Tech News

Netflix in their tech blog announced that their popular cloud gateway Zuul 2 is now open-source. Zuul 2 was announced back in 2016 is Netflix's Java-based API gateway that handles all the request for Netflix's user base. Zuul 2 is the front door, acting as a filter to any request that comes into the Netflix's server. This gateway monitors the request and routes the request to the appropriate service to then act on the request. Zuul, in a way, is responsible for keeping Netflix standing strong and fulfilling your streaming requests. Netflix is known for open sourcing a lot of the tools developed in-house for the community. Zuul 2 is a battle-tested tool as it has been handling the massive Netflix infrastructure. Since its open sourcing, the developers have an option of a more resilient tool that can be used in their infrastructure architecture. Netflix promises to keep the security aspect intact for the open source Zuul 2. Also to add to this news, Netflix announced some more features for Zuul 2. Here are the feature additions: Server protocols: Zuul 2 has full support for HTTP/2 connections. Also, Mutual TLS will enhance Zuul's operation in secure infrastructure. Resiliency features: To increase the availability, Netflix will be adding a feature called Adaptive Retries that is used on Netflix. Also, it would be adding configurable concurrency limits for protecting the origins from getting overloaded and separating the other origins that run behind Zuul. Request Passport: This feature will enable the Zuul server to track all events that occur for each request. This will allow you to compute the asynchronous requests for better availability of your services. Status Categories: This feature helps you categorize the requests by extending the success and failure state in terms of HTTP status code. Request attempts: It tracks all the proxy attempts and provides you the status of each attempt. This really helps to identify the retries and to debug the routing. Zuul also has enhanced self-service routing, load balancing, anomaly detection, among other primary features that Netflix uses to keep the infrastructure secure and running. Netflix has released several other tools including Titus (container management), Conductor (microservice orchestration), Hystrix (cloud management), Vizceral (traffic management), among other efficient tools that can be used in large infrastructures. You can read Netflix's announcement blog to get more insights on the future development in Zuul 2. What software stack does Netflix use?

Red Hat released its 13th version of OpenStack cloud platform i.e Queens. OpenStack usually follows a rapid six-month release cycle. This release was majorly focussed upon using open-source OpenStack to bridge the gap between private and public cloud. RHOP will be generally available in June through the Red Hat customer portal and as a part of both Red Hat infrastructure and cloud suite. Red Hat’s general manager of OpenStack said “RHOP 13 is the first complete containerized OpenStack. Our customers have been asking us to make it easy to run Red Hat OpenShift Container Platform (RHOCP), Red Hat's Kubernete's offering. We want to make this as seamless as possible” OpenStack has come with very interesting cross-portfolio support, to accelerate their hybrid cloud offering. This includes: Red Hat CloudForms which help in managing day-to-day tasks in Hybrid Infrastructure. Red Hat Ceph storage, a scalable storage solution which enables provisioning of hundreds of virtual machines from a single snapshot to build a massive storage solution Red Hat OpenShift container platform which enables running of cloud-native workloads with ease. OpenShift architecture supports running of both Linux as well as Kubernetes containers on a single workload. RHOP 13 also comes with a varied set of feature enhancements and upgrades, like: Containerization capabilities OpenStack 13 is building upon the containerization capabilities and services introduced with the release of OpenStack 12. It enables containerization of all the services including networking and storage. Security capabilities By the inclusion of OpenStack Barbican, RHOP 13 comes up with tenant-level lifecycle for sensitive data protection such as passwords, security certificates and keys. With the introduction of features in Barbican, encryption-based services are available to extensive data protection. For official release notes, please refer to the official OpenStack blog. Introducing OpenStack Foundation’s Kata Containers 1.0 About the Certified OpenStack Administrator Exam OpenStack Networking in a Nutshell

