Tech News - Cloud & Networking

Zeit, the organization behind the cloud deployment software Now, yesterday launched Serverless Docker in beta. The concept was first discussed by the Zeit team at Zeit Day 2018 back in April, but it's now available to use and promises to radically speed up deployments for engineers. In a post published on the Zeit website yesterday, the team listed some of the key features of this new capability, including: An impressive 10x-20x improvement in cold boot performance (in practice this means cold boots can happen in less than a second A new slot configuration property that defines resource allocation in terms of CPU and Memory, allowing you to fit an application within the set of constraints that are most appropriate for it Support for HTTP/2.0 and WebSocket connections to deployments, which means you no longer need to rewrite applications as functions. The key point to remember with this release, according to Zeit, is that  "Serverless can be a very general computing model. One that does not require new protocols, new APIs and can support every programming language and framework without large rewrites." Read next: Modern Cloud Native architectures: Microservices, Containers, and Serverless – Part 1 What's so great about Serverless Docker? Clearly, speed is one of the most exciting things about serverless Docker. But there's more to it than that - it also offers a great developer experience. Johannes Schickling, co-founder and CEO of Prisma (a GraphQL data abstraction layer) said that, with Serverless Docker, Zeit "is making compute more accessible. Serverless Docker is exactly the abstraction I want for applications." https://twitter.com/schickling/status/1029372602178039810 Others on Twitter were also complimentary about Serverless Docker's developer experience - with one person comparing it favourably with AWS - "their developer experience just makes me SO MAD at AWS in comparison." https://twitter.com/simonw/status/1029452011236777985 Combining serverless and containers One of the reasons people are excited about Zeit's release is that it provides the next step in serverless. But it also brings containers into the picture too. Typically, much of the conversation around software infrastructure over the last year or so has viewed serverless and containers as two options to choose from rather than two things that can be used together. It's worth remembering that Zeit's product has largely been developed alongside its customers that use Now. "This beta contains the lessons and the experiences of a massively distributed and diverse user base, that has completed millions of deployments, over the past two years." Eager to demonstrate how Serverless Docker works for a wide range of use cases, Zeit has put together a long list of examples of Serverless Docker in action on GitHub. You can find them here. Read next A serverless online store on AWS could save you money. Build one. Serverless computing wars: AWS Lambdas vs Azure Functions

