Einen Meter hoch ist der LEGO® Technic™ Geländegängige Kranwagen in der Arbeitsposition. Zugleich hat die LEGO Gruppe bei dem größten und höchsten LEGO Technic™ Baufahrzeug viel Wert auf Details gelegt. Einen Freifallhammer gibt es obendrauf. Wir haben gebaut. (Lego)
Quelle: Golem
Analysten haben die erwarteten Abonnentenzahlen für Disney+ nach oben korrigiert. Bis Ende 2019 werde Disneys Videostreamingabo 20 Millionen Kunden haben. (Disney, Streaming)
Quelle: Golem
Gebaut für eine Kühlung mit flüssigem Helium ist Horse Ridge wohl der coolste Chip, den Intel zur Zeit in Entwicklung hat. Er soll einen Quantencomputer steuern, dessen Qubits mit ungewöhnlich hohen Temperaturen zurechtkommen. Von Frank Wunderlich-Pfeiffer (Quantencomputer, Intel)
Quelle: Golem
Attraktive Vergünstigungen für Abonnenten, spannende Deals für unsere IT-Profis, nerdiger Merchandise für Fans oder innovative Verkaufslösungen: Du willst maßgeschneiderte E-Commerce-Angebote für Golem.de entwickeln und umsetzen und dabei eigenverantwortlich und in unserem sympathischen Team arbeiten? Dann bewirb dich bei uns! (Golem.de, Photoshop)
Quelle: Golem
Mit Amazon CloudWatch Synthetics können Sie Anwendungsendpunkte einfacher überwachen. Mit dieser neuen Funktion kann CloudWatch jetzt Canary-Datenverkehr erfassen. So können Sie die Kundenerfahrung kontinuierlich verifizieren, auch wenn gerade kein Kundendatenverkehr für Ihre Anwendungen vorhanden ist, und etwaige Probleme erkennen, bevor sie Kunden betreffen. CloudWatch Synthetics unterstützt die Überwachung von REST-APIs, URLs und Website-Inhalten und prüft auf nicht autorisierte Änderungen durch Phishing, Code Injection und Cross-Site-Scripting. CloudWatch Synthetics führt rund um die Uhr minütliche Tests für Ihre Endpunkte durch und benachrichtigt Sie, wenn die Anwendungsendpunkte sich nicht erwartungsgemäß verhalten. Mit den anpassbaren Tests können Sie z. B. auf Verfügbarkeit, Latenz, Transaktionen, fehlerhafte Links, schrittweise Aufgabenfertigstellung, Seitenladefehler, Ladelatenzen für UI-Ressourcen, komplexe Assistenten-Abläufe und Checkout-Abläufe in Ihren Anwendungen prüfen. Außerdem können Sie mit CloudWatch Synthetics problematische Anwendungsendpunkte isolieren und sie zugrunde liegenden Infrastrukturproblemen zuordnen, um so die mittlere Problembehebungszeit zu reduzieren.
Quelle: aws.amazon.com
At the recent Microsoft Ignite 2019 conference, we introduced two new and related perspectives on the future and roadmap of edge computing.
Before getting further into the details of Network Edge Compute (NEC) and Multi-access Edge Compute (MEC), let’s take a look at the key scenarios which are emerging in line with 5G network deployments. For a decade, we have been working with customers to move their workloads from their on-premises locations to Azure to take advantage of the massive economies of scale of the public cloud. We get this scale with the ongoing build-out of new Azure regions and the constant increase of capacity in our existing regions, reducing the overall costs of running data centers.
For most workloads, running in the cloud is the best choice. Our ability to innovate and run Azure as efficiently as possible allows customers to focus on their business instead of managing physical hardware and associated space, power, cooling, and physical security. Now, with the advent of 5G mobile technology promising larger bandwidth and better reliability, we see significant requirements for low latency offerings to enable scenarios such as smart-buildings, factories, and agriculture. The “smart” prefix highlights that there is a compute-intensive workload, typically running machine learning or artificial intelligence-type logic, requiring compute resources to execute in near real-time. Ultimately the latency, or the time from when data is generated to the time it is analyzed, and a meaningful result is available, becomes critical for these smart-scenarios. Latency has become the new currency, and to reduce latency we need to move the required computing resources closer to the sensors, data origin or users.
