Die wirtschaftlichen Folgen der Coronavirus-Pandemie machen sich auch im Smartphone-Markt bemerkbar – nur Xiaomi und Vivo verbessern sich. (Coronavirus, Smartphone)
Quelle: Golem
thenewstack.io – Kubernetes Operators simplify the experience of automating complex applications in containers — for example, deploying Kubernetes-native stateful Cassandra clusters that can scale alongside your stat…
Quelle: news.kubernauts.io
As COVID-19 continues to affect our world, financial services organizations are working hard to ensure their services are available to all who need them. In many countries, financial services are “essential” and must remain available throughout COVID-19 for important reasons, including national economic security. Moreover, in the U.S. and many other countries, governments are leaning on financial services institutions to deliver additional citizen services, like disbursement of stimulus funds.At the same time, security is as important as ever, especially as we see increased targeting of organizations with COVID-related scams. There are many more people using mobile devices and remote access to perform critical functions, and it’s important that security teams maintain defenses and focus on protecting remote workforces.As the global financial services industry responds to COVID-19, many organizations will rely more readily on remote capabilities to meet rapidly changing government, consumer and security demands. Access to secure, reliable and flexible systems and applications will become increasingly essential for the foreseeable future.Below are a few ways Google Cloud is supporting our financial services industry customers to ensure business continuity and reliability.Helping financial services organizations disperse loans and other funds faster to people who need themThe U.S. Small Business Administration (SBA)’s Paycheck Protection Program (PPP) aims to help numerous businesses facing unprecedented challenges keep their workers employed during the COVID-19 pandemic. But lenders, servicers and processors are struggling to handle current intake volumes of PPP loan requests. To help lenders accelerate and automate processing loan applications, Google Cloud developed the PPP Lending AI Solution to help integrate Google’s AI-based document ingestion tools as part of lenders’ existing underwriting components and lending systems. The PPP Lending AI Solution has three components, each of which can be used individually or in combination with each other:The Loan Processing Portal is a web-based application that lets lending agents and/or loan applicants create, submit, and view the status of their PPP loan application. The Document AI PPP Parser API enables lenders to use AI to extract structured information from PPP loan documents submitted by applicants. This component is available at no cost through June 30, 2020. Loan Analytics enables lenders to quickly onboard historical loan data, assist with the de-identification and anonymization of sensitive information, store information securely, and perform data analytics on this historical loan data. Leveraging artificial intelligence, we’ve created an end-to-end solution that speeds up the time-to-decision on loans and helps inform lenders’ liquidity analysis—from the initial application submission to the underwriting process and SBA validation. The solution is also equipped with Google’s security capabilities, enabling lenders to meet policy requirements and protect critical assets. Providing immediate burst capacity for banks, trading organizations, and insurersProviding remote work capabilities and workstations for employees is increasingly necessary for many financial institutions. Extending secure and reliable access to applications and systems without incurring significant capital investments has become a key priority. Our Burst Capacity Solution provides additional compute and analytics capabilities that can handle some of the most compute-intensive workloads. Our solution can help financial institutions quickly glean more from data, virtualize productivity tools and scale up and out hundreds of cores and terabytes of memory. It leverages core AI components that can hear, see, and understand various forms of structured and unstructured data without requiring data science expertise. Just as importantly, it includes multiple layers of physical and logical protection, encrypts data at rest by default and has a dedicated team of Site Reliability Engineers (SRE) providing continuous monitoring 24x7x365.Our goal is to ensure your infrastructure can handle significant traffic spikes and support the most demanding workloads, securely, efficiently and at scale. Here are some ways we have provided burst capacity for financial services firms:Banks: As banks see increased demand in mobile and online banking applications, our Burst Capacity solution can help them predict credit and loan defaults and margin calls that are timely and accurate. Banks can also comply with liquidity stress testing and capital planning requirements, such as Comprehensive Capital Analysis and Review (CCAR).Institutional and wholesale trading organizations: The Burst Capacity solution