Since 2006, Amazon Web Services (AWS) has been helping millions of customers build and manage their IT workloads. From startups to large enterprises to public sector, organizations of all sizes use our cloud computing services to reach unprecedented levels of security, resiliency, and scalability. Every day, they’re able to experiment, innovate, and deploy to production in less time and at lower cost than ever before. Thus, business opportunities can be explored, seized, and turned into industrial-grade products and services. As Machine Learning (ML) became a growing priority for our customers, they asked us to build an ML service infused with the same agility and robustness. The result was Amazon SageMaker, a fully managed service launched at AWS re:Invent 2017 that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly. Today, Amazon SageMaker is helping tens of thousands of customers in all industry segments build, train and deploy high quality models in production: financial services (Euler Hermes, Intuit, Slice Labs, Nerdwallet, Root Insurance, Coinbase, NuData Security, Siemens Financial Services), healthcare (GE Healthcare, Cerner, Roche, Celgene, Zocdoc), news and media (Dow Jones, Thomson Reuters, ProQuest, SmartNews, Frame.io, Sportograf), sports (Formula 1, Bundesliga, Olympique de Marseille, NFL, Guiness Six Nations Rugby), retail (Zalando, Zappos, Fabulyst), automotive (Atlas Van Lines, Edmunds, Regit), dating (Tinder), hospitality (Hotels.com, iFood), industry and manufacturing (Veolia, Formosa Plastics), gaming (Voodoo), customer relationship management (Zendesk, Freshworks), energy (Kinect Energy Group, Advanced Microgrid Systems), real estate (Realtor.com), satellite imagery (Digital Globe), human resources (ADP), and many more. When we asked our customers why they decided to standardize their ML workloads on Amazon SageMaker, the most common answer was: “SageMaker removes the undifferentiated heavy lifting from each step of the ML process.” Zooming in, we identified five areas where SageMaker helps them most. #1 – Build Secure and Reliable ML Models, Faster As many ML models are used to serve real-time predictions to business applications and end users, making sure that they stay available and fast is of paramount importance. This is why Amazon SageMaker endpoints have built-in support for load balancing across multiple AWS Availability Zones, as well as built-in Auto Scaling to dynamically adjust the number of provisioned instances according to incoming traffic. For even more robustness and scalability, Amazon SageMaker relies on production-grade open source model servers such as TensorFlow Serving, the Multi-Model Server, and TorchServe. A collaboration between AWS and Facebook, TorchServe is available as part of the PyTorch project, and makes it easy to deploy trained models at scale without having to write custom code. In addition to resilient infrastructure and scalable model serving, you can also rely on Amazon SageMaker Model Monitor to catch prediction quality issues that could happen on your endpoints. By saving incoming requests as well as outgoing predictions, and by comparing them to a baseline built from a training set, you can quickly identify and fix problems like missing features or data drift. Says Aude Giard, Chief Digital Officer at Veolia Water Technologies: “In 8 short weeks, we worked with AWS to develop a prototype that anticipates when to clean or change water filtering membranes in our desalination plants. Using Amazon SageMaker, we built a ML model that learns from previous patterns and predicts the future evolution of fouling indicators. By standardizing our ML workloads on AWS, we were able to reduce costs and prevent downtime while improving the quality of the water produced. These results couldn’t have been realized without the technical experience, trust, and dedication of both teams to achieve one goal: an uninterrupted clean and safe water supply.” You can learn more in this video. #2 – Build ML Models Your Way When it comes to building models, Amazon SageMaker gives you plenty of options. You can visit AWS Marketplace, pick an algorithm or a model shared by one of our partners, and deploy it on SageMaker in just a few clicks. Alternatively, you can train a model using one of the built-in algorithms, or your own code written for a popular open source ML framework (TensorFlow, PyTorch, and Apache MXNet), or your own custom code packaged in a Docker container. You could also rely on Amazon SageMaker AutoPilot, a game-changing AutoML capability. Whether you have little or no ML experience, or you’re a seasoned practitioner who needs to explore hundreds of datasets, SageMaker AutoPilot takes care of everything for you with a single API call. It automatically analyzes your dataset, figures out the type of problem you’re trying to solve, builds several data processing and training pipelines, trains them, and optimizes them for maximum accuracy. In addition, the data processing and training source code is available in auto-generated notebooks that you can review, and run yourself for further experimentation. SageMaker Autopilot also now creates machine learning models up to 40% faster with up to 200% higher accuracy, even with small and imbalanced datasets. Another popular feature is Automatic Model Tuning. No more manual exploration, no more costly grid search jobs that run for days: using ML optimization, SageMaker quickly converges to high-performance models, saving you time and money, and letting you deploy the best model to production quicker. “NerdWallet relies on data science and ML to connect customers with personalized financial products“, says Ryan Kirkman, Senior Engineering Manager. “We chose to standardize our ML workloads on AWS because it allowed us to quickly modernize our data science engineering practices, removing roadblocks and speeding time-to-delivery. With Amazon SageMaker, our data scientists can spend more time on strategic pursuits and focus more energy where our competitive advantage is—our insights into the problems we’re solving for our users.” You can learn more in this case study. Says Tejas Bhandarkar, Senior Director of Product, Freshworks Platform: “We chose to standardize our ML workloads on AWS because we could easily build, train, and deploy machine learning models optimized for our customers’ use cases. Thanks to Amazon SageMaker, we have built more than 30,000 models for 11,000 customers while reducing training time for these models from 24 hours to under 33 minutes. With SageMaker Model Monitor, we can keep track of data drifts and retrain models to ensure accuracy. Powered by Amazon SageMaker, Freddy AI Skills is constantly-evolving with smart actions, deep-data insights, and intent-driven conversations.“ #3 – Reduce Costs Building and managing your own ML infrastructure can be costly, and Amazon SageMaker is a great alternative. In fact, we found out that the total cost of ownership (TCO) of Amazon SageMaker over a 3-year horizon is over 54% lower compared to other options, and developers can be up to 10 times more productive. This comes from the fact that Amazon SageMaker manages all the training and prediction infrastructure that ML typically requires, allowing teams to focus exclusively on studying and solving the ML problem at hand. Furthermore, Amazon SageMaker includes many features that help training jobs run as fast and as cost-effectively as possible: optimized versions of the most popular machine learning libraries, a wide range of CPU and GPU instances with up to 100GB networking, and of course Managed Spot Training which lets you save up to 90% on your training jobs. Last but not least, Amazon SageMaker Debugger automatically identifies complex issues developing in ML training jobs. Unproductive jobs are terminated early, and you can use model information captured during training to pinpoint the root cause. Amazon SageMaker also helps you slash your prediction costs. Thanks to Multi-Model Endpoints, you can deploy several models on a single prediction endpoint, avoiding the extra work and cost associated with running many low-traffic endpoints. For models that require some hardware acceleration without the need for a full-fledged GPU, Amazon Elastic Inference lets you save up to 90% on your prediction costs. At the other end of the spectrum, large-scale prediction workloads can rely on AWS Inferentia, a custom chip designed by AWS, for up to 30% higher throughput and up to 45% lower cost per inference compared to GPU instances. Lyft, one of the largest transportation networks in the United States and Canada, launched its Level 5 autonomous vehicle division in 2017 to develop a self-driving system to help millions of riders. Lyft Level 5 aggregates over 10 terabytes of data each day to train ML models for their fleet of autonomous vehicles. Managing ML workloads on their own was becoming time-consuming and expensive. Says Alex Bain, Lead for ML Systems at Lyft Level 5: “Using Amazon SageMaker distributed training, we reduced our model training time from days to couple of hours. By running our ML workloads on AWS, we streamlined our development cycles and reduced costs, ultimately accelerating our mission to deliver self-driving capabilities to our customers.“ #4 – Build Secure and Compliant ML Systems Security is always priority #1 at AWS. It’s particularly important to customers operating in regulated industries such as financial services or healthcare, as they must implement their solutions with the highest level of security and compliance. For this purpose, Amazon SageMaker implements many security features, making it compliant with the following global standards: SOC 1/2/3, PCI, ISO, FedRAMP, DoD CC SRG, IRAP, MTCS, C5, K-ISMS, ENS High, OSPAR, and HITRUST CSF. It’s also HIPAA BAA eligible. Says Ashok Srivastava, Chief Data Officer, Intuit: “With Amazon SageMaker, we can accelerate our Artificial Intelligence initiatives at scale by building and deploying our algorithms on the platform. We will create novel large-scale machine learning and AI algorithms and deploy them on this platform to solve complex problems that can power prosperity for our customers.” #5 – Annotate Data and Keep Humans in the Loop As ML practitioners know, turning data into a dataset requires a lot of time and effort. To help you reduce both, Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to annotate and build highly accurate training datasets at any scale (text, image, video, and 3D point cloud datasets). Says Magnus Soderberg, Director, Pathology Research, AstraZeneca: “AstraZeneca has been experimenting with machine learning across all stages of research and development, and most recently in pathology to speed up the review of tissue samples. The machine learning models first learn from a large, representative data set. Labeling the data is another time-consuming step, especially in this case, where it can take many thousands of tissue sample images to train an accurate model. AstraZeneca uses Amazon SageMaker Ground Truth, a machine learning-powered, human-in-the-loop data labeling and annotation service to automate some of the most tedious portions of this work, resulting in reduction of time spent cataloging samples by at least 50%.” Amazon SageMaker is Evaluated The hundreds of new features added to Amazon SageMaker since launch are testimony to our relentless innovation on behalf of customers. In fact, the service was highlighted in February 2020 as the overall leader in Gartner’s Cloud AI Developer Services Magic Quadrant. Gartner subscribers can click here to learn more about why we have an overall score of 84/100 in their “Solution Scorecard for Amazon SageMaker, July 2020”, the highest rating among our peer group. According to Gartner, we met 87% of required criteria, 73% of preferred, and 85% of optional. Announcing a Price Reduction on GPU Instances To thank our customers for their trust and to show our continued commitment to make Amazon SageMaker the best and most cost-effective ML service, I’m extremely happy to announce a significant price reduction on all ml.p2 and ml.p3 GPU instances. It will apply starting October 1st for all SageMaker components and across the following regions: US East (N. Virginia), US East (Ohio), US West (Oregon), EU (Ireland), EU (Frankfurt), EU (London), Canada (Central), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Seoul), Asia Pacific (Tokyo), Asia Pacific (Mumbai), and AWS GovCloud (US-Gov-West). Instance Name Price Reduction ml.p2.xlarge -11% ml.p2.8xlarge -14% ml.p2.16xlarge -18% ml.p3.2xlarge -11% ml.p3.8xlarge -14% ml.p3.16xlarge -18% ml.p3dn.24xlarge -18% Getting Started with Amazon SageMaker As you can see, there are a lot of exciting features in Amazon SageMaker, and I encourage you to try them out! Amazon SageMaker is available worldwide, so chances are you can easily get to work on your own datasets. The service is part of the AWS Free Tier, letting new users work with it for free for hundreds of hours during the first two months. If you’d like to kick the tires, this tutorial will get you started in minutes. You’ll learn how to use SageMaker Studio to build, train, and deploy a classification model based on the XGBoost algorithm. Last but not least, I just published a book named “Learn Amazon SageMaker“, a 500-page detailed tour of all SageMaker features, illustrated by more than 60 original Jupyter notebooks. It should help you get up to speed in no time. As always, we’re looking forward to your feedback. Please share it with your usual AWS support contacts, or on the AWS Forum for SageMaker. - Julien