Qualcomm Inc announced a new chipset namely Snapdragon XR1, its first dedicated Extended Reality (XR) platform, to power standalone virtual reality (VR) and augmented reality (AR) headsets. This is Qualcomm’s attempt to expand its business beyond the realms of smartphones. The newly dedicated chipset got introduced at the Augmented World Expo in Santa Clara, California today. The AR/VR industry seems to have developed quite an interest in building standalone headsets recently. With standalone headsets such as Facebook’s Oculus Go and Google Daydream powered Lenovo Mirage Solo ruling the market, it is quite evident that the need for powerful chipsets to power these devices is only going to rise. Snapdragon XR1, the first ever dedicated XR platform Snapdragon XR1 Key Features: Let’s look at the features that the makes the new chipset quite a powerful device to have for the standalone headsets: XR1 is a system-on-a-chip (SoC). It has all the required electronic circuitry and smartphone parts on a single integrated circuit (IC). The chip includes ARM-based CPU, a GPU, a vector processor and AI engine. AI engine will be able to optimize different AI functions such as object recognition and pose prediction on the device. With this chip, Head-tracking interaction with headsets will also be possible. It will also be capable of handling voice control. It enables better user experience with high-quality visual and audio playback, as well as 3-DoF and 6-DoF interactive controls.  The XR1 will provide support for 4K video at up to 60 frames per second, dual displays, 3D overlays and popular graphics such as APIs OpenGL, OpenCL, and Vulkan. The chipset consists of Spectra image signal processor which will help reduce noise for clearer image quality. Currently, Qualcomm's audio technologies like Aqstic, 3D Audio Suite, and aptX are being used by XR1 which enables high-fi sound. It also uses Aqstic's always-on and always-listening assistance. There will also be a system, namely, Head Related Transfer Functions which will give an impression of sounds coming from a specific point in space. This will create a more realistic experience. It will be able to delegate tasks to various different cores for more efficient performance by using heterogeneous computing. Qualcomm’s goal with this chip design is an effort to make it easy for the AR/VR hardware manufacturers to design and build headsets which are cheap yet powerful and energy-efficient. The XR1 consists of an SDK which helps manufacturers to implement some of these features, as well as Bluetooth and WiFi capabilities. The famous Oculus Go makes use of Qualcomm smartphone chip and Lenovo Mirage solo also uses Qualcomm phone processors but the battery life of the standalone headsets is not comparable to that of a smart-phone. Now, with chipsets being built specifically for these headsets, the battery life would improve considerably. Qualcomm is not the only one working on manufacturing chips dedicated to headsets, others are aiming at similar technologies too. Apple is working on developing its own chip for the AR glasses which will be on sale in early 2020. Nvidia and Intel are among the others that want to join the game. It is also worth noting that Qualcomm is on the lookout for new sources of revenue as the smartphone industry is drying up and competition is continually increasing. Qualcomm will team up with other existing headset makers that plan to include the chip such as HTC ( Vive ), Vuzix, Meta, and Pico. With Qualcomm unveiling the new Snapdragon XR1 at the Augmented World Expo today, the AR/VR manufacturers across the globe have received an extra boost in terms of shipping hardware for the AR/VR space. For more details on Snapdragon XR1, check out the official Qualcomm press release. Top 7 modern Virtual Reality hardware systems Types of Augmented Reality targets Google open sources Seurat to bring high precision graphics to Mobile VR  

ROS is nothing but a middleware with a set of tools and software frameworks for building and stimulating robots. ROS follows a stable release cycle, coming with a new version every year on 23rd of May. ROS released its Melodic Morenia version this year on the said date, with a decent number of enhancements and upgrades. Following are the release notes: class_loader header deprecation class_loader’s headers has been renamed and the previous ones have been deprecated in an effort to bring them close to multi-platform support and its ROS 2 counterpart. You can refer to the migration script provided for the header replacements and PRs will be released for all the .packages in previous ROS distribution. Kdl_parser package enhancement Kdl_parser has now deprecated a method that was linked with tinyxml (which was already deprecated) The tinyxml replacement code is as follows: bool treeFromXml(const tinyxml2::XMLDocument * xml_doc, KDL::Tree & tree) The deprecated API will be removed in N-turle. OpenCV version update For standardization reason, the OpenCV usage version is restricted to 3.2. Enhancements in pluginlib Similar to class_loader, the headers were deprecated here as well, to bring them closer to multi-platform support. plugin_tool which was deprecated for years, has been finally removed in this version. For more updates on the packages of ROS, refer to ROS Wiki page.

