainable software, like the Green Metrics Tool helps: - Reduces energy usage for software diff --git a/content/blog/cloud-energy-usage-data.md b/content/blog/cloud-energy-usage-data.md index d24ea25..5da81b8 100644 --- a/content/blog/cloud-energy-usage-data.md +++ b/content/blog/cloud-energy-usage-data.md @@ -34,7 +34,7 @@ and not so quickly subject to change (Servers have 4-5 years lifetime). ## Getting energy data directly Very few vendors allow this and actually the only one we know that provides direct access to IPMI for tenants in it's -cloud offerings is [Blockheating](https://blockheating.com/). Here you get bare metal machines with also access to IPMI. +cloud offerings was [Blockheating](http://web.archive.org/web/20230624233405/https://blockheating.com/). Here you get bare metal machines with also access to IPMI. If you do not know what IPMI is and how to read the data, check out our [Metrics Provider for IPMI](https://docs.green-coding.io/docs/measuring/metric-providers/psu-energy-ac-ipmi-machine/) in the [Green Metrics Tool]({{< relref path="products/green-metrics-tool" >}}). diff --git a/content/blog/containers-on-macos-for-gmt.md b/content/blog/containers-on-macos-for-gmt.md index 7900594..f52e017 100644 --- a/content/blog/containers-on-macos-for-gmt.md +++ b/content/blog/containers-on-macos-for-gmt.md @@ -22,9 +22,9 @@ This is a quite high level overview and discussion and in no way complete. Diagram of linux containers architecture {{< /rawhtml >}} -First we need to discuss how docker works in it’s native environment, Linux. Docker uses features in the Linux kernel called [namespaces](https://en.wikipedia.org/wiki/Linux_namespaces), [cgroups](https://en.wikipedia.org/wiki/Cgroups) and many more. This technology enables processes to only see certain parts of resources previously allocated to them. Like this every docker process can be isolated from the rest of the operating system and only manage what has been allocated. The vita point is that the docker container basically runs in an isolated part of the Linux kernel. +First we need to discuss how docker works in it’s native environment, Linux. Docker uses features in the Linux kernel called [namespaces](https://en.wikipedia.org/wiki/Linux_namespaces), [cgroups](https://en.wikipedia.org/wiki/Cgroups) and many more. This technology enables processes to only see certain parts of resources previously allocated to them. Like this every docker process can be isolated from the rest of the operating system and only manage what has been allocated. The vita point is that the docker container basically runs in an isolated part of the Linux kernel. -This is in contrast to “classical” virtualisation. Which builds a totally new operating system, incuding kernel etv, on top of the so called host system. +This is in contrast to “classical” virtualisation. Which builds a totally new operating system, incuding kernel etv, on top of the so called host system. A super simplified view on how the Docker container is run @@ -35,14 +35,14 @@ This is in contrast to “classical” virtualisation. Which builds a totally ne Diagram of macOS containers architecture {{< /rawhtml >}} -Because docker can not rely on namespaces on other systems like Mac it needs to do a little trick. +Because docker can not rely on namespaces on other systems like Mac it needs to do a little trick. Just to mention it: Windows is a special case and will be discussed in another article once we port the Green Metrics Tool to windows (at some stage). To run docker containers on Mac docker uses a virtual machine that runs Linux. So it adds a totally new operating system via virtualisation. -You can see this in the Docker desktop app which gives you the option to either use the new [virtualisation framework](https://developer.apple.com/documentation/virtualization) from apple or [qemu](https://www.qemu.org/). If you don’t know about apples virtualisation framework is is worth a read as it lets you spin up VMs with ease. +You can see this in the Docker desktop app which gives you the option to either use the new [virtualisation framework](https://developer.apple.com/documentation/virtualization) from apple or [qemu](https://www.qemu.org/). If you don’t know about apples virtualisation framework is is worth a read as it lets you spin up VMs with ease. Looking at your processes running on your host you can’t see any containers as they are encapsulated in the VM. The only thing you see is the VM as you can see from this line from `ps` @@ -50,17 +50,17 @@ Looking at your processes running on your host you can’t see any containers as 501 15207 15188 0 9:06PM ?? 