Microsoft Azure has just released a Blockchain-as-a-Service product that uses Ethereum to support blockchain with a set of templates to deploy and configure your choice of blockchain network. This can be done with minimal Azure and blockchain knowledge. The conventional blockchain in the open is based on Proof-of-Work (PoW) and requires mining as the parties do not trust each other. An enterprise blockchain does not require PoW but is based on Proof-of-Authority (PoA) where approved identities or validators on a blockchain, validate the transactions on the blockchain. The PoA product features a decentralized application (DApp) called the Governance DApp. Blockchains in this new model can be deployed in 5-45 minutes depending on the size and complexity of the network. The PoA network comes with security features such as identity leasing system to ensure no two nodes carry the same identity. There are also other features to achieve good performance. Web assembly smart contracts: Solidity is cited as one of the pain areas when developing smart contracts on Ethereum. This feature allows developers to use familiar languages such as C, C++, and Rust. Azure Monitor: Used to track node and network statistics. Developers can view the underlying blockchain to track statistics while the network admins can detect and prevent network outages. Extensible governance: With this feature, customers can participate in a consortium without managing the network infrastructure. It can be optionally delegated to an operator of their choosing. Governance DApp: Provides a decentralized governance in which network authority changes are administered via on-chain voting done by select administrators. It also contains validator delegation for authorities to manage their validator nodes that are set up in each PoA deployment. Users can audit change history, each change is recorded, providing transparency and auditability. Source: Microsoft Blog Along with these features, the Governance DApp will also ensure each consortium member has control over their own keys. This enables secure signing on a wallet chosen by the user. The blog mentions “In the case of a VM or regional outage, new nodes can quickly spin up and resume the previous nodes’ identities.” To know more visit the official Microsoft Blog. Read next Automate tasks using Azure PowerShell and Azure CLI [Tutorial] Microsoft announces general availability of Azure SQL Data Sync Microsoft supercharges its Azure AI platform with new features

Google today announced that it is stepping back from managing the Kubernetes architecture and is funding the Cloud Native Computing Foundation (CNCF) $9M in GCP credits for a successful transition. These credits are split over a period of three years to cover infrastructure costs. Google is also handing over operational control of the Kubernetes project to the CNCF community. They will now take ownership of day-to-day operational tasks such as testing and builds, as well as maintaining and operating the image repository and download infrastructure. Kubernetes was first created by Google in 2014. Since then Google has been providing Kubernetes with the cloud resources that support the project development. These include CI/CD testing infrastructure, container downloads, and other services like DNS, all running on Google Cloud Platform. With Google passing the reign to CNCF, it’s goal is to make make sure “Kubernetes is ready to scale when your enterprise needs it to”. The $9M grant will be dedicated to building the world-wide network and storage capacity required to serve container downloads. In addition, a large part of this grant will also be dedicated to funding scalability testing, which runs 150,000 containers across 5,000 virtual machines. “Since releasing Kubernetes in 2014, Google has remained heavily involved in the project and actively contributes to its vibrant community. We also believe that for an open source project to truly thrive, all aspects of a mature project should be maintained by the people developing it. In passing the baton of operational responsibilities to Kubernetes contributors with the stewardship of the CNCF, we look forward to seeing how the project continues to evolve and experience breakneck adoption” said Sarah Novotny, Head of Open Source Strategy for Google Cloud. The CNCF foundation includes a large number of companies of the likes of Alibaba Cloud, AWS, Microsoft Azure, IBM Cloud, Oracle, SAP etc. All of these will be profiting from the work of the CNCF and the Kubernetes community. With this move, Google is perhaps also transferring the load of running the Kubernetes infrastructure to these members. As mentioned in their blog post, they look forward to seeing the new ideas and efficiencies that all Kubernetes contributors bring to the project’s operations. To learn more, check out the CNCF announcement post and the Google Cloud Platform blog. Kubernetes 1.11 is here! Google Kubernetes Engine 1.10 is now generally available and ready for enterprise use. Kubernetes Container 1.1 Integration is now generally available.

