Quelle: <a href="Rumors Of Kiwi Farms’ Death Are Greatly Exaggerated“>BuzzFeed
Today, we are excited to announce that the new Google Cloud region in Israel is open. We’ll be celebrating the launch at an event in Tel Aviv on November 9 — register to join us. Israel is known as the startup nation, and has long been a hub of technology innovation for startups and Google alike. We’re excited to extend that innovation-first approach to other industries, accelerating digital transformation to help create new jobs and digital experiences that better serve users in Israel. According to recent research commissioned by Google, AlphaBeta Economics (part of Access Partnership) estimates that by 2030, the Google Cloud region in Tel Aviv will contribute a cumulative USD 7.6 billion to Israel’s GDP, and support the creation of 21,200 jobs in that year alone1. The Google Cloud region in Tel Aviv (me-west1), joins our network of cloud regions around the world, delivering high-performance, low-latency services to customers of all sizes and across industries. Now that the Israel cloud region is part of the Google Cloud network, it will help local organizations connect with users and customers around the globe, and help fuel innovation and digital transformation across every sector of the economy. Last year, Google Cloud was selected by the Israeli government to provide cloud services to government ministries. This partnership can enable the government, and private companies operating in regulated industries, to simplify the way in which users are served, create a uniform approach to digital security, and support compliance and residency requirements. Over a number of years we’ve grown our local Googler presence in both Tel Aviv and Haifa to support the growing number of customers, and bring a culture of innovation to every sector of the economy. From technology, retail, and media and entertainment, to financial services and the public sector, leading organizations come to Google Cloud as their trusted innovation partner. “With Google Cloud we are changing the way millions of people read and write, by serving our own Large Language Models on top of the most advanced GPU platform that offers unparalleled performance, availability and elasticity.” – Ori Goshen, CEO, AI21 Labs“PayBox is supervised by the Bank of Israel and is completely hosted in the cloud. Google Cloud provides us with the tools needed to meet regulatory compliance and security obligations as well as the flexibility and agility to serve the millions of customers that rely on our app every day.” – Dima Levitin, CIO, PayboxIsrael has long been a hub of technology innovation, and we’re excited to support customers Like AI21, Paybox, and others with a cloud that helps them:Better understand and use data: Google Cloud helps customers make better decisions with a unified data platform. We help customers reduce complexity and combine unstructured and structured data — wherever it resides — to quickly and easily produce valuable insights. Establish an open foundation for growth: When customers move to Google Cloud, they get a flexible, secure, and open platform that can evolve with their organization. Our commitment to multicloud, hybrid cloud, and open source offers organizations the freedom of choice, helping to allow their developers to build faster.Create a collaborative environment: In today’s hybrid work environment, Google Cloud provides the tools needed to help transform how people connect, create, and collaborate. Protect systems and users: As every company rethinks its security posture, we help customers protect their data using the same infrastructure and security services that Google uses for its own operations. Build a cleaner, more sustainable future: Google has been carbon neutral for our operations since 2007, and we are working to operate entirely on carbon-free energy by 2030. Today, when customers run on Google Cloud — the cleanest cloud in the industry — the energy that powers their workloads is matched with 100% renewable energy. We’re excited to see what you build with the new Google Cloud region in Israel. Learn more about ourglobal cloud infrastructure, including new and upcoming regions. And don’t miss the Israel launch event.Related ArticleGoogle Cloud announces new region to support growing customer base in IsraelThe new Google Cloud region in Israel will bring low-latency for users in the area, as well as a full complement of Google Cloud services.Read Article
Quelle: Google Cloud Platform
Das Medion Major X10 soll sich für Spiele eignen und hat dafür HDMI sowie viele USB- und Ethernet-Ports. Im Innern arbeitet Alder Lake-H. (Aldi-PC, Notebook)
Quelle: Golem
Cadillac will sein Showcar Celestiq in einer Kleinserie bauen und setzt auf 3D-Druck und eine betuchte Kundschaft. (General Motors, Technologie)
Quelle: Golem
Autos sollen zunehmend Methanol tanken, das ohne Beeinträchtigung des Klimas produziert wird. Phase eins hat jedoch noch einen Schönheitsfehler. (Elektromobilität, Technologie)
Quelle: Golem