0:25.61 /Applications/Docker.app/Contents/MacOS/qemu-system-aarch64 -accel hvf -cpu host -machine virt,highmem=off -m 8092 -smp 4 -kernel /Applications/Docker.app/Contents/Resources/linuxkit/kernel -append page_poison=1 vsyscall=emulate panic=1 nospec_store_bypass_disable noibrs noibpb no_stf_barrier mitigations=off linuxkit.unified_cgroup_hierarchy=1 vpnkit.connect=tcp+bootstrap+client://192.168.65.2:52436/6bd3c43205d2e38a101ff4a22191af5bb947845a420aaad097305ef937474f33 vpnkit.disable=osxfs-data console=ttyAMA0 -initrd /Applications/Docker.app/Contents/Resources/linuxkit/initrd.img -serial pipe:/var/folders/dz/z2d2thkj76j2fxm_932w8qn80000gn/T/qemu-console2891520672/fifo -drive if=none,file=/Users/didi/Library/Containers/com.docker.docker/Data/vms/0/data/Docker.raw,format=raw,id=hd0 -device virtio-blk-pci,drive=hd0,serial=dummyserial -netdev socket,id=net1,fd=3 -device virtio-net-device,netdev=net1,mac=02:50:00:00:00:01 -vga none -nographic -monitor none ``` -An interesting point is that docker is using the Arm emulation binary [qemu-system-aarch64](https://www.qemu.org/docs/master/system/target-arm.html) as all containers I am running are build for arm. This might become a separate article in the future but for now the main points are that you can build containers for different architectures and docker will always try to use the one for the arch it is running on. It is however possible to run containers that are build for different architectures as we are running everything in a VM and qemu can pretty much emulate [anything](https://www.qemu.org/docs/master/system/targets.html). +An interesting point is that docker is using the Arm emulation binary [qemu-system-aarch64](https://www.qemu.org/docs/master/system/target-arm.html) as all containers I am running are build for arm. This might become a separate article in the future but for now the main points are that you can build containers for different architectures and docker will always try to use the one for the arch it is running on. It is however possible to run containers that are build for different architectures as we are running everything in a VM and qemu can pretty much emulate [anything](https://www.qemu.org/docs/master/system/targets.html). ## So what does this mean for you as a user: -Docker on Mac will always be slower as running docker on Linux. Through adding the extra virtualisation layer it creates quite an overhead when running anything in docker. But the docker tool does quite a good job at hiding all the nifty details. +Docker on Mac will always be slower as running docker on Linux. Through adding the extra virtualisation layer it creates quite an overhead when running anything in docker. But the docker tool does quite a good job at hiding all the nifty details. ## And for running the Green Metrics Tool on Mac: -The Green Metrics Tool (GMT) relies on Docker to isolate the single components that are needed to +The Green Metrics Tool (GMT) relies on Docker to isolate the single components that are needed to -a) run the support infrastructure for measuring like DB and our Web frontend +a) run the support infrastructure for measuring like DB and our Web frontend b) run the actual app in containers so we can measure the impact each piece of the app has @@ -70,5 +70,5 @@ As on Mac everything is isolated in a VM that isn’t easy to connect to from th - Shows how to replace the linuxkit vm with something you built yourself: [https://www.codeluge.com/post/setting-up-docker-on-macos-m1-arm64-to-use-debian-10.4-docker-engine/](https://www.codeluge.com/post/setting-up-docker-on-macos-m1-arm64-to-use-debian-10.4-docker-engine/) - [https://zarinfam.medium.com/what-are-the-advantages-of-the-new-virtualization-framework-in-macos-big-sur-7685c3aca0f7](https://zarinfam.medium.com/what-are-the-advantages-of-the-new-virtualization-framework-in-macos-big-sur-7685c3aca0f7) -- [https://www.ginkgobioworks.com/2022/07/19/using-docker-on-apple-silicon/](https://www.ginkgobioworks.com/2022/07/19/using-docker-on-apple-silicon/) +- [https://www.ginkgobioworks.com/2022/07/19/using-docker-on-apple-silicon/](http://web.archive.org/web/20240813031315/https://www.ginkgobioworks.com/2022/07/19/using-docker-on-apple-silicon/) - [https://earthly.dev/blog/using-apple-silicon-m1-as-a-cloud-engineer-two-months-in/](https://earthly.dev/blog/using-apple-silicon-m1-as-a-cloud-engineer-two-months-in/) diff --git a/content/blog/cpu-utilization-mac.md b/content/blog/cpu-utilization-mac.md index fe18178..453629d 100644 --- a/content/blog/cpu-utilization-mac.md +++ b/content/blog/cpu-utilization-mac.md @@ -66,10 +66,10 @@ This works and gives you realistic values. You can check this quite quickly with If you reduce the time to under 500 ms, that the script should wait till it loops, you start getting `0` values as `host_statistics` returns the same data. It is not exactly 500 ms and it varies on machines but around that time it starts returning the same values. Apple is notoriously bad at documenting their software but it looks like the kernel just updates the data every *n* ticks. Which makes sense from a performance perspective. Normally you wouldn't need a higher resolution. We looked how other tools implement getting cpu data and even `psutil` [[1]](https://pypi.org/project/psutil/) has the same problem. You can see the details in the -[bug report](https://github.com/giampaolo/psutil/issues/2368) that we filed. +[bug report](https://github.com/giampaolo/psutil/issues/2368) that we filed. Doing more research there is actually some caching int the [`host.c`](https://gitea.com/matteyeux/darwin-xnu/src/branch/master/osfmk/kern/host.c#L342) file that caches the results but I didn't do a deep dive why the statistics are not updated. -While the implications are minor we didn't want to ship code that would not perform with a high resolution on MacOS. After some searching around we found that [htop](https://github.com/htop-dev/htop) uses the `host_processor_info`[[2]](https://developer.apple.com/documentation/kernel/1502854-host_processor_info) kernel call which internally uses the `processor_info`[[3]](https://opensource.apple.com/source/xnu/xnu-792/osfmk/mach/processor_info.h.auto.html) call which also gives you the cpu load statistics on a per processor basis. And this gives a far higher resolution. So we can rewrite the code to look like: +While the implications are minor we didn't want to ship code that would not perform with a high resolution on MacOS. After some searching around we found that [htop](https://github.com/htop-dev/htop) uses the `host_processor_info`[[2]](https://developer.apple.com/documentation/kernel/1502854-host_processor_info) kernel call which internally uses the `processor_info` call which also gives you the cpu load statistics on a per processor basis. And this gives a far higher resolution. So we can rewrite the code to look like: ```C void loop_utilization(unsigned int msleep_time) { diff --git a/content/blog/dena-study-green-coding.md b/content/blog/dena-study-green-coding.md index 12e5b21..cb52391 100644 --- a/content/blog/dena-study-green-coding.md +++ b/content/blog/dena-study-green-coding.md @@ -19,6 +19,6 @@ Als Experten für nachhaltige Softwareentwicklung sehen wir die immense Bedeutun Nachhaltigkeit ist kein isoliertes Thema, sondern erfordert gemeinsame Anstrengungen und Kooperationen. Deshalb freuen wir uns auf die Zusammenarbeit mit Deutsche Energie-Agentur GmbH (dena) . ✨ -Außerdem vielen Dank an unser Konsortium [Hochschule für Technik und Wirtschaft Berlin](https://www.htw-berlin.de/), [SYNGENIO AG](https://syngenio.com/), [bluehands GmbH & Co.mmunication KG](https://www.bluehands.de/), [envite consulting](https://www.envite.de/), [DVC - Digital Venture Consultants](https://www.dvc.ventures/), [oktobit](https://oktobit.de/) und [metafinanz Informationssysteme GmbH](https://metafinanz.de/) 🍀 +Außerdem vielen Dank an unser Konsortium [Hochschule für Technik und Wirtschaft Berlin](https://www.htw-berlin.de/), [SYNGENIO AG](https://syngenio.com/), [bluehands GmbH & Co.mmunication KG](https://www.bluehands.de/), [envite consulting](https://www.envite.de/), [DVC - Digital Venture Consultants](https://dvc.ventures/), [oktobit](https://oktobit.de/) und [metafinanz Informationssysteme GmbH](https://metafinanz.de/) 🍀 diff --git a/content/blog/eco-server-energy-estimation.md b/content/blog/eco-server-energy-estimation.md index ddf891c..f265560 100644 --- a/content/blog/eco-server-energy-estimation.md +++ b/content/blog/eco-server-energy-estimation.md @@ -34,7 +34,7 @@ While knowing your