Last week, Google announced that its Cloud Healthcare API is now available in beta. The API acts as a bridge between on-site healthcare systems and applications that are hosted on Google Cloud. This API is HIPAA compliant, ecosystem-ready and developer-friendly. The aim of the team at Google is to give hospitals and other healthcare facilities more analytical power with the help of Cloud Healthcare API. The official post reads, "From the beginning, our primary goal with Cloud Healthcare API has been to advance data interoperability by breaking down the data silos that exist within care systems. The API enables healthcare organizations to ingest and manage key data and better understand that data through the application of analytics and machine learning in real time, at scale." This API offers a managed solution for storing and accessing healthcare data in Google Cloud Platform (GCP). With the help of this API, users can now explore new capabilities for data analysis, machine learning, and application development for healthcare solutions. The  Cloud Healthcare API also simplifies app development and device integration to speed up the process. This API also supports standards-based data formats and protocols of existing healthcare tech. For instance, it will allow healthcare organizations to stream data processing with Cloud Dataflow, analyze data at scale with BigQuery, and tap into machine learning with the Cloud Machine Learning Engine. Features of Cloud Healthcare API Compliant and certified This API is HIPAA compliant and HITRUST CSF certified. Google is also planning ISO 27001, ISO 27017, and ISO 27018 certifications for Cloud Healthcare API. Explore your data This API allows users to explore their healthcare data by incorporating advanced analytics and machine learning solutions such as BigQuery, Cloud AutoML, and Cloud ML Engine. Managed scalability Google’s Cloud Healthcare API provides web-native, serverless scaling which is optimized by Google’s infrastructure. Users can simply activate the API to send requests as the initial capacity configuration is not required. Apigee Integration This API integrates with Apigee, which is recognized by Gartner as a leader in full lifecycle API management, for delivering app and service ecosystems around user data. Developer-friendly This API organizes users’ healthcare information into datasets with one or more modality-specific stores per set where each store exposes both a REST and RPC interface. Enhanced data liquidity The API also supports bulk import and export of FHIR data and DICOM data, which accelerates delivery for applications with dependencies on existing datasets. It further provides a convenient API for moving data between projects. The official post reads, “While our product and engineering teams are focused on building products to solve challenges across the healthcare and life sciences industries, our core mission embraces close collaboration with our partners and customers.” Google will highlight what its partners, including the American Cancer Society, CareCloud, Kaiser Permanente, and iDigital are doing with the API at the ongoing Google Cloud Next. To know more about this news, check out Google’s official announcement. Ian Goodfellow quits Google and joins Apple as a director of machine learning Google dissolves its Advanced Technology External Advisory Council in a week after repeat criticism on selection of members Google employees filed petition to remove anti-trans, anti-LGBTQ and anti-immigrant Kay Coles James from the AI council  

Last week, the team behind Ubuntu announced the release of Ubuntu 19.04 Disco Dingo Beta, which comes with Linux 5.0 support, GNOME 3.32, and more. Its stable version is expected to release on April 18th, 2019. Following are some of the updates in Ubuntu 19.04 Disco Dingo: Updates in Linux kernel Ubuntu 19.04 is based on Linux 5.0, which was released last month. It comes with support for AMD Radeon RX Vega M graphics processor, complete support for the Raspberry Pi 3B and the 3B+, Qualcomm Snapdragon 845, and much more. Toolchain Upgrades The tools are upgraded to their latest releases. The upgraded toolchain includes glibc 2.29, OpenJDK 11, Boost 1.67, Rustc 1.31, and updated GCC 8.3, Python 3.7.2 as default,  Ruby 2.5.3, PHP 7.2.15, and more. Updates in Ubuntu Desktop This release ships with the latest GNOME 3.32 giving it a refreshed visual design. It also brings a few performance improvements and new features: GNOME Disks now supports VeraCrypt, a utility used for on-the-fly encryption. A panel is added to the Settings menu to help users manage Thunderbolt devices. With this release, more shell components are cached in GPU RAM, which reduces load and increases FPS count. Desktop zoom works much smoother. An option is added to automatically submit error reports to the error reporting dialog window. Other updates include new Yaru icon sets, Mesa 19.0, QEMU 13.1, and libvirt 14.0. This release will be supported for 9 months until January 2020. Users who require Long Term Support are recommended to use Ubuntu 18.04 LTS instead. To read the full list of updates, visit Ubuntu’s official website. Chromium blacklists nouveau graphics device driver for Linux and Ubuntu users Ubuntu releases Mir 1.0.0 Ubuntu free Linux Mint Project, LMDE 3 ‘Cindy’ Cinnamon, released