lets institutional and wholesale trading organizations calculate and simulate increased risks—such as market, value, counterparty, credit, liquidity and redemption—in order to identify market opportunities and mitigate losses.Life and casualty insurers: The Burst Capacity solution can support an increased demand for online support, video brokerage and advisory services for policies and claims such as life insurance. It can also help insurers conduct more comprehensive actuarial modeling.Burst Capacity solution provisioning takes approximately one week and is available to scale up when necessary. The solution’s pay-for-used-compute-seconds structure adds a level of flexibility.Helping financial services organizations modernize their infrastructureAs financial services organizations adjust to new realities, modernizing IT structures will become more critical than ever. Cloud-based infrastructures offer more flexible computational capacity, and offloading certain workloads from the mainframe to new cloud architectures may support flexible traffic and access patterns, suggesting a new way of thinking about network design. Data and AI tools will be integral to the new model, too, as they can improve insights, risk management and cybersecurity. And finally, reliance on hosted data centers may diminish, suggesting more collaboration between firms and technology partners for business continuity. One way we’re helping financial institutions modernize their infrastructure is assisting with migrating mainframe workloads to the cloud as quickly as possible through mainframe app automation. While many mission-critical workloads run on mainframe architecture, moving to the cloud offers access to new technologies that foster faster innovation. Through Google Cloud’s acquisition of Cornerstone earlier this year, we’re now helping customers like Boa Vista by offering migration roadmap development, conversion flexibility and automated data migration. Cornerstone can help solve immediate mainframe modernization needs by offering automated migration tools to applications, without requiring Cobal and PL4 expertise. Although, firms will still need a more holistic mainframe modernization strategy in the post-COVID-19 world.Another way we’re facilitating infrastructure modernization is through our managed, cloud-native platform Anthos. This application platform lets enterprises modernize how they develop, secure and operate hybrid cloud environments. By providing an agnostic, Kubernetes-based environment, customers can build once, and run anywhere, across clouds and on-premises. It’s already leveraged by leading financial institutions including DenizBank and KeyBank.For “essential” industries such as financial services, having a reliable, resilient infrastructure has never been more important than now. Helping financial services firms make value connections in real timeOnline platforms are key in supporting remote workers. AI-based agents and video conferencing can be used to assist customers and deliver financial advice, in real time. AI and robotic process automation (RPA) can also bring efficiency to tasks such as loan modifications, mortgage refinancing, ratings actions and credit extensions, freeing valuable staff to focus more on complex tasks and ensuring timely customer support.Working with Google Cloud and partner NubeliU, Banco Santander in Argentina developed a solution in less than 24 hours to expedite low interest loans for companies suffering the economic effects of the COVID-19 crisis. Using Google Vision AI, they could automatically validate documents and forms in PDF format—helping to meet a surge in demand and deliver loans in record time to support small and medium-sized companies.Meeting face-to-face is an important way financial services organizations serve their customers, now made more challenging as a result of COVID-19. To help support remote interactions, Google Meet enables effortless video conferencing with enterprise-grade security and reliability built on Google’s secure and reliable global infrastructure. Firms can safely cultivate client relationships through virtual advisory services, such as financial planning, and engage in video brokerage for policies such as life insurance. Finally, as financial services firms are handling extraordinary spikes in customer inquiries over digital channels, we developed the Contact Center AI Rapid Response Virtual Agent program to help automate simple customer service interactions so call center agents can focus on more complex cases. The program provides contact center customers with immediate self-service to address general questions and concerns about COVID-19, letting employees focus on providing higher value-added, more personalized responses to customers who need it.Continuing to support financial services organizations in this uncertain time and beyondWe are committed to maintaining the health of the systems that power the financial services industry, and will do everything we can to empower our customers’ business continuity planning and resilience. We’ll continue to look for ways to leverage the latest technologies to improve and enhance the current situation.