Vor über zehn Jahren hat ein Bastler aus Cherrys MX Black und MX Clear den MX Ergo Clear gebaut – nun bietet Cherry den Tastatur-Switch selbst an. (Cherry, Eingabegerät)
Quelle: Golem
Stellantis Europachef will den Preisunterschied zwischen Verbrennern und E-Autos mit niedrigeren Steuern auf letztere reduzieren. (Elektroauto, Technologie)
Quelle: Golem
As a developer, DevOps engineer or a database administrator, you have to typically deal with database lock issues. Often, rows locked by queries cause lags and can slow down applications resulting in poor user experience. Today, we are excited to announce the launch of lock insights and transaction insights for Cloud Spanner that provide a set of new visualization tools for developers and database administrators to quickly diagnose lock contention issues on Spanner. If you observe application slowness, a common issue could be lock contentions, which happen when multiple transactions are trying to modify the same row. Debugging lock contentions is not easy as it requires identifying the row ranges and columns on which transactions are contending for locks. This process can be tedious and time consuming without a visual interface. Today, we are solving this problem for customers.Lock insights and transaction insights provide pre-built dashboards that make it easy to detect row ranges with the highest lock wait time, find transactions reading or writing on these row ranges, and identify the transactions with highest latencies causing these lock conflicts.Earlier this year, we launched query insights for debugging query performance issues. Together with lock insights and transaction insights, these capabilities provide developers easy-to-use observability tools to troubleshoot issues and optimize the performance of their Spanner databases.Lock insights and transaction insights are available at no additional cost.”Lock insights will be very helpful to debug lock contention which typically takes hours.” said Dominick Anggara, MSc., Staff Software Engineer at Kohl’s. “It allows the user to see the big picture, and make it easy to make correlations, and then narrow down to specific transactions. That’s what makes it powerful. Really looking forward to using this in production”.Why do lock issues happen?Most databases take locks on data to prohibit other transactions from concurrently changing the data to preserve data integrity. When you access data with the intent to change it, a lock prohibits other transactions from accessing the data while it is being modified. But when the data is locked, it can negatively impact application performance as other tasks wait to access the data. Cloud Spanner, Google Cloud’s fully managed horizontally scalable relational database service, offers the strictest concurrency-control guarantees, so that you can focus on the logic of the transaction without worrying about data integrity. To give you this peace of mind, and to ensure consistency of multiple concurrent transactions, Spanner uses a combination of shared locks and exclusive locks at the table cell level (granularity of row-and-column) and not at the whole row level. You can learn more about different types of Lock modes for Spanner in our documentation.Follow a visual journey with pre-built dashboardsWith lock insights and transaction insights, developers can smoothly move from detection of latency issues to diagnosis of lock contentions, and ultimately identification of transactions that are contending for locks. Once the transactions causing the lock conflicts are identified, you can then try to identify issues in each transaction that are contributing to the problem.You could do this by following a simple journey where you can quickly confirm if the application slowness is due to lock contentions, correlate row ranges and columns which have the highest lock wait time with the transactions taking locks on these row ranges, identify the transactions with the highest latencies, and analyze these transactions which are contending on locks. Let’s walk through an example scenario. Diagnose application slownessThis journey will start by setting up an alert on Google Cloud Monitoring for latency (api/request_latencies) going above a certain threshold. The alert could be configured in a way that if this threshold is crossed, you will be notified with an email alert, with a link to the “Monitoring” dashboard.Once you receive this alert, you would click on the link in the email, and navigate to the “Monitoring” dashboard. If you observe a spike in read/write latency, no observable spike in CPU utilization, and a dip in Throughput and/or Operations per second, a possible root cause could be lock contentions. A combination of these patterns in these metrics could be a strong signal that the system is locking due to the transactions contending on the same cells, even though the workload remains the same. Below, you can observe a spike between 5:45 PM and 6:00 PM. This could be due to new application code deployment which might have introduced a new access pattern.The next step is to