Tech News - Cloud & Networking

Infrastructure made of connected things is highly trending as organizations are in the process of adopting Internet of Things. At the same time security concerns around these connected devices continues to be a bottleneck for IoT adoption. In an effort to improve IoT security, earlier this month, Microsoft released Azure Sphere, a cost-effective way of securing connected devices. Gartner claims that worldwide spending on IoT security will reach 1.5 billion in 2018. Azure Sphere is basically a suite of services, used to enhance IoT security. Following are the services included in the suite: Azure Sphere MCUs These are a certified class of microcontrollers specially designed for security of internet of things. It follows a cross-over mechanism which allows the combination of running realt-time and application processors with built-in microsoft security mechanism and connectivity. MCU chips are designed using custom silicon security technology, made by Microsoft. Some of the highlights are: A pluton security subsystem to execute complex cryptographic operations A cross-over MCU with the combination of both Cortex-A and Cortext M class processor. Build-in network connectivity to ensure devices are upto date Azure Sphere OS Azure Sphere OS is nothing but a Linux distro used to securely run the internet of things. This highly scalable and secure operating system can be used to run the specialized MCUs by adding an extra layer of security. Some of the highlights are: Secured application containers focussing on agility and robustness A custom Linux Kernel enabling silicon diversity and innovation A security monitor to manage access and integrity The Azure Sphere Security Service An end-to-end security service solely dedicated to secure Azure sphere devices, enhancing security, identifying threats, and managing trust between cloud and device endpoints. Following are the highlights: Protects your devices using certificate based-authentication system. Ensure devices authenticity by ensuring that they are running on genuine software Managing automated updates for Azure Sphere OS, for threat and incident response Easy deployment of software updates to Azure Sphere connected devices. For more information, refer the official Microsoft blog. Serverless computing wars: AWS Lambdas vs Azure Functions How to call an Azure function from an ASP.NET Core MVC application

On Tuesday, Google announced its plans to acquire cloud migration company Alooma, which helps other companies move their data from multiple sources into a single data warehouse. Alooma not only provides services to help with migrating to the cloud but also helps in cleaning up this data and then using it for Artificial Intelligence and machine learning use cases. Google Cloud’s blog states that “ The addition of Alooma, subject to closing conditions, is a natural fit that allows us to offer customers a streamlined, automated migration experience to Google Cloud, and give them access to our full range of database services, from managed open source database offerings to solutions like Cloud Spanner and Cloud Bigtable” The financial details of the deal haven't been released yet. In early 2016, Alooma raised about $15 million, including an $11.2 million Series A round led by Lightspeed Venture Partners and Sequoia Capital. Aloomas’ blog states that “Joining Google Cloud will bring us one step closer to delivering a full self-service database migration experience bolstered by the power of their cloud technology, including analytics, security, AI, and machine learning” In a statement to TechCrunch, Google says “Regarding supporting competitors, yes, the existing Alooma product will continue to support other cloud providers. We will only be accepting new customers that are migrating data to Google Cloud Platform, but existing customers will continue to have access to other cloud providers.” This means that, after the deal is closed, Alooma will not accept any new customers who want to migrate data to any competitors--for instance, Amazon’s Azure. Those who use Alooma in combination with AWS, Azure and other non-Google services will likely start looking for other solutions. Microsoft acquires Citus Data with plans to create a ‘Best Postgres Experience’ Autodesk acquires PlanGrid for $875 million, to digitize and automate construction workflows GitHub acquires Spectrum, a community-centric conversational platform