VMware kicked off its VMworld 2019 US in San Francisco last week on 25th August and ended yesterday with a series of updates, spanning Kubernetes, Azure, security and more. This year’s event theme was “Make Your Mark” aimed at empowering VMworld 2019 attendees to learn, connect and innovate in the world of IT and business. 20,000 attendees from more than 100 countries descended to San Francisco for VMworld 2019. VMware CEO Pat Gelsinger took the stage, and articulated VMware’s commitment and support for TechSoup, a one-stop IT shop for global nonprofits. Gelsinger also put emphasis on the company's 'any cloud, any application, any device, with intrinsic security' strategy. “VMware is committed to providing software solutions to enable customers to build, run, manage, connect and protect any app, on any cloud and any device,” said Pat Gelsinger, chief executive officer, VMware. “We are passionate about our ability to drive positive global impact across our people, products and the planet.” Let us take a look at the key highlights of the show: VMworld 2019: CEO's take on shaping tech as a force for good The opening keynote from Pat Gelsinger had everything one would expect; customer success stories, product announcements and the need for ethical fix in tech. "As technologists, we can't afford to think of technology as someone else's problem," Gelsinger told attendees, adding “VMware puts tremendous energy into shaping tech as a force for good.” Gelsinger cited three benefits of technology which ended up opening the Pandora's Box. Free apps and services led to severely altered privacy expectations; ubiquitous online communities led to a crisis in misinformation; while the promise of blockchain has led to illicit uses of cryptocurrencies. "Bitcoin today is not okay, but the underlying technology is extremely powerful," said Gelsinger, who has previously gone on record regarding the detrimental environmental impact of crypto. This prism of engineering for good, alongside good engineering, can be seen in how emerging technologies are being utilised. With edge, AI and 5G, and cloud as the "foundation... we're about to redefine the application experience," as the VMware CEO put it. Read also: VMware reaches the goal of using 100% renewable energy in its operations, a year ahead of their 2020 vision Gelsinger’s 2018 keynote was about the theme of tech 'superpowers'. Cloud, mobile, AI, and edge. This time, more focus was given to how the edge was developing. Whether it was a thin edge, containing a few devices and an SD-WAN connection, a thick edge of a remote data centre with NFV, or something in between, VMware aims to have it all covered. "Telcos will play a bigger role in the cloud universe than ever before," said Gelsinger, referring to the rise of 5G. "The shift from hardware to software [in telco] is a great opportunity for US industry to step in and play a great role in the development of 5G." VMworld 2019 introduces Tanzu to build, run and manage software on Kubernetes VMware is moving away from virtual machines to containerized applications. On the product side VMware Tanzu was introduced, a new product portfolio that aims to enable enterprise-class building, running, and management of software on Kubernetes. In Swahili, ’tanzu’ means the growing branch of a tree and in Japanese, ’tansu’ refers to a modular form of cabinetry. For VMware, Tanzu is their growing portfolio of solutions that help build, run and manage modern apps. Included in this is Project Pacific, which is a tech preview focused on transforming VMware vSphere into a Kubernetes native platform. "With project Pacific, we're bringing the largest infrastructure community, the largest set of operators, the largest set of customers directly to the Kubernetes. We will be the leading enabler of Kubernetes," Gelsinger said. Read also: VMware Essential PKS: Use upstream Kubernetes to build a flexible, cost-effective cloud-native platform Other product launches included an update to collaboration program Workspace ONE, including an AI-powered virtual assistant, as well as the launch of CloudHealth Hybrid by VMware. The latter, built on cloud cost management tool CloudHealth, aims to help organisations save costs across an entire multi-cloud landscape and will be available by the end of Q3. Collaboration, not compete with major cloud providers - Google Cloud, AWS & Microsoft Azure At VMworld 2019 VMware announced an extended partnership with Google Cloud earlier this month led the industry to consider the company's positioning amid the hyperscalers. VMware Cloud on AWS continues to gain traction - Gelsinger said Outposts, the hybrid tool announced at re:Invent last year, is being delivered upon - and the company also has partnerships in place with IBM and Alibaba Cloud. Further, VMware in Microsoft Azure is now generally available, with the facility to gradually switch across Azure data centres. By the first quarter of 2020, the plan is to make it available across nine global areas. Read also: Cloud Next 2019 Tokyo: Google announces new security capabilities for enterprise users The company's decision not to compete, but collaborate with the biggest public clouds has paid off. Gelsinger also admitted that the company may have contributed to some confusion over what hybrid cloud and multi-cloud truly meant. But the explanation from Gelsinger was pretty interesting. Increasingly, with organisations opting for different clouds for different workloads, and changing environments, Gelsinger described a frequent customer pain point for those nearer the start of their journeys. Do they migrate their applications or do they modernise? Increasingly, customers want both - the hybrid option. "We believe we have a unique opportunity for both of these," he said. "Moving to the hybrid cloud enables live migration, no downtime, no refactoring... this is the path to deliver cloud migration and cloud modernisation." As far as multi-cloud was concerned, Gelsinger argued: "We believe technologists who master the multi-cloud generation will own it for the next decade." Collaboration with NVIDIA to accelerate GPU services on AWS NVIDIA and VMware today announced their intent to deliver accelerated GPU services for VMware Cloud on AWS to power modern enterprise applications, including AI, machine learning and data analytics workflows. These services will enable customers to seamlessly migrate VMware vSphere-based applications and containers to the cloud, unchanged, where they can be modernized to take advantage of high-performance computing, machine learning, data analytics and video processing applications. Through this partnership, VMware Cloud on AWS customers will gain access to a new, highly scalable and secure cloud service consisting of Amazon EC2 bare metal instances to be accelerated by NVIDIA T4 GPUs, and new NVIDIA Virtual Compute Server (vComputeServer) software. “From operational intelligence to artificial intelligence, businesses rely on GPU-accelerated computing to make fast, accurate predictions that directly impact their bottom line,” said Jensen Huang, founder and CEO, NVIDIA. “Together with VMware, we’re designing the most advanced GPU infrastructure to foster innovation across the enterprise, from virtualization, to hybrid cloud, to VMware's new Bitfusion data center disaggregation.” Read also: NVIDIA’s latest breakthroughs in conversational AI: Trains BERT in under an hour, launches Project Megatron to train transformer based models at scale Apart from this, Gelsinger made special note to mention VMware's most recent acquisitions, with Pivotal and Carbon Black and discussed about where they fit in the VMware stack at the back. VMware’s hybrid cloud platform for Next-gen Hybrid IT VMware introduced new and expanded cloud offerings to help customers meet the unique needs of traditional and modern applications. VMware empowers IT operators, developers, desktop administrators, and security professionals with the company’s hybrid cloud platform to build, run, and manage workloads on a consistent infrastructure across their data center, public cloud, or edge infrastructure of choice. VMware uniquely enables a consistent hybrid cloud platform spanning all major public clouds – AWS, Azure, Google Cloud, IBM Cloud – and more than 60 VMware Cloud Verified partners worldwide. More than 70 million workloads run on VMware. Of these, 10 million are in the cloud. These are running in more than 10,000 data centers run by VMware Cloud providers. Take a look at the full list of VMworld 2019 announcements here. What’s new in cloud and virtualization this week? VMware signs definitive agreement to acquire Pivotal Software and Carbon Black Pivotal open sources kpack, a Kubernetes-native image build service Oracle directors support billion dollar lawsuit against Larry Ellison and Safra Catz for NetSuite deal