confirm that this application slowness is indeed due to the lock contentions. This is where lock insights comes in. You can get to this tool by clicking on “Lock insights” in the left navigation of the Spanner Instance view in your Cloud Console. Here, the first graph that you see will be for Total lock wait time. If you observe a corresponding spike on this graph in the corresponding time window, this would confirm that the application’s slowness is due to lock contentions.Co-relating row ranges, columns and transactionsNow you can select the database which is seeing the spike in total lock wait time, and drill down to see the row ranges with the highest lock wait times. When a user clicks on a row-range which has the highest lock wait times, a right panel will open up. This will show sample lock requests for that row range which includes the columns which were read from or written to, the type of lock which was acquired on this row-column combination (database cell), and links to view the transactions which were contending for these locks. This helps co-relate row ranges, columns and transactions makes this journey seamless to switch between lock insights and transaction insights as explained in the next section.In the above screenshot, we can see that at 5:53 PM, the first row range in the table (order_item(82,12)) is showing the highest lock wait times. You can investigate further by looking at the transactions which were acting on the sample lock columns. Identifying transactions with highest write latencies causing locksWhen you click on “View transactions” on the lock insights page, you will navigate to the transaction insights page with the topN transactions table (by latency) filtered on the Sample lock Columns from the previous page (lock insights), so you will view the topN transactions in the context of the locks (and row ranges) which were identified earlier in the journey.In this example we can see that the first transaction reading from and writing to columns item_inventory._exists, item_inventory.count has the highest latencies and could be one of the transactions causing lock contentions. We can also see that the second transaction in the table is also trying to read from the same column, and could be waiting on locks since the average latency is high. We should drill deep and investigate both these transactions.Analyzing transactions to fix lock contentionsOnce you have identified the transactions causing the locks, you can drill down into these transaction shapes to analyze the root cause of lock contentions.You can do this by clicking on the Fingerprint ID for the specific transactions from the topN table, and navigating to the Transaction Details page where you will be able to see a list of metrics (Latency, CPU Utilization, Execution count, Rows Scanned / Rows Returned) over a time series for that specific transaction.In this example, we notice that when we drill down into the second transaction, this transaction is only attempting to read and not write. By definition, the topN transactions table (on the previous page) only shows read-write transactions which take locks. We can also see that the abort count / total attempt count ratio (28/34) is very high, which means that most of the attempts are getting aborted.Fixing the issueTo fix the problem in this scenario, you can convert this transaction from a read-write transaction to a read-only transaction, which would prevent it from taking locks on the cell, and thereby reducing lock contention and reducing write latencies.By following this simple visual journey, you can easily detect, diagnose and fix lock contention issues on Spanner.When looking at potential issues in your application, or even when designing your application, consider these best practices to reduce the number of lock conflicts in your database.Get started with lock insights and transaction insights todayTo learn more about lock insights and transaction insights, review the documentation here, and watch the explainer video here.Lock insights and transaction insights are enabled by default. In the Spanner console, you can click on “Lock insights” and “Transaction insights” in the left navigation and start visualizing lock issues and transaction performance metrics! New to Spanner? Create a 90-day Spanner free trial instance. Try Spanner for free.Related ArticleIntroducing Query Insights for Cloud Spanner: troubleshoot performance issues with pre-built dashboardsSpanner’s ‘Query insights’ – a new tool that makes it easy to debug query performance issues.Read Article
Quelle: Google Cloud Platform