Yesterday, Cloud Native Computing Foundation (CNCF) accepted Cortex as a CNCF Sandbox project. Cortex is an open source, horizontally scalable, multi-tenant Prometheus-as-a-service. It provides long-term storage for Prometheus metrics when used as a remote write destination. It also comes with a horizontally scalable, Prometheus-compatible query API. It provides uses cases for: Service providers to enable them to manage a large number of Prometheus instances and provide long-term storage. Enterprises to centralize management of large-scale Prometheus deployments and ensure long-term durability of Prometheus data. Originally developed by Weaveworks, it is now being used in production by organizations like Grafana Labs, FreshTracks, and EA. How does it work? The following diagram shows its architecture: Source: CNCF 1. Scraping samples: First, a Prometheus instance scraps all of the users’ services and then forwards them to a Cortex deployment. It does this using the remote_write API, which was added to Prometheus to support Cortex and other integrations. 2. Distributor distributes the samples: The instance then sends all these samples to distributor, which is a stateless service that consults the ring to figure out which ingesters should ingest the sample. The ingesters are arranged using a consistent hash ring, keyed on the fingerprint of the time series, and stored in a consistent data store, such as Consul. Distributor finds the owner ingester and forwards the sample to it and also to two ingesters after it in the ring. This means if an ingester goes down, we have two others that have its data. 3. Ingesters make chunks of samples: Ingesters continuously receive a stream of samples and group them together in chunks. These chunks are then stored in a backend database, such as DynamoDB, BigTable, or Cassandra. Ingesters facilitate this chunking process so that Cortex isn’t constantly writing to its backend database. Alexis Richardson, CEO of Weaveworks believes that being a CNCF Sandbox project will help grow the Prometheus ecosystem: “By joining CNCF, Cortex will have a neutral home for collaboration between contributor companies, while allowing the Prometheus ecosystem to grow a more robust set of integrations and solutions. Cortex already has a strong affinity with several CNCF technologies, including Kubernetes, gRPC, OpenTracing and Jaeger, so it’s a natural fit for us to continue building on these interoperabilities as part of CNCF.” To know more in detail, check out the official announcement by CNCF and also read What is Cortex?, a blog post published on Weaveworks Blog. Google Cloud hands over Kubernetes project operations to CNCF, grants $9M in GCP credits CNCF Sandbox, the home for evolving cloud native projects, accepts Google’s OpenMetrics Project Modern Cloud Native architectures: Microservices, Containers, and Serverless – Part 1

Last week, the team at Opus announced the general availability of Opus Audio Codec version 1.3. Opus 1.3 comes along with a new set of features, namely, a recurrent neural network, reliable speech/music detector, convenience, ambisonics support, efficient memory, compatibility with RFC 6716 and a lot more. Opus is an open and royalty-free audio codec, which is highly useful for all audio applications, right from music streaming and storage to high-quality video-conferencing and VoIP. Six years after its standardization by the IETF, Opus is included in all major browsers and mobile operating systems, used for a wide range of applications and is the default WebRTC codec. New features in Opus Audio Codec 1.3 Reliable speech/music detector powered by machine learning Opus 1.3 promises a new speech/music detector. As it is based on a recurrent neural network, it is way simpler and reliable than the detector used in version 1.1.The speech/music detector in earlier versions was based on a simple (non-recurrent) neural network, followed by an HMM-based layer to combine the neural network results over time. Opus 1.3 introduces a new recurrent neuron which is the Gated Recurrent Unit (GRU). The GRU does not just learn how to use its input and memory at a time, but it also promises to learn, how and when to update its memory. This, in turn, helps it to remember information for a longer period of time. Mixed Content encoding gets better Mixed content encoding, especially at bit rates below 48 kb/s, will get more convenient as the new detector helps in improving the performance of Opus. Developers will experience a great change in speech encoding at lower bit rates, both for mono and stereo. Encode 3D audio soundtracks for VR easily This release comes along with ambisonics support. Ambisonics can be used to encode 3D audio soundtracks for VR and 360 videos. Opus detector won’t take much of your space The Opus detector has just 4986 weights (that fit in less than 5 KB) and takes about 0.02% memory of CPU to run in real-time, instead of thousands of neurons and millions of weights running on a GPU. Additional Updates Improvements in Security/hardening, Voice Activity Detector (VAD), and speech/music classification using an RNN are simply add-ons. The major bug fixes in this release are CELT PLC and bandwidth detection fixes. Read more about the release on Mozilla’s official website. Also, check out a demo for more details. YouTube starts testing AV1 video codec format, launches AV1 Beta Playlist Google releases Oboe, a C++ library to build high-performance Android  audio apps How to perform Audio-Video-Image Scraping with Python