energy usage is already a really nice thing to have we wanted This shows the view you will get for a machine. It shows you the sum of of all the energy the machine has used, the [CO2eq](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Carbon_dioxide_equivalent), the average [Carbon Intensity](https://en.wikipedia.org/wiki/Emission_intensity) and how many records we have used to generate these values. You can also see a list aggregated by day. We are planning on giving you detailed analytics as we already do for the Power Hog or Eco-CI. This is a really useful tool for any company that wants to record their impact. -See an example whow the data looks on our [Green Metrics Tool Dashboard Machine](https://metrics.green-coding.io/carbondb-details.html?machine_uuid=09015ff3-8a00-47f0-906a-9360b1808d38), which is also included in Carbon DB. +See an example whow the data looks on our [Green Metrics Tool Dashboard Machine](https://metrics.green-coding.io/carbondb.html), which is also included in Carbon DB. You can find the CarbonDB uploader on [our Github repo](https://github.com/green-coding-solutions/eco-server-energy-estimation/tree/main/carbondb_upload) diff --git a/content/blog/energy-measurement-progress.md b/content/blog/energy-measurement-progress.md index f9bd87d..7327b64 100644 --- a/content/blog/energy-measurement-progress.md +++ b/content/blog/energy-measurement-progress.md @@ -16,14 +16,13 @@ Intel RAPL readings. {{< /rawhtml >}} You can see the full details of our demo runs with the static version of the -Green Web Foundation Page in our [Green Metrics Tool Repositories Overview](https://metrics.green-coding.io/runs.html&show=repositories) +Green Web Foundation Page in our [Green Metrics Tool Repositories Overview](https://metrics.green-coding.io/runs.html?show=repositories) All metrics providers are made in a UNIX-style philosphy, which means they provide very conscise and scoped functionality, but can be chained through redirecting their output. -For the moment we are working on the documentation. If you want to have an early -look be sure to check out the [development branch](https://github.com/green-coding-solutions/green-metrics-tool/tree/rapl-and-cgoup-reader) +For the moment we are working on the documentation. Also we have made progress on getting the prototype for the DC Measurements working and are now coding the Linux metrics providers to read directly from diff --git a/content/blog/estimating-cloud-energy-linear-model-part-1.md b/content/blog/estimating-cloud-energy-linear-model-part-1.md index 6ce42be..a2d1463 100644 --- a/content/blog/estimating-cloud-energy-linear-model-part-1.md +++ b/content/blog/estimating-cloud-energy-linear-model-part-1.md @@ -16,22 +16,22 @@ A big goal for us here is at how to measure the energy/carbon use of cloud servi AWS Carbon dashboard {{< /rawhtml >}} -There already exists proprietary energy consumption dashboards for [Microsoft Azure](https://appsource.microsoft.com/en-us/product/power-bi/coi-sustainability.emissions_impact_dashboard), [Google](https://cloud.google.com/carbon-footprint), and [Amazon](https://aws.amazon.com/blogs/aws/new-customer-carbon-footprint-tool/) products. These generally use billing, hardware, and market data, alongside data center efficiency information, to provide emissions data. While they seem robust and a fantastic step in the right direction - there are some shortcomings with these tools. A big one is the lack of transparency - these numbers don't have much way to be verified.\ +There already exists proprietary energy consumption dashboards for [Microsoft Azure](http://web.archive.org/web/20260116012331/https://marketplace.microsoft.com/en-us/product/power-bi/coi-sustainability.emissions_impact_dashboard), [Google](https://cloud.google.com/carbon-footprint), and [Amazon](https://aws.amazon.com/blogs/aws/new-customer-carbon-footprint-tool/) products. These generally use billing, hardware, and market data, alongside data center efficiency information, to provide emissions data. While they seem robust and a fantastic step in the right direction - there are some shortcomings with these tools. A big one is the lack of transparency - these numbers don't have much way to be verified.