Quelle: Google Cloud Platform
The Domain Name System, or DNS, is one of the most foundational services of the Internet, turning human-friendly domain names into IP addresses. Often handled by specialized network engineers within an organization, DNS can feel like a black box to people who don’t deal with it often. For one, DNS terminology can be confusing, and some terms have different meanings in different parts of the cloud network (e.g. peering). But understanding how DNS works is critical, especially in a cloud environment, where you need DNS to make your applications available to enterprise users.If you’re running on Google Cloud, chances are you use Cloud DNS, a scalable, reliable and managed authoritative DNS service running on the same infrastructure as Google. It has low latency, high availability and scalability and is a cost-effective way to make your applications and services available to your users.One of the more complex DNS setups that customers struggle with is building multiple projects and VPCs, all of which need to establish connectivity back to an on-prem DNS resource. Unless there’s outside connectivity to an on-prem network or another cloud, VPCs logically look like “islands,” or self-contained networks. As such, a logical assumption would be that each VPC would use its own forwarding zone and individually forward DNS queries back to on-prem for resolution. However, isolating your VPCs from one another leads to challenges, and the more VPCs you have, the harder it becomes. First, let’s unpack why this is challenging, and then show you how to solve for it.The trouble with handling DNS forwarding requests in multiple VPCsThe challenge is fundamentally a routing one. Google utilizes an egress proxy for all outbound DNS requests to the on-prem environment from an outbound forwarding zone. This highly available and scalable pool of proxies uses the same IP address block and does so for all VPCs. If you have multiple VPCs that forward DNS requests to the same on-premises network, it is not possible to create a route to send the response specifically to the originating VPC (because they are all using the same IP blocks for their proxies). The more VPCs using the pool of proxies you have, the greater the chances of sending things back to the wrong VPC. In the drawing below, two VPCs, A and B, are both set up with outbound forwarding zones to on-prem, and both the cloud routers A and B are advertising the DNS proxy range of 35.199.192.0/19. To the on-prem network, all traffic appears to originate from 35.199.192.0/19 and when a response is generated on-prem, the return traffic could end up in the wrong VPC network. In this scenario, the on-prem network has a 50/50 chance of guessing which VPC originated the request. And as more VPCs get introduced into the model, the chances of reaching the right source diminish rapidly.Outbound forwarding zones and DNS peering for connecting multiple VPCsIn order to address the challenge of connecting multiple VPCs to an on-prem network, you need to use a combination of outbound forwarding zones alongside DNS peering in a hub-and-spoke model. The hub VPC utilizes DNS forwarding to perform the hybrid connection to the on-prem network and the spoke VPCs uses DNS peering to connect to the hub VPC. In the drawing below, a single outbound forwarding zone is set up in VPC H. All other VPCs peer with VPC H. Any queries set to be resolved from on-prem will now go from the originating VPC(A, B, or C in this example) to VPC H. Once in VPC H, it will identify this as part of the outbound forwarding zone, and forward the request to on-prem through established network connectivity. In this case, the 35.199.192.0/19 range is only being advertised from VPC H’s cloud router, therefore when the query is being routed back to Google Cloud, there is only a single VPC network path for that route. VPC H then cascades the appropriate information back to the originating VPC (A, B, or C) and everything functions as expected.Keeping up with Cloud DNSManaging DNS might not be your day job, but understanding how it works can be critical when configuring enterprise cloud environments. In this post, we’ve shown you how to use some of Google’s DNS constructs to connect multiple zones to your on-premises DNS infrastructure, using a combination of zones, peering, and forwarding. You can learn more about Google Cloud’s networking portfolio, including our DNS services, online and reach us at gcp-networking@google.com.