Today, in a blog post, Dropbox explained how the Prilo system used by the team has automated most of the processes of the company, that were previously manually attended to by Dropbox personnel. Pirlo is used by Dropbox in two main areas- validate and configure network switches and ensure the reliability of servers before entering production. This has, in turn, helped Dropbox to safely manage their physical infrastructure operations with ease. Pirlo consists of a distributed MySQL-backed job queue built by Dropbox itself, using primitives like gRPC, service discovery, and our managed MySQL clusters. Switch provisioning at Dropbox is handled by the TOR STarter which is a Pirlo component. The TOR Starter validates and configures switches in Dropbox datacenter server racks, PoP server racks, and at the different layers of the data center fabric; responsible to connect racks in the same facility together. Server provisioning and repair validation is handled by Pirlo Server Validation. All new servers arriving at the company are validated using this component. Repaired servers are also validated before they are transitioned back into production. Pirlo has automated these manual processes at Dropbox and has led to a reduction in downtime, outages, and inefficiencies associated with the incomplete or erroneous fixing of the systems. By reducing manual work, employees can now focus their attention to more value adding jobs. Before using Pirlo, the above tasks had to be performed by operations engineers and subject matter experts who used various server error logs to take appropriate actions to fix failed hardware. After applying the remediation actions, the engineer would send the machine back into production by sending the server to Dropbox re-imaging system. If the remediation actions didn’t fix the system or properly prepare it for re-imaging, the server would be sent back to the operations engineer for additional fixing. This would end up consuming a lot of the operation engineer's time as well as company resources. Operating engineers who used Pirlo system steadily increased their output by 40+%. The automation of manual tasks allowed engineers to address more issues in the same amount of time. You can head over to Dropbox’s official blog to explore the workings of Pirlo and how it benefited the organization. How to navigate files in a Vue app using the Dropbox API Tech jobs dominate LinkedIn’s most promising jobs in 2019 NGINX Hybrid Application Delivery Controller Platform improves API management, manages microservices and much more!

Google Cloud launched new Blockchain tools for developers on Monday, as a result of a collaboration with a DLT (distributed ledger technology) startup to help developers easily build apps. Google seems to take inspiration from AWS. Amazon’s cloud company partnered with Hyperledger Fabric earlier this year, introducing Blockchain templates which, allow developers to launch Ethereum apps without needing to write all the code required to create a smart contract. Digital Asset, a Blockchain platform services run by former JPMorgan executive Blythe Masters, will provide a software development kit to developers working on Google cloud. Along with this, Digital Asset Modeling Language (DAML), a platform-as-a-service (PaaS) program will also be made available to developers. The DAML PaaS program is now available through Google Cloud’s Orbitera Application Marketplace. According to Leonard Law, Head of Financial Services Platform at Google Cloud, “DLT has great potential to benefit customers not just in the financial services industry, but across many industries, and we’re excited to bring these developer tools to Google Cloud.” Blythe Masters, CEO of Digital Asset, also mentioned that the company is partnering with Google Cloud to provide a full stack solution to developers “so they can unleash the potential for web-paced innovation in Blockchain”. This, in turn, will help developers and organizations overcome some of the most common technical barriers to DLT application development today. With the arrival of the new Blockchain toolkit, developers will be able to easily manage the distributed systems for financial applications, games, etc. Oracle makes its Blockchain cloud service generally available Blockchain can solve tech’s trust issues – Imran Bashir  