Microsoft released version 1.0 of Open Service Broker for Azure (OSBA) along with full support for Azure SQL, Azure Database for MySQL, and Azure Database for PostgreSQL. Microsoft announced the preview of Open Service Broker for Azure (OSBA) at the KubeCon 2017. OSBA is the simplest way to connect apps running on cloud-native environment (such as Kubernetes, Cloud Foundry, and OpenShift) and rich suite of managed services available on Azure. The OSBA 1.0 ensures to connect mission-critical applications to Azure’s enterprise-grade backing services. It is also ideal to run on a containerized environment like Kubernetes. In a recent announcement of a strategic partnership between Microsoft and Red Hat to provide  OpenShift service on Azure, Microsoft demonstrated the use of OSBA using an OpenShift project template. OSBA will enable customers to deploy Azure services directly from the OpenShift console and connect them to their containerized applications running on OpenShift. It also plans to collaborate with Bitnami to bring OSBA into KubeApps, for customers to deploy solutions like WordPress built on Azure Database for MySQL and Artifactory on Azure Database for PostgreSQL. Microsoft plans 3 additional focus areas for OSBA and the Kubernetes service catalog: Plans to expand the set of Azure services available in OSBA by re-enabling services such as Azure Cosmos DB and Azure Redis. These services will progress to a stable state as Microsoft will learn how customers intend to use them. They plan to continue working with the Kubernetes community to align the capabilities of the service catalog with the behavior that customers expect. With this, the cluster operator will have the ability to choose which classes/plans are available to developers. Lastly, Microsoft has a vision for the Kubernetes service catalog and the Open Service Broker API. It will enable developers to describe general requirements for a service, such as “a MySQL database of version 5.7 or higher”. Read the full coverage on Microsoft’s official blog post GitLab is moving from Azure to Google Cloud in July Announces general availability of Azure SQL Data Sync Build an IoT application with Azure IoT [Tutorial]

Linux is one of the most popular operating systems built around the Linux kernel by Linus Torvalds. Because it is free and open source, it gained a huge audience among developers very fast. Torvalds further welcomed other developers’ contributions to add to the kernel granted that they keep their contributions free. Due to this, thousands of developers have been working to improve Linux over the years, leading to its huge popularity today. Yesterday, Linus, who has been working on the Kernel for almost 30-years caught the Linux community by surprise as he apologized and opened up about going on a break over his ‘hurtful’ behavior that ‘contributed to an unprofessional environment’. In a long email to the Linux Kernel mailing list, Torvalds announced Linux 4.19 release candidate and then talked about his ‘look yourself in the mirror’ moment. “This week people in our community confronted me about my lifetime of not understanding emotions. My flippant attacks in emails have been both unprofessional and uncalled for. Especially at times when I made it personal. In my quest for a better patch, this made sense to me. I know now this was not OK and I am truly sorry” admitted Torvalds. The confession came about after Torvalds confessed to messing up the schedule of the Maintainer's Summit, a meeting of Linux's top 40 or so developers, by planning a family vacation. “Yes, I was somewhat embarrassed about having screwed up my calendar, but honestly, I was mostly hopeful that I wouldn't have to go to the kernel summit that I have gone to every year for just about the last two decades. That whole situation then started a whole different kind of discussion --  I realized that I had completely mis-read some of the people involved,” confessed Torvalds. Torvalds has been notorious for his outspoken nature and outbursts towards others (especially the developers in the Linux Community). Sarah Sharps, Linux maintainer quit the Linux community in 2015 over Torvald’s offensive behavior and called it ‘toxic’. Torvalds exploded at Intel, earlier this year, for spinning Spectre fix as a security feature. Also, Torvalds responded with profanity, last year, about different approaches to security during a discussion about whitelisting the proposed features for Linux version 4.15. “Maybe I can get an email filter in place so that when I send email with curse-words, they just won't go out. I really had been ignoring some fairly deep-seated feelings in the Community...I am not an emotionally empathetic kind of person...I need to change some of my behavior, and I want to apologize to the people that my personal behavior hurt and possibly drove away from kernel development entirely,” writes Torvalds. Torvalds then went ahead to talk about him taking a break from the Linux Community. “This is not some kind of "I'm burnt out, I need to just go away" break. I'm not feeling like I don't want to continue maintaining Linux. I very much want to continue to do this project that I've been working on for almost three decades. I need to take a break to get help on how to behave differently and fix some issues in my tooling and workflow”. A discussion with over 500 comments has started already on Reddit regarding Torvald’s decision.  While some people are supporting Torvald by accepting his apology, there are others who feel that the apology was long overdue and will believe him after he puts his words into action. https://twitter.com/TejasKumar_/status/1041527028271312897 https://twitter.com/coreytabaka/status/1041468174397399041 Python founder resigns – Guido van Rossum goes ‘on a permanent vacation from being BDFL’ Facebook and Arm join Yocto Project as platinum members for embedded Linux development NSA researchers present security improvements for Zephyr and Fucshia at Linux Security Summit 201