Quelle: Google Cloud Platform
In April, we announced we were expanding our Google Cloud learning resources to support the growing number of people working and learning from home. Today, we are excited to announce that if you sign up by May 31, 20201, you can still enroll in Google Cloud training on both Pluralsight and Qwiklabs at no cost for 30 days—here’s where you can get started.If you’re new to Google Cloud, we recommend our five-hour introductory-level series of labs, Google Cloud Essentials. These labs will give you a tour of Google Cloud and help you familiarize yourself with basic cloud concepts such as virtual machines on Google Compute Engine, containerized applications with Kubernetes Engine, network load balancers, and HTTP load balancers. To get started, register here and select Qwiklabs.If you’re ready to dive even deeper into cloud, Pluralsight offers a full breadth of video-based Google Cloud learning paths, courses, and skills assessments. To help you pick your path, we’ve recommended below three in-demand skills you can start learning over the next 30 days at no cost on Pluralsight. To get started, register and select Pluralsight here to receive a special access link via email. Once you set up your Pluralsight account, you can search for any of the learning paths mentioned in this post from the Pluralsight catalog. Build your data analytics expertise According to McKinsey research, almost 60% of businesses find it harder to source talent for data and analytics positions than any other roles. The Data Analytics on Google Cloud learning path helps you build these much-needed skills, teaching you to explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. You’ll also dig deeper into data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This 13-hour learning path includes four courses which are a combination of hands-on labs and lectures. We recommend you have some prior experience with ANSI SQL before taking this learning path.Do some learning on machine learning The global machine learning market is expected to grow almost 44% from 2019 to 2025, making these skills relevant for any technical professional. The Machine Learning on Google Cloud learning path will allow you to experiment with end-to-end machine learning, starting from building a machine learning-focused strategy and progressing into model training, optimization, and production with hands-on labs using Google Cloud. This 17-hour learning path includes five courses with interactive hands-on labs and lectures. Anyone with knowledge of querying with SQL and programming in Python can take this learning path.Sharpen your Kubernetes skillsGoogle Kubernetes Engine (GKE) is a managed, production-ready environment for running containerized applications that’s trusted by businesses all over the world. The Architecting with Google Kubernetes Engine learning path will teach you how to implement solutions using GKE by building, scheduling, load balancing, and monitoring workloads. You’ll also learn to manage role-based access control and security, as well as provide persistent storage to these applications. This 10-hour learning path consists of four courses that have a mix of presentations, demos, and hands-on labs. To get the most from this training, we recommend you have experience with virtual machines, networks, storage in the cloud as well as experience with developing, deploying, and monitoring in the cloud. If you’d like to gain more experience before taking Architecting with Google Kubernetes Engine, you can take the three-hour Google Cloud Platform Fundamentals: Core Infrastructure course on Pluralsight. Ready to strengthen your cloud skills with Google Cloud training? Register here and claim your training offers by May 31 to get your free 30-days access on Qwiklabs and Pluralsight.1. Your 30-days access to Google Cloud training at no cost starts when you enroll for your courses. These offers are valid until May 31, 2020. After your 30-days, you will incur charges on Pluralsight; for Qwiklabs, you will need to purchase credits to continue taking labs.
Quelle: Google Cloud Platform
There are many ways to design distributed systems. One way involves growing systems organically—components are rewritten or redesigned as the system handles more requests. Another method starts with a proof of concept. Once the system adds value to the business, a second version is designed from the ground up. At Google, we use a method called non-abstract large system design (NALSD). NALSD describes an iterative process for designing, assessing, and evaluating distributed systems, such as Borg cluster management for distributed computing and the Google distributed file system. Designing systems using NALSD can be a bit daunting at first, so in this post, we introduce a nifty strategy to make things easier: flashcards. We describe how you can use flashcards to connect the most important numbers around constrained resources when designing distributed systems. These numbers include educated estimates concerning the CPU, memory, storage, and network latencies and throughputs.Let’s look at two examples illustrating the use of these numbers.For the first example, say you have a server designed to store images. We are most interested in the write throughput of the underlying