Later last week, Red Hat announced that RHEL has deprecated KDE (K Desktop Environment) support. KDE Plasma Workspaces (KDE) is an alternative to the default GNOME desktop environment for RHEL. Major future release of Red Hat Enterprise Linux will no longer support using KDE instead of the default GNOME desktop environment. In the 90’s, the Red Hat team was entirely against KDE and had put lots of effort into Gnome. Since Qt was under a not-quite-free license that time, the Red Hat team was firmly behind Gnome. Steve Almy, principal product manager of Red Hat Enterprise Linux, told the Register, “Based on trends in the Red Hat Enterprise Linux customer base, there is overwhelming interest in desktop technologies such as Gnome and Wayland, while interest in KDE has been waning in our installed base.” Red Hat heavily backs the Linux desktop environment GNOME, which is developed as an independent open-source project. Also, it is used by a large bunch of other distros. Although Red Hat is indicating the end of KDE support in RHEL, KDE is very much its own independent project that will continue on its own, with or without support from future RHEL editions. Almy said, “While Red Hat made the deprecation note in the RHEL 7.6 notes, KDE has quite a few years to go in RHEL's roadmap.” This is simply a warning that certain functionality may be removed or replaced from RHEL in the future with functionality similar or more advanced to the one deprecated. Though KDE, as well as anything listed in Chapter 51 of the Red Hat Enterprise Linux 7.6 release notes,  will continue to be supported for the life of Red Hat Enterprise Linux 7. Read more about this news on the official website of Red Hat. Red Hat released RHEL 7.6 Red Hat Enterprise Linux 7.5 (RHEL 7.5) now generally available Red Hat Enterprise Linux 7.6 Beta released with focus on security, cloud, and automation

Kubernetes v1.12 now offers alpha support for volume snapshotting. This will allow users to create or delete volume snapshots, and natively create new volumes from a snapshot using the Kubernetes API. A snapshot represents a copy of a volume at that particular instant of time. This snapshot can be used to provision a new volume that can be pre-populated with the snapshot data or to restore the existing volume to a previous state. Importance of adding Snapshots to Kubernetes The main goal of the Kubernetes team is to create an abstraction layer between distributed systems applications and underlying clusters. The layer will ensure that application deployment requires no "cluster specific" knowledge. Snapshot operations are a critical functionality for many stateful workloads. For instance, a database administrator may want to snapshot a database volume before starting a database operation. By providing a standard way to trigger snapshot operations in the Kubernetes API, users don’t have to manually execute storage system specific operations around the Kubernetes API. They can instead incorporate snapshot operations in a cluster agnostic way into their tooling and policy assured that it will work against arbitrary Kubernetes clusters regardless of the underlying storage. These snapshot primitives help to develop advanced, enterprise-grade, storage administration features for Kubernetes which includes data protection, data replication, and data migration. 3 new API objects introduced by Kubernetes Volume Snapshots: #1 VolumeSnapshot The creation and deletion of this object depicts if a user wants to create or delete a cluster resource (a snapshot). It is used to request the creation of a snapshot for a specified volume. It gives the user information about snapshot operations like the timestamp at which the snapshot was taken and whether the snapshot is ready to use. #2 VolumeSnapshotContent This object is created by the CSI volume driver once a snapshot has been successfully created. It contains information about the snapshot including its ID. This object represents a provisioned resource on the cluster (a snapshot). Once a snapshot is created, the VolumeSnapshotContent object binds to the VolumeSnapshot- with a one to one mapping- for which it was created. #3 VolumeSnapshotClass This object created by cluster administrators describes how snapshots should be created. It includes the driver information, how to access the snapshot, etc. These Snapshot objects are defined as CustomResourceDefinitions (CRDs).  End users need to verify if a CSI driver that supports snapshots is deployed on their Kubernetes cluster. CSI Drivers that support snapshots will automatically install the required CRDs. Limitations of the alpha implementation of snapshots The alpha implementation does not support reverting an existing volume to an earlier state represented by a snapshot It does not support "in-place restore" of an existing PersistentVolumeClaim from a snapshot. Users can provision a new volume from a snapshot. However, updating an existing PVC to a new volume and reverting it back to an earlier state is not allowed. No snapshot consistency guarantees given beyond any of those provided by storage system An example of creating new snapshots and importing existing snapshots is explained well on the Kubernetes Blog. Head over to  the team's Concepts page or Github to find more official documentation of the snapshot feature. ‘AWS Service Operator’ for Kubernetes now available allowing the creation of AWS resources using kubectl Limited Availability of DigitalOcean Kubernetes announced! Google Cloud hands over Kubernetes project operations to CNCF, grants $9M in GCP credits