AWS announced support for two new actions namely, redirect and fixed-response for elastic load balancing in Application Load Balancer last week. Elastic Load Balancing offers automatic distribution of the incoming application traffic. The traffic is distributed across targets, such as Amazon EC2 instances, IP addresses, and containers. One of the types of load balancers that Elastic load offers is Application Load Balancer. Application Load Balancer simplifies and improves the security of your application as it uses only the latest SSL/TLS ciphers and protocols. It is best suited for load balancing of HTTP and HTTPS traffic and operates at the request level which is layer 7. Redirect and Fixed response support simplifies the deployment process while leveraging the scale, availability, and reliability of Elastic Load Balancing. Let’s discuss how these latest features work. The new redirect action enables the load balancer to redirect the incoming requests from one URL to another URL. This involves redirecting HTTP requests to HTTPS requests, allowing more secure browsing, better search ranking and high SSL/TLS score for your site. Redirects also help redirect the users from an old version of an application to a new version. The fixed-response actions help control which client requests are served by your applications. This helps you respond to the incoming requests with HTTP error response codes as well as custom error messages from the load balancer. There is no need to forward the request to the application. If you use both redirect and fixed-response actions in your Application Load Balancer, then the customer experience and the security of your user requests are improved considerably. Redirect and fixed-response actions are now available for your Application Load Balancer in all AWS regions. For more details, check out the Elastic Load Balancing documentation page. Integrate applications with AWS services: Amazon DynamoDB & Amazon Kinesis [Tutorial] Build an IoT application with AWS IoT [Tutorial]

On Monday, the Kubernetes team announced that DigitalOcean, which was available in Early Access, is now accessible as Limited Availability. DigitalOcean simplifies the container deployment process that accompanies plain Kubernetes and offers Kubernetes container hosting services. Incorporating DigitalOcean’s trademark simplicity and ease of use, they aim to reduce the headache involved in setting up, managing and securing Kubernetes clusters. DigitalOcean incidentally are also the people behind Hacktoberfest which runs all of October in partnership with GitHub to promote open source contribution. The Early Access availability was well received by users who commented on the simplicity of configuring and provisioning a cluster. They appreciated that deploying and running containerized services consumed hardly any time. Users also brought to light issues and feedback that was utilized to increase reliability and resolve a number of bugs, thus improving user experience in the limited availability of DigitalOcean Kubernetes. The team also notes that during early access, they had a limited set of free hardware resources for users to deploy to. This restricted the total number of users they could provide access to. In the Limited Availability phase, the team hopes to open up access to anyone who requests it. That being said, the Limited Availability will be a paid product. Why should users consider DigitalOcean Kubernetes? Each customer has their own Dedicated Managed Cluster. This provides security and isolation for their containerized applications with access to the full Kubernetes API. DigitalOcean products provide storage for any amount of data.   Cloud Firewalls make it easy to manage network traffic in and out of the Kubernetes cluster. DigitalOcean provides cluster security scanning capabilities to alert users of flaws and vulnerabilities. In typical Kubernetes environments; metrics, logs, and events can be lost if nodes are spun down. To help developers learn from the performance of past environments, DigitalOcean stores this information separately from the node indefinitely. To know more about these features, head over to their official blog page. Some benefits for users of Limited Availability: Users will be able to provision Droplet workers in many more of regions with full support. To test out their containers in an orchestrated environment, they can start with a single node cluster using a $5/mo Droplet. As they scale their applications, users can add worker pools of various Droplet sizes, attach persistent storage using DigitalOcean Block Storage for $0.10/GB per month, and expose Kubernetes services with a public IP using $10/mo Load Balancers. This is a highly available service designed to protect against application or hardware failures while spreading traffic across available resources. Looks like users are really excited about this upgrade: Source: DigitalOcen Blog Users that have already signed up for Early Access, will receive an email shortly with details about how to get started. To know more about this news, head over to DigitalOcean’s Blog post. Kubernetes 1.12 released with general availability of Kubelet TLS Bootstrap, support for Azure VMSS Nvidia GPUs offer Kubernetes for accelerated deployments of Artificial Intelligence workloads  