In-memory databases are a critical component that deliver the lowest possible latency for your users who might be adding items to online shopping carts, getting personalized content recommendations, or checking their latest account balances. Memorystore makes it easy for developers building these types of applications on Google Cloud to leverage the speed and powerful capabilities of the most loved in-memory store: Redis. Memorystore for Redis offers zonal high availability with a 99.9% SLA for its Standard Tier instances. In some cases, users are looking to expand their Memorystore footprint to multiple regions to support disaster recovery scenarios for regional failure or to provide the lowest possible latency for a multi-region application deployment. We’ll show you how to deploy such an architecture today with the help of the Envoy proxy Redis filter, which we introduced in our previous blog, Scaling to new heights with Cloud Memorystore and Envoy. Envoy makes creating such an architecture both simple and extensible due to its numerous supported configurations. Let’s get started with a hands-on tutorial which demonstrates how you can build a similar solution.Architecture OverviewLet’s start by discussing an architecture of Google Cloud native services combined with open-source software which enables a multi-region Memorystore architecture. To do this, we’ll be using Envoy to mirror traffic to two Memorystore instances which we’ll create in separate regions. For simplicity, we’ll be using Memtier Benchmark, a popular CLI for Redis load generation, as a sample application to simulate end user traffic. In practice, feel free to use your existing application or write your own.Because of Envoy’s traffic mirroring configuration, the application does not need to be aware of the various backend instances that exist and only needs to connect to the proxy. You’ll find a sample architecture below and we’ll briefly detail each of the major components.Before we start, you’ll also want to ensure compatibility with your application by reviewing the list of the Redis commands which Envoy currently supports. Prerequisites To follow along with this walkthrough, you’ll need a Google Cloud project with permissions to do the following: Deploy Cloud Memorystore for Redis instances (required permissions)Deploy GCE instances with SSH access (required permissions)Cloud Monitoring viewer access (required permissions) Access to Cloud Shell or another gCloud authenticated environment Deploying the multi-region Memorystore backend You’ll start by deploying a backend Memorystore for Redis cache which will serve all of your application traffic. You’ll deploy two instances in separate regions so that we can protect our deployment against regional outages. We’ve chosen regions US-West1 and US-Central1 though you are free to choose whichever regions work best for your use case. From an authenticated cloud shell environment, this can be done as follows:$ gcloud redis instances create memorystore-primary –size=1 –region=us-west1 –tier=STANDARD –async$ gcloud redis instances create memorystore-standby –size=1 –region=us-central1 –tier=STANDARD –asyncIf you do not already have the Memorystore for Redis API enabled in your project, the command will ask you to enable the API before proceeding. While your Memorystore instances deploy, which typically takes a few minutes, you can move onto the next steps. Creating the Client and Proxy VMsNext, you’ll need a VM where you can deploy a Redis client and the Envoy proxy. To protect against regional failures, we’ll create a GCE instance per region. On each instance, you will deploy the two applications, Envoy and Memtier Benchmark, as containers. This type of deployment is referred to as a “sidecar architecture” which is a common Envoy deployment model. Deploying in this fashion nearly eliminates any added network latency as there is no additional physical network hop that takes place. You can start by creating the primary region VM: $ gcloud compute instances create client-primary –zone=us-west1-a –machine-type=e2-highcpu-8 –image-family cos-stable –image-project cos-cloud Next, create the secondary region VM: $ gcloud compute instances create client-standby –zone=us-central1-a –machine-type=e2-highcpu-8 –image-family cos-stable –image-project cos-cloud Configure and Deploy the Envoy Proxy Before deploying the proxy, you need to gather the necessary information to properly configure the Memorystore endpoints. To do this, you need the host IP addresses for the Memorystore instances you have already created. You can gather these like: gcloud redis instances describe memorystore-primary –region us-west1 –format=json | jq -r “.host”gcloud redis instances describe memorystore-standby –region us-central1 –format=json | jq -r “.host”Copy these IP addresses somewhere easily accessible as you’ll use them shortly in your Envoy configuration. You can also find these addresses in the Memorystore console page under the “Primary Endpoint” columns. Next, you’ll need to connect to each of your newly created VM instances, so that you can deploy the Envoy Proxy. You can do this easily via SSH in the Google Cloud Console. More details can be found here.After you have successfully connected to the instance, you’ll create the Envoy configuration. Start by creating a new file named envoy.yaml on the instance with your text editor of choice. Use the following .yaml file, entering the IP addresses of the primary and secondary instances you