The AWS team introduced AWS DataSync, an online data transfer service for automating data movement, yesterday. AWS DataSync offers data transfer from on-premises storage to Amazon S3 or Amazon Elastic File System (Amazon EFS) and vice versa. Let’s have a look at what’s new in AWS DataSync. Key Functionalities Move data 10x faster: AWS DataSync uses a purpose-built data transfer protocol along with a parallel, multi-threaded architecture that has the capability to run 10 times as fast as open source data transfer. This also speeds up the migration process and the recurring data processing workflows for analytics, machine learning, and data protection processes. Per-gigabyte fee: It is a managed service and you only need to pay the per-gigabyte fee which is paying only for the amount of data that you transfer. Other than that, there are no upfront costs and no minimum fees. DataSync Agent: The ‘AWS DataSync Agent’ is a crucial part of the service. It helps connect to your existing storage and the in-cloud service to automate, scale, and validate transfers. This, in turn, ensures that you don't have to write scripts, or modify the applications. Easy setup: It is very easy to set up and use (Console and CLI access is available). All you need to do is deploy the DataSync agent on-premises, then connect it to your file systems using the Network File System (NFS) protocol. After this, select Amazon EFS or S3 as your AWS storage, and you can start moving the data. Secure data transfer: AWS DataSync offers secure data transfer over the Internet or AWS Direct Connect. It also comes with automatic encryption and data. This, in turn, minimizes the in-house development and management which is needed for fast and secure transfers. Simplify and automate data transfer: With the help of AWS DataSync, you can perform one-time data migrations, transfer the on-premises data for timely in-cloud analysis, and automate the replication to AWS to ensure data protection and recovery. AWS DataSync is available for use from now in the US East, US West, Europe and Asia Pacific Regions. For more information, check out the official AWS DataSync blog post.  Amazon re:Invent 2018: AWS Key Management Service (KMS) Custom Key Store Amazon rolls out AWS Amplify Console, a deployment and hosting service for mobile web apps, at re:Invent 2018  Day 1 at the Amazon re: Invent conference – AWS RoboMaker, Fully Managed SFTP Service for Amazon S3, and much more! 

It has been a big year for GitHub. The code sharing platform has this year celebrated its 10th birthday, been bought by Microsoft for an impressive $7.5 billion, and has now reached an astonishing 100 million repositories. While there will be rumblings of discontent following the huge Microsoft acquisition, it doesn't look like threats to leave GitHub have come to fruition. True, it has only been a matter of weeks since Microsoft finally took over, but there are no signs that GitHub is losing favor with developers. 1 in 3 of all GitHub repositories were created in 2018 According to GitHub, 1 in 3 of the 100 million repositories were created in 2018. That demonstrates the astonishing growth of the platform, and just how embedded it is within the day to day life of software engineers. This is further underlined by more data in GitHub's Octoverse report, published in October. "We've seen more new accounts in 2018 so far than in the first six years of GitHub combined," the report states. Perhaps the new relationship with Microsoft has actually helped push GitHub from strength to strength - MicrosoftDocs/azure-docs is the fastest growing repository in 2018. Of course, some credit should probably go to Microsoft as well - the organization has done a lot to change its image and ethos, becoming much more friendly towards open source software. Meanwhile, at Packt, we've been delighted to play a small part in helping GitHub get to its 100 million milestone. Earlier this year we hit 2,000 project repos.