\ Another problem is that the data is only available with 1-3 months latency, which is not really suitable for any actionable insights, given todays fast iteration cycles on software releases. Our goal therefore, is to try to make an open source generalized tool that can be used to estimate the usage on (ideally) any cloud setup. Starting this, we quickly come across a very fundamental issue: the information we can query from a cloud system itself can be very limited. Normally, we would measure the energy used via reading an energy register such as RAPL, but very often these registers are not exposed, or give null-value data because of security concerns. -Since we cannot measure energy used directly, the best course of action is to use a mathematical model to estimate the emissions based on the hardware setup. However, even this approach has some hurdles to overcome. In some cloud setups, the information you can find out about the system can be so limited that you don't even know the exact CPU model! This is especially problematic since we have found that in most server setups, the CPU is what draws 65-80% of the power in a system (for compute workloads without GPU aid). +Since we cannot measure energy used directly, the best course of action is to use a mathematical model to estimate the emissions based on the hardware setup. However, even this approach has some hurdles to overcome. In some cloud setups, the information you can find out about the system can be so limited that you don't even know the exact CPU model! This is especially problematic since we have found that in most server setups, the CPU is what draws 65-80% of the power in a system (for compute workloads without GPU aid). With these problems in mind - we wanted to create a model that can be used to give a carbon estimation based on what information you *do* have about your machine. -Other folks have already worked on models over at [Teads](https://medium.com/teads-engineering/building-an-aws-ec2-carbon-emissions-dataset-3f0fd76c98ac) and [Cloud Carbon Footprint (CCF)](https://www.cloudcarbonfootprint.org/docs/methodology). These models try to make either an energy or carbon estimation based on some key factors such as CPU model, utilization rate, region, RAM, and virtual host capacity. +Other folks have already worked on models over at [Teads](https://medium.com/teads-engineering/building-an-aws-ec2-carbon-emissions-dataset-3f0fd76c98ac) and [Cloud Carbon Footprint (CCF)](https://www.cloudcarbonfootprint.org/docs/methodology). These models try to make either an energy or carbon estimation based on some key factors such as CPU model, utilization rate, region, RAM, and virtual host capacity. -Both of these models leverage an open data set called [SPEC Power](https://www.spec.org/power_ssj2008/results/) and also work from [Etsy Cloud Jewels](https://www.etsy.com/codeascraft/cloud-jewels-estimating-kwh-in-the-cloud). -SPECPower is an industry-used and trusted set of benchmarks in which many different cloud setups were put under incrementally increasing loads and the power draw measured and recorded. -They look up your CPU in the dataset to find an estimated wattage. +Both of these models leverage an open data set called [SPEC Power](https://www.spec.org/power_ssj2008/results/) and also work from [Etsy Cloud Jewels](https://www.etsy.com/codeascraft/cloud-jewels-estimating-kwh-in-the-cloud). +SPECPower is an industry-used and trusted set of benchmarks in which many different cloud setups were put under incrementally increasing loads and the power draw measured and recorded. +They look up your CPU in the dataset to find an estimated wattage. In the case where the model is not in the data set, they use the Thermal Design Power ([TDP](https://en.wikipedia.org/wiki/Thermal_design_power)) to get a wattage estimation. An addition to these three models from Teads, CCF and Etsy Cloud Jewels is the work from [Greenpixie](https://greenpixie.com/blog/cloud-emission-calculation-methodology-AWS). diff --git a/content/products/webnrg.de.md b/content/products/webnrg.de.md new file mode 100644 index 0000000..404c150 --- /dev/null +++ b/content/products/webnrg.de.md @@ -0,0 +1,38 @@ +--- +title: "webNRG" +date: 2026-03-10 10:00:00 +publishDate: 2026-03-10 +draft: false +icon: "lightning" +desc: "webNRG misst den Energieverbrauch und die CO₂-Emissionen von Websites durch die Kombination von Netzwerkverkehrsanalyse und tatsächlichem Gerätestromverbrauch beim Rendering" +ordering: 9 +--- + +webNRG ist ein kostenloses Open-Source-Tool, das den Energieverbrauch und die CO₂-Emissionen von Websites misst. Im Gegensatz zu Tools, die sich nur auf grünes Hosting und Netzwerkdaten konzentrieren, legt webNRG den Fokus auf zwei häufig vernachlässigte Faktoren: **Energie des Netzwerkverkehrs** und **Rendering-Leistung auf der Nutzerseite**. + +Das Tool kombiniert beide Faktoren zu einem einzigen Score, der nach einem Nutri-Score-ähnlichen System von **A+ bis F** bewertet wird – damit sind die Ergebnisse sowohl für Entwickler als auch für Nicht-Techniker sofort verständlich. + +{{< rawhtml >}} +