storage layer. The underlying storage layer might be limited by the write speed of the disks it consists of. Knowing disk seek times and the write throughput is important so we can spot the bottleneck in the overall system.For the next example, say you have another server that may be responsible for serving low-latency metadata search queries. Here, potential bottlenecks might be memory consumption or CPU utilization. The memory consumption is from holding an index, and CPU utilization is from performing the actual search. To find out which one is the bottleneck, we have to consult latency numbers on CPU cache and main memory access. We are probably less concerned with network throughput, because we expect requests and responses to be small in size. However, as we scale the system up on the drawing board, the bottlenecks may change. So it’s best to always assign educated estimates to all components in a distributed system.NALSD helps identify potential bottlenecks as systems scale up. We address the bottlenecks early on—for example, by iterating on the design until we find an overall more scalable architecture.‘The numbers everyone should know’So what are the magical numbers we’ve alluded to? According to long-time Google engineer Jeff Dean, there are “numbers everyone should know.” These include numbers that describe common actions performed by the machines that servers and other components of a distributed system run on. (Numbers have changed since this video was recorded. In this post, we’re using the most recent figures.) Here are some examples:An L1 cache reference takes a nanosecond.A branch misprediction is roughly three times as expensive as an L1 cache reference, and takes three nanoseconds.Locking or unlocking a mutex (a resource-guarding structure used for synchronizing concurrency) costs about 17 nanoseconds, more than five times the cost of a branch misprediction.Referencing main memory is slightly more expensive, costing roughly 100 nanoseconds.Sending two kilobytes over a 10 Gb/s network takes 1.6 microseconds, or 1600 nanoseconds. Stuff gets expensive here!A round trip within the same data center takes only 500 microseconds, while a round trip from California to the Netherlands takes roughly 300 times as long (150 milliseconds).A disk seek takes about 10 milliseconds. That’s quite expensive compared to reading 1 MB sequentially from disk, which takes about 5 milliseconds.Memorizing these numbers may come naturally to some, but others, like us, may prefer flashcards to help remember the numbers that engineers use to design and maintain a system. Flashcards are a helpful companion for designing large systems. An added bonus of these flashcards is that they can be used as an entertaining, on-the-spot quiz for fellow site reliability engineers (SREs), or as a preparation tool for an NALSD interview with Google’s SRE team.If you’re interested in these flashcards, you can download your own set of flashcards for site reliability engineers. Follow these easy steps to turn them into handy flashcards: Print the document, preferably on thick paper.Fold each page once vertically, then glue the back sides together.Cut out the cards along the lines.Voilà! Now you have a nice set of NALSD flash cards. Happy quizzing!Learn more about how these numbers fit in with the overall process of NALSD:Distributed Log-Processing Design WorkshopNon-Abstract Large System Design (The Site Reliability Workbook, chapter 12)
Quelle: Google Cloud Platform
In the past few months, there has been a dramatic and rapid shift in the speed at which organizations of all sizes have enabled remote work amidst the global health crisis. Companies examining priorities and shifting resources with agility can help their employees stay connected from new locations and devices, allowing for business continuity essential to productivity.
We have seen thousands of organizations turn to Windows Virtual Desktop to help them quickly deploy remote desktops and apps on Azure for users all over the globe. The service and its new updates available today in preview will simplify getting started and enabling secure access to what users need each day.
Get started quickly with the Azure Portal with a new admin experience to accelerate end-to-end deployment, management, and optimization.
Gain enhanced security and compliance using reverse connect technology, Azure Firewall to limit internet egress traffic from your virtual machines to specific IP addresses in Azure, and several other new additions.
Enjoy an upgraded Microsoft Teams experience coming in the next month with audio/video redirection (AV redirect) to reduce latency in conversations running in a virtualized environment.
Learn more about today’s announcement in the Microsoft 365 blog from Julia White and Brad Anderson.
Get started with Windows Virtual Desktop and connect with technical experts in the Windows Virtual Desktop Tech Community.
Quelle: Azure
Smartphones mit Mediateks Dimensity 1000 benötigen dank AV1 weniger Daten und Energie. (AV1, Smartphone)
Quelle: Golem
Denn es sei “richtig, so zu handeln”, sagte die Icann zu ihrer Entscheidung. (Icann, DNS)
Quelle: Golem
Das Desktop-Replacement-Notebook wird von Schenker obendrein mit einer Geforce RTX 2080 Super ausgestattet. (Schenker, Notebook)
Quelle: Golem