Google Cloud announced its recent alliance with Unity at the Unite Berlin conference this week. Unity is a popular game development platform for a real-time 3D game and content creation. Google Cloud stated that they are building a suite of managed services and tools for creating connected games. This suite will be much focussed on real-time multiplayer experiences. With this Google Cloud becomes the default cloud provider helping developers build connected games using Unity. It will also assist them to easily build and scale their games. Additionally, developers will get an advantage of Google Cloud right from the Unity development environment without needing to become cloud experts. The reason Google Cloud collaborates with Unity is to create an open source for connecting players in multiplayer games. This project mainly aims at creating an open source, community-driven solutions built in collaboration with the world’s leading game companies. Unity will also be migrating all of the core infrastructure powering its services and offerings to Google Cloud. Unity will also be running its business on the same cloud that Unity game developers will develop, test and globally launch their games. John Riccitiello, Chief Executive Officer, Unity Technologies, said, “Migrating our infrastructure to Google Cloud was a decision based on the company’s impressive global reach and product quality. Now, Unity developers will be able to take advantage of the unparalleled capabilities to support their cloud needs on a global scale.” Google Cloud plans to release new products and features over the coming months. Keep yourself updated on this alliance by checking out Unity’s homepage. AI for Unity game developers: How to emulate real-world senses in your NPC agent behavior Google announces Cloud TPUs on the Cloud Machine Learning Engine (ML Engine) Unity 2D & 3D game kits simplify Unity game development for beginners

At Ceph Day Berlin, yesterday (November 12)  the Linux Foundation announced the launch of the Ceph Foundation. A total of 31 organizations have come together to launch the Ceph Foundation including industries like ARM, Intel, Harvard and many more. The foundation aims to bring industry members together to support the Ceph open source community. What is Ceph? Ceph is an open source distributed storage technology that provides storage services for many of the world’s largest container and OpenStack deployments. The range of organizations using Ceph is vast. They include financial institutions like Bloomberg and Fidelity, cloud service providers like Rackspace and Linode, car manufacturers like BMW, and software firms like SAP and Salesforce. The main aim of the Ceph Foundation The main focus of the foundation is to raise money via annual membership fees from industry members. The combined pool of funds will then be spent in support of the Ceph community. The team has already raised around half a million dollars for their first year which will be used to support the Ceph project infrastructure, cloud infrastructure services, internships, and community events. The new foundation will provide a forum for community members and industry stakeholders to meet and discuss project status, development and promotional activities, community events, and strategic direction. The Ceph Foundation replaces the Ceph Advisory Board formed back in 2015. According to a Linux Foundation statement, the Ceph Foundation, will “organize and distribute financial contributions in a coordinated, vendor-neutral fashion for immediate community benefit” Ceph has an ambitious plan for new initiatives once the foundation gets properly functional. Some of these include: Expansion of and improvements to the hardware lab used to develop and test Ceph An events team to help plan various programs and targeted regional or local events Investment in strategic integrations with other projects and ecosystems Programs around interoperability between Ceph-based products and services Internships, training materials, and much more! The Ceph Foundation will provide an open, collaborative, and neutral home for project stakeholders to coordinate their development and community investments in the Ceph ecosystem. You can head over to their blog to know more about this news. Facebook’s GraphQL moved to a new GraphQL Foundation, backed by The Linux Foundation NIPS Foundation decides against name change as poll finds it an unpopular superficial move; instead increases ‘focus on diversity and inclusivity initiatives’ Node.js and JS Foundation announce intent to merge; developers have mixed feelings

Verizon Communications Inc. recently announced that it is migrating about 1000 of its business-critical applications and database back-end systems to the popular cloud provider, Amazon Web Services (AWS). Verizon had bought Terramark, a cloud and service provider, in 2011 as part of its public and private cloud strategy. This strategy included building its own cloud that offered infrastructure-as-a-service to its customers. AWS has stayed ahead of competition, where it offered added services to its customers. On the other hand, Verizon could not stay in the race for longer as it was usurped by Microsoft and Google. Due to this, two years ago, in 2016, Verizon closed down its public cloud offering and then sold off its cloud and managed hosting service assets to IBM and also sold a number of data centres to Equinix. Verizon had first started working with AWS in 2015 and has many business and consumer applications already running in the cloud. The current migrations to AWS is part of Verizon’s corporate-wide initiative, which is, to increase agility and reduce costs through the use of cloud computing. Some benefits of this migration include: With the help of AWS, Verizon will enable it to access more comprehensive set of cloud capabilities. This will ensure that its developers are able to invent on behalf of its customers. Verizon has built AWS-specific training facilities where its employees can quickly update themselves on the AWS technologies and learn how to innovate with speed and at scale. AWS enables Verizon to quickly deliver the best, most efficient customer experiences. Verizon also aims to make the public cloud a core part of its digital transformation, upgrading its database management approach to replace its proprietary solutions with Amazon Aurora To know more about AWS and Verizon’s partnership, read the AWS blog post. Linux Foundation launches the Acumos Al Project to make AI accessible Analyzing CloudTrail Logs using Amazon Elasticsearch How to create your own AWS CloudTrail