Multi-access Edge Compute: The intersection of compute and networking
Internet of Things (IoT) creates incredible opportunities, but it also presents real challenges. Local connectivity in the enterprise has historically been limited to Ethernet and Wi-Fi. Over the past two decades, Wi-Fi has become the de-facto standard for wireless networks, not due to it necessarily being the best solution, but rather its entrenchment in the consumer ecosystem and lack of alternatives. Our customers from around the world tell us that deploying Wi-Fi to service their IoT devices requires compromises on coverage, bandwidth, security, manageability, reliability, and interoperability/roaming. For example, autonomous robots require better bandwidth, coverage, and reliability to operate safely within a factory. Airports generally have decent Wi-Fi coverage inside the terminals, but on the tarmac, coverage often drops significantly, making it insufficient and less suitable to power the smart airport.
Next-gen private cellular connectivity greatly improves bandwidth, coverage, reliability, and manageability. Through the combination of local compute resources and private mobile connectivity (private LTE), we can enable many new scenarios. For instance, in the smart factory example used earlier customers are now able to run their robotic control logic, highly available and independent of connectivity to the public cloud. MEC helps ensure that operations and any associated critical first-stage data processing remain up and production can continue uninterrupted.
With its promise and advantage of near-infinite compute and storage, the cloud is ideal for large data-intensive and computational tasks, such as machine learning jobs for predictive maintenance analytics. At this year’s Ignite conference, we shared our thoughts and experience, along with a technology preview of MEC with Azure. The technology preview brings private mobile network capabilities to Azure Stack Edge; an on-premises compute platform managed from Azure. In practical terms, the MEC allows locally controlling the robots; even if the factory suffers a network outage.
From an edge computing perspective, we have containers, running across Azure Stack Edge and Azure. A key aspect is that the same programming paradigm can be used for Azure and the edge-based MEC platform. Code can be developed and tested in the cloud, then seamlessly deployed at the edge. Developers can take advantage of the vast array of DevOps tools and solutions available in Azure and apply them to the new exciting edge scenarios. The MEC technology preview focuses on the simplified experience of cross-premises deployment and operations of managed compute and Virtual Network Functions with integration to existing Azure services.
Network Edge Compute
Whereas Multi-access Edge Compute (MEC) is intended to be deployed at the customer’s premises, Network Edge Compute (NEC) is the network carrier equivalent, placing the edge computing platform within their network. Last week we announced the initial deployment of our NEC platform in AT&T’s Dallas facility. Instead of needing to access applications and games running in the public cloud, software providers can bring their solutions physically closer to their end-users. At AT&T’s Business Summit we gave an augmented reality demonstration, working with Taqtile, and showed how to perform maintenance on an aircraft landing gear.
The HoloLens user sees the real landing gear along with the virtual manual along with specific parts of the landing gear virtually highlighted. The mixing of real-world and virtual objects displayed via HoloLens is what is often referred to as augmented reality (AR) or mixed reality (MR).
Edge Computing Scenarios
We have been showcasing multiple MEC and NEC use-cases over these past few weeks. For more details please refer to our Microsoft Ignite MEC and 5G session.
Mixed Reality (MR)
Mixed reality use cases such as remote assistance can revolutionize several industrial automation scenarios. Lower latencies and higher bandwidth coupled with local compute, enables new remote rendering scenarios to reduce battery consumption in handsets and MR devices.
Retail e-fulfillment
Attabotics provides a robotic warehousing and fulfillment system for the retail and supply chain industries. Attabotics employs robots (Attabots) for storage and retrieval of goods from a grid of bins. A typical storage structure has about 100,000 bins and is serviced by between 60 and 80 Attabots. Azure Sphere powers the robots themselves. Communications using Wi-Fi or traditional 900 MHz spectrum does not meet the scale, performance and reliability requirements.
The Nexus robot control system, used for command and control of the warehousing system, is built natively on Azure and uses Azure IoT Central for telemetry. With a Private LTE (CBRS) radio from our partners Sierra Wireless and Ruckus Wireless and packet core partner Metaswitch, we enabled the Attabots to communicate over a private LTE network. The reduced latency improved reliability and made the warehousing solution more efficient. The entire warehousing solution, including the private LTE network used for a warehouse, run on a single Azure Stack Edge.
Gaming
Multi-player online gaming is one of the canonical scenarios for low-latency edge computing. Game Cloud Studios has developed a game based on Azure Play Fab, called Tap and Field. The game backend and controls run on Azure, while the game server instances reside and run on the NEC platform. Having lower latencies results in better gaming experiences for players who are nearby in venues such as e-sport events, arcades, arenas, and similar venues.