Yesterday, Microsoft announced NVIDIA GPU Cloud (NGC) support on its Azure platform. Following this, data scientists, researchers, and developers can build, test, and deploy GPU computing projects on Azure. With this availability, users can run containers from NGC with Azure giving them access to on-demand GPU computing that can scale as per their requirement. This eventually eliminates the complexity of software integration and testing. The need for NVIDIA GPU Cloud (NGC) It is challenging and time-consuming to build and test reliable software stacks to run popular deep learning software such as TensorFlow, Microsoft Cognitive Toolkit, PyTorch, and NVIDIA TensorRT. This is due to the operating level and updated framework dependencies. Finding, installing, and testing the correct dependency is quite a hassle as it is supposed to be done in a multi-tenant environment and across many systems. NGC eliminates these complexities by offering pre-configured containers with GPU-accelerated software. Users can now access 35 GPU-accelerated containers for deep learning software, high-performance computing applications, high-performance visualization tools and much more enabled to run on the following Microsoft Azure instance types with NVIDIA GPUs: NCv3 (1, 2 or 4 NVIDIA Tesla V100 GPUs) NCv2 (1, 2 or 4 NVIDIA Tesla P100 GPUs) ND (1, 2 or 4 NVIDIA Tesla P40 GPUs) According to NVIDIA, these same NVIDIA GPU Cloud (NGC) containers can also work across Azure instance types along with different types or quantities of GPUs. Using NGC containers with Azure is quite easy. Users just have to sign up for a free NGC account before starting, then visit Microsoft Azure Marketplace to find the pre-configured NVIDIA GPU Cloud Image for Deep Learning and high-performance computing. Once you launch the NVIDIA GPU instance on Azure, you can pull the containers you want from the NGC registry into your running instance. You can find detailed steps to setting up NGC in the Using NGC with Microsoft Azure documentation. Microsoft Azure’s new governance DApp: An enterprise blockchain without mining NVIDIA leads the AI hardware race. But which of its GPUs should you use for deep learning? NVIDIA announces pre-orders for the Jetson Xavier Developer Kit, an AI chip for autonomous machines, at $2,499  

Organizations all over the world rely on Cloud CDN for fast, reliable web and video content delivery. Now, we’re making it even easier for you to take advantage of our global network and cache infrastructure by reducing the cost of Cloud CDN for your content delivery going forward. First, we’re reducing the price of cache fill (content fetched from your origin) charges across the board, by up to 80%. You still get the benefit of our global private backbone for cache fill though—ensuring continued high performance, at a reduced cost. We’ve also removed cache-to-cache fill charges and cache invalidation charges for all customers going forward. This price reduction, along with our recent introduction of a new set of flexible caching capabilities, makes it even easier to use Cloud CDN to optimize the performance of your applications. Cloud CDN can now automatically cache web assets, video content or software downloads, control exactly how they should be cached, and directly set response headers to help meet web security best practices. You can review our updated pricing in our public documentation, and customers egressing over 1PB per month should reach out to our sales team to discuss commitment-based discounts as part of your migration to Google Cloud. To read more about Cloud CDN, or begin using it, start here.

Microsoft will be offering a new tool called Windows Sandbox next year with a Windows 10 update. Revealed this Tuesday, it provides an environment to safely test EXE applications before running them on your computer. Windows sandbox features Windows Sandbox is an isolated desktop environment where users can run untrusted software without any risk of them having any effects on your computer. Any application you install in Windows Sandbox is contained in the sandbox and cannot affect your computer. All software with their files and state are permanently deleted when a Windows Sandbox is closed. You need Windows 10 Pro or Windows 10 Enterprise to use it and will be shipped with an update, no separate download needed. Every run of Windows Sandbox is new and runs like a fresh installation of Windows. Everything is deleted when you close Windows Sandbox. It uses hardware-based virtualization for kernel isolation based on Microsoft’s hypervisor. A separate kernel isolates it from the host machine. It has an integrated kernel scheduler and virtual GPU. Source: Microsoft website Requirements In order to use this new feature based on Hyper-V, you’ll need, AMD64 architecture, virtualization capabilities enabled in BIOS, minimum 4GB RAM (8GB recommended), 1 GB of free disk space (SSD recommended), and dual-core CPU (4 cores with hyperthreading recommended). What are the people saying The general sentiment towards this release is positive. https://twitter.com/AnonTechOps/status/1075509695778041857 However, a comment on Hacker news suggests that this might not be that useful for its intended purpose: “Ironically, even though the recommended use for this in the opening paragraph is to combat malware, I think that will be the one thing this feature is no good at. Doesn’t even moderately sophisticated malware these days try to detect if it’s in a sandbox environment? A fresh-out-of-the-box Windows install must be a giant red flag for that.” Meanwhile, if you’re on Windows 7 or Windows 8, you can try Sandboxie. For more technical details under the hood of Sandbox, visit the Microsoft website. Oracle releases VirtualBox 6.0.0 with improved graphics, user interface and more Chaos engineering platform Gremlin announces $18 million series B funding and new feature for “full-stack resiliency” Are containers the end of virtual machines?

Last week, GitHub announced that they have built a new integration to enable software teams to connect their code on GitHub.com to their projects on Jira Software Cloud. This integration updates Jira with data from GitHub, providing a better visibility into the current status of your project. What are the advantages of this new GitHub and Jira integration? No need to constantly switch between GitHub and Jira With your GitHub account linked to Jira, your team can see the branches, commit messages, and pull request in the context of the Jira tickets they’re working on. This integration provides a deeper connection by allowing you to view references to Jira in GitHub issues and pull requests. Source: GitHub Improved capabilities This new GitHub-managed app provides improved security, along with the following capabilities: Smart commits: You can use smart commits to update the status, leave a comment, or log time without having to leave your command line or GitHub View from within a Jira ticket: You can view associated pull requests, commits, and branches from within a Jira ticket Searching Jira issues: You can search for Jira issues based on related GitHub information, such as open pull requests. Check the status of development work: The status of development work can be seen from within Jira projects Keep Jira issues up to date: You can automatically keep your Jira issues up to date while working in GitHub Install the Jira Software and GitHub app to connect your GitHub repositories to your Jira instance. The previous version of the Jira integration will be deprecated in favor of this new GitHub-maintained integration. Once the migration is complete, the legacy integration (DVCS connector) is disabled automatically. Read the full announcement at the GitHub blog. 4 myths about Git and GitHub you should know about GitHub addresses technical debt, now runs on Rails 5.2.1 GitLab raises $100 million, Alphabet backs it to surpass Microsoft’s GitHub