created:code_block[StructValue([(u’code’, u’static_resources:rn listeners:rn – name: primary_redis_listenerrn address:rn socket_address:rn address: 0.0.0.0rn port_value: 1999rn filter_chains:rn – filters:rn – name: envoy.filters.network.redis_proxyrn typed_config:rn “@type”: type.googleapis.com/envoy.extensions.filters.network.redis_proxy.v3.RedisProxyrn stat_prefix: primary_egress_redisrn settings:rn op_timeout: 5srn enable_hashtagging: truern prefix_routes:rn catch_all_route:rn cluster: primary_redis_instancern request_mirror_policy:rn cluster: secondary_redis_instancern exclude_read_commands: truern – name: secondary_redis_listenerrn address:rn socket_address:rn address: 0.0.0.0rn port_value: 2000rn filter_chains:rn – filters:rn – name: envoy.filters.network.redis_proxyrn typed_config:rn “@type”: type.googleapis.com/envoy.extensions.filters.network.redis_proxy.v3.RedisProxyrn stat_prefix: secondary_egress_redisrn settings:rn op_timeout: 5srn enable_hashtagging: truern prefix_routes:rn catch_all_route:rn cluster: secondary_redis_instancern clusters:rn – name: primary_redis_instancern connect_timeout: 3srn type: STRICT_DNSrn lb_policy: RING_HASHrn dns_lookup_family: V4_ONLYrn load_assignment:rn cluster_name: primary_redis_instancern endpoints:rn – lb_endpoints:rn – endpoint:rn address:rn socket_address:rn address: <primary_region_memorystore_ip>rn port_value: 6379 rn – name: secondary_redis_instancern connect_timeout: 3srn type: STRICT_DNS rn lb_policy: RING_HASHrn load_assignment:rn cluster_name: secondary_redis_instancern endpoints:rn – lb_endpoints:rn – endpoint:rn address:rn socket_address:rn address: <secondary_region_memorystore_ip>rn port_value: 6379rn rnadmin:rn address:rn socket_address:rn address: 0.0.0.0rn port_value: 8001′), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e4ff3ba9090>)])]The various configuration interfaces are explained below:Admin: This interface is optional, it allows you to view configuration and statistics etc. It also allows you to query and modify different aspects of the envoy proxy. Static_resources: This contains items that are configured during startup of the envoy proxy. Inside this we have defined clusters and listeners interfaces. Clusters: This interface allows you to define clusters which we are defining per region. Inside cluster configuration you define all the available hosts and how to distribute load across those hosts. We have defined two clusters, one in the primary region and another in the secondary region. Each cluster can have a different set of hosts and different load balancer policies. Since there is only one host in each cluster, you can use any load balancer policy as all the requests will be forwarded to that single host.Listeners: This interface allows you to expose the port on which the client would connect, and define behavior of traffic received. In this case we have defined two listeners, one for each regional Memorystore instance.Once you’ve added your Memorystore instance IP addresses, save the file locally to your container OS VM where it can be easily referenced. Make sure to repeat these steps for your secondary instance as well. Now, you’ll use Docker to pull the official Envoy proxy image and run it with your own configuration. On primary region client machine, run this command: $ docker run –rm -d -p 8001:8001 -p 6379:1999 -v $(pwd)/envoy.yaml:/envoy.yaml envoyproxy/envoy:v1.21.0 -c /envoy.yaml On the standby region client machine, run this command: $ docker run –rm -d -p 8001:8001 -p 6379:2000 -v $(pwd)/envoy.yaml:/envoy.yaml envoyproxy/envoy:v1.21.0 -c /envoy.yamlFor our standby region, we have changed the binding port to port 2000. This is to ensure that traffic from our standby clients are routed to the standby instance in the event of a regional failure which makes our primary instance unavailable.In this example, we are deploying envoy proxy manually, but, in practice, you will implement a CI/CD pipeline which will deploy the envoy proxy and bind ports depending on your region based configuration. Now that Envoy is deployed, you can test it by visiting the admin interface from the container VM: $ curl -v localhost:8001/statsIf successful, you should see a print out of the various Envoy admin stats in your terminal. Without any traffic yet, these will not be particularly useful, but they allow you to ensure that your container is running and available on the network. If this command does not succeed, we recommend checking that the Envoy container is running. Common issues include syntax errors within your envoy.yaml and can be found by running your Envoy container interactively and reading the terminal output. Deploy and Run Memtier Benchmark After reconnecting to the primary client instance in us-west1 via SSH, you will now deploy the Memtier Benchmark utility which you’ll use to generate artificial Redis traffic. Since you are using Memtier Benchmark, you do not need to provide your own dataset. The utility will populate the cache for you using a series of set commands.code_block[StructValue([(u’code’, u’$ for i in {1..5}; do docker run –network=”host” –rm -d redislabs/memtier_benchmark:1.3.0 -s 127.0.0.1 -p 6379 u2014threads 2 u2013clients 10 –test-time=300 –key-maximum=100000 –ratio=1:1 –key-prefix=”memtier-$RANDOM-“; done’), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e4ff3ba4910>)])]Validate the cache contents Now that we’ve generated some data from our primary region’s client, let’s ensure that it has been written to both of our regional Memorystore instances. We can do this by using cloud monitoring metrics-explorer. Next, you’ll configure the chart via “MQL” which can be selected at the top of the explorer pane. For ease, we’ve created a query which you can simply paste into your console to populate your graph:code_block[StructValue([(u’code’, u”fetch redis_instancern| metric ‘redis.googleapis.com/keyspace/keys’rn| filterrn (resource.instance_id =~ ‘.*memorystore.*’) && (metric.role == ‘primary’)rn| group_by 1m, [value_keys_mean: mean(value.keys)]rn| every 1m”), (u’language’, u”), (u’caption’, <wagtail.wagtailcore.rich_text.RichText object at 0x3e4ff25a6210>)])]If you have created your Memorystore instances with a different naming convention or have other Memorystore instances within the same project, you may need to modify the resource.instance_id filter. Once you’re finished, ensure that your chart is viewing the appropriate time range, and you should see something like:In this graph, you should see two like lines which show the same number of keys in both Memorystore instances. If you want to view metrics for a single instance, you can do this by using the default monitoring graphs which are available from the Memorystore console after selecting a specific instance. Simulate Regional Failure Regional failure is a rare event. We will simulate this by deleting our primary Memorystore instance and primary client VM. Let’s start by deleting our primary Memorystore instance like: $ gcloud redis instances delete memorystore-primary –region=us-west1And then our client VM like: $ gcloud compute instances delete client-primaryNext, we’ll need to generate traffic from our secondary region client VM which we are using as our standby application. For the sake of this example, we’ll manually perform a failover and generate traffic to save time. In practice, you’ll want to devise a failover strategy to automatically divert traffic to the standby region when the primary region becomes unavailable. Typically, this is done with the help of services like Cloud Load Balancer. Once more, ssh into the secondary region client VM from the console and run the Memtier benchmark application as mentioned in the previous section. You can validate that reads and writes are properly routing to our standby instance by viewing the console’s monitoring graphs once more. Once the original primary Memorystore instance is available again, it will become the new standby instance based on our Envoy configuration. It will also be out of sync with our new primary instance as it has missed writes during its unavailability. We do not intend to cover a detailed solution in this post, but we find that most users opt to rely on TTL which they have set on their keys to determine when their caches will eventually be in sync. Clean UpIf you have followed along, you’ll want to spend a few minutes cleaning up resources to avoid accruing unwanted charges. You’ll need to delete the following: Any deployed Memorystore instances Any deployed GCE instancesMemorystore instances can be deleted like: $ gcloud redis instances delete <instance-name> –region=<region>The GCE container OS instance can be deleted like: $ gcloud compute instances delete <instance-name>If you created additional instances, you can simply chain them in a single command separated by spaces. ConclusionWhile Cloud Memorystore Standard tier provides high availability, some use cases require an even higher availability guarantee. Envoy and its Redis filter make creating a multi-regional deployment simple and extensible. The outline provided above is a great place to get started. These instructions can easily be extended to support automated region failover or even dual region active-active deployments. As always, you can learn more about Cloud Memorystore through our documentation or request desired features via our public issue tracker.Related ArticleScaling to new heights with Cloud Memorystore and EnvoyLearn how to scale your Google Cloud Memorystore for Redis database for high volume use cases in just a few minutes with the help of Envo…Read Article
Quelle: Google Cloud Platform