Public Safety
The proliferation of drone use is disrupting many industries, from security and privacy to the delivery of goods. Air Traffic Control operations are on the cusp of one of the most significant disruptive events in the field, going from monitoring only dozens of aircrafts today to thousands tomorrow. This necessitates a sophisticated near real-time tracking system. Vorpal VigilAir has built a solution where drone and operator tracking is done using a distributed sensor network powered by a real-time tracking application running on the NEC.
Data-driven digital agriculture solutions
Azure FarmBeats is an Azure solution that enables aggregation of agriculture datasets across providers, and generation of actionable insights by building artificial intelligence (AI) or machine learning (ML) models by fusing the datasets. Gathering datasets from sensors distributed across the farm requires a reliable private network, and generating insights requires a robust edge computing platform that is capable of being operated in a disconnected mode in remote locations where connectivity to the cloud is often sparse. Our solution, based on the Azure Stack Edge along with a managed private LTE network, offers a reliable and scalable connectivity fabric along with the right compute resources close to the farm.
MEC, NEC, and Azure: Bringing compute everywhere
MEC enables a low-latency connected Azure platform in your location, NEC provides a similar platform in a network carrier’s central office, and Azure provides a vast array of cloud services and controls.
At Microsoft, we fundamentally believe in providing options for all customers. Because it is impractical to deploy Azure datacenters in every major metropolitan city throughout the world, our new edge computing platforms provide a solution for specific low-latency application requirements that cannot be satisfied in the cloud. Software developers can use the same programming and deployment models for containerized applications using MEC where private mobile connectivity is required, deploying to NEC where apps are optimally located outside the customer’s premises, or directly in Azure. Many applications will look to take advantage of combined compute resources across the edge and public cloud.
We are building a new extended platform and continue to work with the growing ecosystem of mobile connectivity and edge computing partners. We are excited to enable a new wave of innovation unleashed by the convergence of 5G, private mobile connectivity, IoT and containerized software environments, powered by new and distributed programming models. The next phase of computing has begun.
Quelle: Azure
Der Telekomanbieter 1&1 hat nach Ansicht des Bundesdatenschutzbeauftragten seine Kundendaten nicht ausreichend geschützt. Das Unternehmen kritisiert nun die Bußgeldregelung der deutschen Datenschutzbehörden. (DSGVO, Datenschutz)
Quelle: Golem
Exploring public datasets is an important aspect of modern data analytics, and all this gathered data can help us understand our world. At Google Cloud, we maintain a collection of public datasets, and we’re pleased to collaborate with the Lamont-Doherty Earth Observatory (LDEO) of Columbia University and the Pangeo Project to host the latest climate simulation data in the cloud. The World Climate Research Programme (WCRP) recently began releasing the Coupled Model Intercomparison Project Phase 6 (CMIP6) data archive, aggregating the climate models created across approximately 30 working groups and 1,000 researchers investigating the urgent environmental problem of climate change. The CMIP6 climate model datasets include rich details on many aspects of the climate system, including historical and future simulations. The data are now accessible in Cloud Storage and will be in BigQuery soon. Along with making CMIP6 available on Google Cloud, the Pangeo Project develops software and infrastructure to make it easier to analyze and visualize climate data using cloud computing.On Google Cloud, this dataset will be continuously updated and available to researchers around the globe to use for their own projects—without the constraints of downloading terabytes or even petabytes of data. The entire archive may eventually contain 20 PB of data, of which about 100 TB of data are currently available in the cloud. You can request data from Pangeo’s CMIP6 Google Cloud Collection in this form.“It’s a very live data set. It’s going to be updated over the next year as the data come online and as people’s needs arise,” says Ryan Abernathey, associate professor of Earth and environmental sciences at Columbia University and LDEO. He emphasizes the practical impact of this project. “What people actually care about most is not the global mean temperature because no one lives in the ‘global mean world.’ People care about the local impacts of drought or extreme rainfall, which can cause severe hardship for society. With these high-resolution simulations of rare events, we get much better information for planning in response to expected changes in the climate.”What you’ll find in the CMIP6 dataThe models in CMIP6’s data range from high-resolution simulations based on historical data from 1850 onward to hypothetical scenarios that manipulate key variables. For example, Abernathey asks, “What if carbon dioxide (CO2) were to instantaneously quadruple its concentration overnight? That’s a very useful experiment, not because it helps us make a detailed projection about the future, but because it helps us probe our physical understanding of how the climate system responds to CO2.” Each of the CMIP6 models includes dozens of variables, ensemble members, and scenarios, leading to large, unwieldy datasets. But Pangeo, an ensemble of open-source Python tools for big data analysis, makes it easier to perform large-scale computations on CMIP6 and other similar large datasets.To help researchers work with the multidimensional datasets of climate research, Abernathey and his colleagues at LDEO and the National Center for Atmospheric Research (NCAR) drew on funding from the National Science Foundation (NSF) and computing support from Google Cloud to develop Pangeo, which is an open-source platform aimed at accelerating geoscience data analysis. Pangeo can be run on nearly any high-performance computing system, including Google Kubernetes Engine (GKE), which supports easy deployment with autoscaling (both up and down) and integration with other Google Cloud tools such as Cloud Storage and BigQuery. The Pangeo community shares expertise, such as use cases for different domain-specific applications, and contributes to the development of open-source tools, like a cloud-optimized data storage format called Zarr.”The CMIP project has grown since its early days, and now is seeing tremendous growth beyond the U.S. and E.U. into the developing world,” says V. Balaji, a computational climate scientist on leave from Princeton University. Currently at the Institut Pierre-Simon Laplace in Paris, Balaji has been involved with all aspects of CMIP, from defining the experiments and running the simulations to analyzing the output and designing the Earth System Grid Federation (ESGF), a network of services that underpin the global data infrastructure enabling this critical research enterprise. “For new entrants, and for academic researchers worldwide, Pangeo in the cloud represents an exciting new opportunity to broaden the user base of very large-scale climate data, without the need to acquire supercomputer-scale storage and analysis facilities,” says Balaji. “It bridges what I call the gap between ‘inspiration-driven’ and ‘industrial strength’ science, enabling a scientist to explore the data and design their own analysis, and immediately apply their findings at very large scale. The progress of Pangeo in the cloud will inform our own architectural choices in designing the future of the global climate data infrastructure.”With these high-resolution simulations of rare events, we get much better information for planning in response to expected changes in the climate.The Pangeo team at LDEO and NCAR recently hosted a hackathon to jumpstart the analysis of the CMIP6 data on Google Cloud for pressing scientific questions. One participant—Henri Drake, a Ph.D. candidate in MIT’s Program in Atmospheres, Oceans, and Climate—created a tutorial for analyzing simulations of global warming in state-of-the-art CMIP6 models, under the worst-case scenario of uncontrolled greenhouse gas emissions. These CMIP6 model projections “reflect millions of lines of model code and represent everything from forest transpiration in the Amazon rainforest and thunderstorms in the U.S. Midwest to the formation of meltwater ponds on Arctic sea ice,” says Drake. “We would need a huge supercomputer to run the simulations from the model source code ourselves. Thankfully, the climate modeling community does this for us by making their output publicly available.”Drake used these tutorials as a teaching assistant for the Climate Change course at MIT to demonstrate the ease of cloud computing for data-intensive climate science research, and also the value of open-source tools like the Pangeo software stack on Google Cloud. “The CMIP6 dataset was already technically publicly available, it just was not very accessible,” says Drake. “The cloud-based data and computation, when combined with the Pangeo software stack, enabled me to make calculations in just a few hours that could have taken weeks using more conventional methods. Using the Pangeo binder, it was easy to make these calculations available to the rest of the world.”The CMIP6 data join many other weather and climate-related datasets available through Google’s Public Dataset program at no charge. By making data more accessible and usable with BigQuery and Cloud Storage, we support academic research by accelerating discoveries and promoting innovative solutions to complex problems. For Abernathey, the benefits of cloud computing are a particularly good match for the needs of scientific research: “With Google Cloud, you’ve essentially got a supercomputer just sitting right there, so you can directly process the data at a very high speed.”Get started with your own project by requesting data here.
Quelle: Google Cloud Platform
Last month, we started sharing the DevOps journey at Microsoft through the stories of several teams at Microsoft and how they approach DevOps adoption. As the next story in this series, we want to share the transition one team made from a classic operations role to a Site Reliability Engineering (SRE) role: the story of the Xbox Reliability Engineering and Operations (xREO) team.