Red Hat recently announced that its latest enterprise distribution, Red Hat Enterprise linux version 7.5 (RHEL 7.5) is now generally available. This release aims at simplifying hybrid computing. The RHEL 7.5 is packed with multiple features for the server administrators and developers. New features in the RHEL 7.5 RHEL 7.5 provides support for Network Bound Disk Encrypted (NBDE) devices, new Red Hat cluster management capabilities, and compliance management features. Enhancements to the cockpit administrator console. Cockpit provides a simplified web interface to help eliminate complexities around Linux system administration. This makes it easier for new administrators, or administrators moving over from non-Linux systems, to better understand the health and status of their operations. Helps cut down storage costs by providing improved compliance controls and security, enhanced usability, and tools to cut down storage costs. Better Integration with Microsoft Windows infrastructure both in Microsoft Azure and on-premise. This includes improved management and communication with Windows Server, more secure data transfers with Azure, and performance improvements when used within Active Directory architectures. If one wishes to use both RHEL and Windows for their network, RHEL 7.5 serves this purpose. Delivers improved software security controls to alleviate risk while also augmenting IT operations. A significant component of these controls is security automation via the integration of OpenSCAP with Red Hat Ansible Automation. This is aimed at facilitating the development of Ansible Playbooks straight from OpenSCAP scans which, in turn, can be leveraged to execute remediations more consistently and quickly across a hybrid IT environment. Provides high availability support for enterprise applications running on Amazon Web Services or Microsoft Azure with Pacemaker support in public clouds via the Red Hat High Availability Add-On and Red Hat Enterprise Linux for SAP® Solutions. To know more about this release in detail read Red Hat official blog. Linux Foundation launches the Acumos Al Project to make AI accessible How to implement In-Memory OLTP on SQL Server in Linux Kali Linux2 released    

GitLab recently announced the release of GitLab 11.0 which includes major features such as the Auto DevOps and License Management; among other features. The Auto DevOps feature is generally available in GitLab 11.0. It is a pre-built, fully featured CI/CD pipeline that automates the entire delivery process. With this feature, one has to simply commit their code and Auto DevOps does the rest. This includes tasks such as building and testing the app; performing code quality, security, and license scans. One can also package, deploy and monitor their applications using Auto DevOps. Chris Hill, head of systems engineering for infotainment at Jaguar Land Rover, said, “We’re excited about Auto DevOps, because it will allow us to focus on writing code and business value. GitLab can then handle the rest; automatically building, testing, deploying, and even monitoring our application.” License Management automatically detects licenses of project's dependencies such as, Enhanced Security Testing of code, containers, and dependencies: GitLab 11.0 has an extended coverage of Static Analysis Security Testing (SAST) and  includes Scala and .Net. Kubernetes integration features: If one needs to debug or check on a pod, they can do so by reviewing the Kubernetes pod logs directly from GitLab's deployment board. Improved Web IDE:  One can view their CI/CD pipelines from the IDE and get immediate feedback if a pipeline fails. Switching tasks can be disruptive, so the updated Web IDE makes it easy to quickly switch to the next merge request, to create, improve, or review without leaving the Web IDE. Enhanced Epic and Roadmap views : GitLab 11.0 has an updated Epic/Roadmap navigation interface to make it easier to see the big images and make planning easier. Read more about GitLab 11.0 on its GitLab’s official website. GitLab’s new DevOps solution GitLab open sources its Web IDE in GitLab 10.7 The Microsoft-GitHub deal has set into motion an exodus of GitHub projects to GitLab