We’re at a pivotal moment in systems design. Demand for computing is growing at insatiable rates. At the same time, the slowing of Moore’s law means that improvements to CPU performance, power consumption, memory and storage cost efficiencies have all plateaued. These headwinds are further exacerbated by new challenges in reliability, and security. At Google, we’ve responded to these challenges and opportunities with system design innovations across the stack: from new custom-silicon accelerators (e.g., TPU, VCU, and IPU), new hardware and data center infrastructure, all the way to new distributed systems and cloud solutions. But this is only the beginning. There are many more opportunities for advancements, including closely-coupled accelerators for core data center functions to minimize the so-called “data center tax.” As server and data center infrastructure diverges from decades-old traditional designs to be more modular, heterogeneous, disaggregated, and software-defined, distributed systems are also entering a new epoch — one defined by optimizations for the “killer microsecond” and novel programming models optimized for low-latency and accelerators. At Google, we believe that these new opportunities and challenges are best addressed together, across the industry. Today, at the Open Compute Project (OCP) Global Summit, we are demonstrating our support of open hardware ecosystems, presenting at more than 40 talks, and announcing several key contributions:Server design: We will share Google’s vision for a “multi-brained” server of the future, transforming traditional server designs to more modular disaggregated distributed systems across host computing, accelerators, memory expansion trays, infrastructure processing units, etc. We are sharing the work we are doing with all our OCP partners on the varied innovations needed to make this a reality — from modular hardware with DC-MHS, standardized management with OpenBMC and RedFish, standardized root of trust, and standardized interfaces including CXL, NVMe and beyond.Trusted computing: The root of trust is an essential part of future systems. Google has a tradition of making contributions for transparent and best in-class security, including our OpenTitan discrete security solutions on consumer devices. We are looking ahead to future innovations in confidential computing and varied use-cases that require chip-level attestation at the level of a package or System on a Chip (SoC). Together with other industry leaders, AMD, Microsoft, and NVIDIA, we are contributing Caliptra, a re-usable IP block for root of trust measurement, to OCP. In the coming months we will roll out initial code for the community to collectively harden together.Reliable computing: To address the challenges of reliability at scale, we’ve formed a new server-component resilience workstream at OCP, along with AMD, ARM, Intel, Meta, Microsoft, and NVIDIA. Through this workstream, we’ll develop consistent metrics about silent data errors and corruptions for the broader industry to track. We’ll also contribute test execution frameworks and suites, and provide access to test environments with faulty devices. This will enable the broader community — across industry and academia — to take a systems-approach to addressing silicon faults and silent data errors. Sustainability: Finally, we’re announcing our support for a new initiative within OCP to support environmental sustainability as a key tenet across the ecosystem. Google has been a leader in environmental sustainability for many years. We have been carbon neutral since 2007, powered by 100% renewable energy since 2017, and have an ambitious goal to achieve net-zero emissions across all of our operations and value chain by 2030. In turn, as the cleanest cloud in the industry, we have helped customers track and reduce their carbon footprint and achieve significant energy savings. We’re excited to share these best practices with OCP and work with the broader community to standardize sustainability measurement and optimization in this important area. As the industry body focused on system integration (e.g., compute, memory, storage, management, power and cooling), the OCP Foundation is uniquely positioned to facilitate the industry-wide codesign we need. Google is active in OCP, serving in leadership roles, incubating new initiatives, and supporting numerous contributions.These announcements are the latest example of our history of fostering open and standards-based ecosystems. Open ecosystems enable a diverse product marketplace, with agility in time-to-market, and the opportunity to be strategic about innovation. Google’s open source leadership is multidimensional: driving industry standardization and adoption, strong and varied community contributions to grow the ecosystem, as well as broad policy and organizational leadership and sharing of best practices. The four initiatives we are announcing today, in combination with the Google-led talks at the OCP Summit, provide a small glimpse into the exciting new era of systems ahead. We look forward to working with the broader OCP community and other industry organizations to build a vibrant open hardware ecosystem to support even more innovation in this space. Please join us in this exciting journey.Related ArticleJupiter evolving: Reflecting on Google’s data center network transformationThanks to optical circuit switching (OCS) and wave division multiplexing (WDM) in the Jupiter data center network, Google enjoys a host o…Read Article
Quelle: Google Cloud Platform