This transition was not easy and came out of necessity when Microsoft decided to bring Xbox games to gamers wherever they are through cloud game streaming (project xCloud). In order to deliver cutting-edge technology with top-notch customer experience, the team had to redefine the way it worked—improving collaboration with the development team, investing in automation, and get involved in the early stages of the application lifecycle. In this blog, we’ll review some of the key learnings the team collected along the way. To explore the full story of the team, see the journey of the xREO team.
Consistent gameplay requirements and the need to collaborate
A consistent experience is crucial to a successful game streaming session. To ensure gamers experience a game streamed from the cloud, it has to feel like it is running on a nearby console. This means creating a globally distributed cloud solution that runs on many data centers, close to end users. Azure’s global infrastructure makes this possible, but operating a system running on top of so many Azure regions is a serious challenge.
The Xbox developers who have started architecting and building this technology understood that they could not just build this system and “throw it over the wall” to operations. Both teams had to come together and collaborate through the entire application lifecycle so the system can be designed from the start with considerations on how it will be operated in a production environment.
Architecting a cloud solution with operations in mind
In many large organizations, it is common to see development and operation teams working in silos. Developers don’t always consider operation when planning and building a system, while operations teams are not empowered to touch code even though they deploy it and operate it in production. With an SRE approach, system reliability is baked into the entire application lifecycle and the team that operates the system in production is a valued contributor in the planning phase. In a new approach, involving the xREO team in the design phase enabled a collaborative environment, making joint technology choices and architecting a system that could operate with the requirements needed to scale.
Leveraging containers to clearly define ownership
One of the first technological decisions the development and xREO teams made together was to implement a microservices architecture utilizing container technologies. This allowed the development teams to containerize .NET Core microservices they would own and remove the dependency from the cloud infrastructure that was running the containers and was to be owned by the xREO team.
Another technological decision both teams made early on, was to use Kubernetes as the underlying container orchestration platform. This allowed the xREO team to leverage Azure Kubernetes Service (AKS), a managed Kubernetes cloud platform that simplifies the deployment of Kubernetes clusters, removing a lot of the operational complexity the team would have to face running multiple clusters across several Azure regions. These joint choices made ownership clear—the developers are responsible for everything inside the containers and the xREO team is responsible for the AKS clusters and other Azure services make the cloud infrastructure hosting these containers. Each team owns the deployment, monitoring and operation of its respective piece in production.
This kind of approach creates clear accountability and allows for easier incident management in production, something that can be very challenging in a monolithic architecture where infrastructure and application logic have code dependencies and are hard to untangle when things go sideways.
Scaling through infrastructure automation
Another best practice the xREO team invested in was infrastructure automation. Deploying multiple cloud services manually on each Azure region was not scalable and would take too much time. Using a practice known as “infrastructure as code” (IaC) the team used Azure Resource Manager templates to create declarative definitions of cloud environments that allow deployments to multiple Azure regions with minimal effort.
With infrastructure managed as code, it can also be deployed using continuous integration and continuous delivery (CI/CD) to bring further automation to the process of deploying new Azure resources to existing data centers, updating infrastructure definitions or bringing online new Azure regions when needed. Both IaC and CI/CD, allowed the team to remain lean, avoid repetitive mundane work and remove most of the risk of human error that comes with manual steps. Instead of spending time on manual work and checklists, the team can focus on further improving the platform and its resilience.
Site Reliability Engineering in action
The journey of the xREO team started with a need to bring the best customer experience to gamers. This is a great example that shows how teams who want to delight customers with new experiences through cutting edge innovation must evolve the way they design, build, and operate software. Shifting their approach to operations and collaborating more closely with the development teams was the true transformation the xREO team has undergone.
With this new mindset in place, the team is now well positioned to continue building more resilience and further scale the system and by so, deliver the promise of cloud game streaming to every gamer.
Resources
The full story of the xREO team
Additional stories: The DevOps journey at Microsoft
Microsoft Game Stack
Quelle: Azure
Es ist voll in der Erdumlaufbahn: Immer mehr Satelliten kreisen im Orbit, und auch immer mehr Weltraumschrott. Die einzige Möglichkeit, des Problems Herr zu werden, ist laut Esa, ihn zu beseitigen. Für 2025 ist die erste europäische Aufräummission geplant. (Weltraumschrott, Raumfahrt)
Quelle: Golem