+ Green-Coding.io webNRG Zusammenfassung +
green-coding.io webNRG⚡️ Zusammenfassung
+
+{{< /rawhtml >}} + +Eine der zentralen Erkenntnisse beim Entwickeln von webNRG: Eine Seite, die 35× mehr Daten überträgt als eine andere, kann dennoch denselben Rendering-Energieverbrauch haben. Netzwerkgröße und Stromverbrauch korrelieren nur schwach (0,49) – deshalb ist es wichtig, beide separat zu messen. + +### So funktioniert es + +- Reiche deine Website-URL für einen einmaligen Test ein oder registriere dich für ein **wöchentliches Monitoring** +- webNRG misst die Energie über ein 5-Sekunden-Fenster nach dem ersten Seitenaufruf +- Netzwerkverkehr und CPU-/Rendering-Energie werden unabhängig voneinander erfasst +- Ergebnisse werden gegen den Leerlauf-Stromverbrauch benchmarkt, mit 25%-Schritten pro Bewertungsstufe +- Emissionen werden auf Basis eines typischen Szenarios von 10.000 Nutzern/Monat berechnet + +### Links + +{{< button "lightning" "webNRG ausprobieren" "https://website-tester.green-coding.io/" >}} + +{{< button "book" "Release-Blogartikel" "/blog/webnrg-released" >}} + +
diff --git a/content/talks-and-events/cosmos-sustainall.md b/content/talks-and-events/cosmos-sustainall.md index 184eaa8..61ca3c8 100644 --- a/content/talks-and-events/cosmos-sustainall.md +++ b/content/talks-and-events/cosmos-sustainall.md @@ -22,7 +22,7 @@ Afterwards we will have time to discuss the problem further and to brainstorm so #### Cosmos community -https://www.cosmos.community +http://web.archive.org/web/20230330082739/https://www.cosmos.community/ Cosmos is a community for sharing things with all the people you trust - friends, friends of friends, neighbours, groups and everyone else. diff --git a/content/talks-and-events/sdia-event-0922.md b/content/talks-and-events/sdia-event-0922.md index 617e8c2..4d600bc 100644 --- a/content/talks-and-events/sdia-event-0922.md +++ b/content/talks-and-events/sdia-event-0922.md @@ -17,9 +17,9 @@ This is the appetizer text: > Arne will be presenting tooling for developers to measure energy-use & emissions. > At our community event in Berlin, Arne will be presenting the open-source tooling that his team is building for developers to understand the energy-use of their applications. It is aimed at measuring the energy use during the execution of standard-usage scenarios (as required by the German Blue Angel certification), but can be used to get a good understanding of any running application. -> It’s based on the RAPL interface and is under heavy development. Green Coding Solutions is also actively contributing to the Life Cycle Assessment methodology that is under development at the SDIA for digital resources (https://sdia.io/def-summary) +> It’s based on the RAPL interface and is under heavy development. Green Coding Solutions is also actively contributing to the Life Cycle Assessment methodology that is under development at the SDIA for digital resources (https://sdia.io/) #### Update: Recording -The SDIA provided a wonderful recording on their [SDIA Knowledge Base](https://knowledge.sdialliance.org/community-events/23-09-2022-berlin/recordings) +The SDIA provided a wonderful recording on their [SDIA Knowledge Base](https://knowledge.sdialliance.org/5abc10a0d0aa41cc99fd60548611bc1e) diff --git a/layouts/partials/references.html b/layouts/partials/references.html index b34517f..510d199 100644 --- a/layouts/partials/references.html +++ b/layouts/partials/references.html @@ -10,7 +10,7 @@

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SDIAlliance Logo Bits & Bäume Logo FOSDEM Logo - Arbeitsagentur IT Systemhaus Logo + Arbeitsagentur IT Systemhaus Logo Rohde & Schwarz HTW Berlin Logo OpenText Logo diff --git a/layouts/partials/site-footer.html b/layouts/partials/site-footer.html index 9388343..e6aecd4 100755 --- a/layouts/partials/site-footer.html +++ b/layouts/partials/site-footer.html @@ -3,7 +3,7 @@