Last Friday, the MariaDB Foundation officially announced the general availability of its popular database MariaDB with a newer stable version 10.3.7. This release is considered to be a major and substantial release within 10.3 series of release. MariaDB is fast, scalable and robust with a rich ecosystem of storage engines and plugins for a wide variety of use cases across banks, social sites, ecommerce and many more. Improvement Highlights MyRocks Storage Engine 1.0 now Stable for MariaDB 10.3.7 with high compression ratio. Spider Storage Engine 3.3.13 now Stable, supporting partitioning and XA Transactions. Added two new algorithm options, INSTANT and NOCOPY for operations of data modification and rebuilding clustered index respectively. SSL support for embedded server library when connecting to remote servers. Added new status variables namely, feature_json and feature_system_versioning for monitoring JSON functionality usage and system versioning respectively. Removed InnoDB version number 5.7 in MariaDB 10.3 and onwards. Bugs fixed for ADD COLUMN. Improved ALTER TABLE algorithms along with  ALGORITHM=INSTANT and ALGORITHM=NOCOPY. Various performance fixes and code cleanup, including Clean up InnoDB parameter validation Fixed bug that caused the system to hang while shutting down InnoDB. Performance improved in FLUSH TABLES…FOR EXPORT causing no hang. No more support to Debian 7 Wheezy and Fedora 26 in future releases. Users need to update their OS with either Debian 8 “Jessie” or Fedora 27 and onwards. With these added features and performance improvements, MariaDB developers are equipped now to churn out their data into better structured information. Please refer to the release notes and changelog for more details. MySQL 8.0 is generally available with added features Why Oracle is losing the Database Race Neo4j 3.4 aims to make connected data even more accessible

Here is this week's newsletter showcasing a select set of news stories, interviews, analyses and tutorials along with special content curated from the web. If you like what you see, make sure to subscribe and tell others too. Here’s what you may have missed in the last 7 days – Tech news, insights and tutorials… Featured interview “Tableau is the most powerful and secure end-to-end analytics platform” - Joshua Milligan This week we interviewed Joshua Milligan, Tableau Zen Master and the best selling author of the book, Learning Tableau. In this insightful interview, Joshua explains why Tableau is the Google of data visualization. He explores what's possible in Tableau like Geospatial analysis and automating workflows, what the exciting new features such as Hyper, Tableau Prep mean for BI developers, among other topics. Tech news Conferences/Events in focus this week What the PSF & Jetbrains 2017 Python Developer Survey reveals Five developer centric sessions at IoT World 2018 F8 AR Announcements What we learned at the ICRA 2018 conference for robotics & automation Data news in depth Amazon is selling facial recognition technology to police PostgreSQL 11 Beta 1 is out! Google announces Cloud TPUs on the Cloud Machine Learning Engine (ML Engine) TensorFlow.js 0.11.1 releases! Development & programming news in depth Microsoft introduces SharePoint Spaces, adds virtual reality support to SharePoint Qt 5.11 has arrived! Adobe is going to acquire Magento for $1.68 Billion Typescript 2.9 release candidate is here CRYENGINE 5.5 preview 3 goes live! Cloud & networking news in depth Kubernetes Containerd 1.1 Integration is now generally available Introducing OpenStack Foundation’s Kata Containers 1.0 Other News What matters on an engineering resume? Hacker Rank report says skills, not certifications Partnership alliances of Kontakt.io and IOTA Foundation for IoT and Blockchain Tutorials Data tutorials How to use M functions within Microsoft Power BI for querying data Development & programming tutorials Development tutorials Testing Single Page Applications (SPAs) using Vue.js developer tools How to Scaffold a New module in Odoo 11 Build powerful progressive web apps with Firebase How to integrate Firebase with NativeScript for cross-platform app development Build an ARCore app with Unity from scratch Working with Unity Variables to script powerful Unity 2017 games How to use Bootstrap grid system for responsive website design? Programming tutorials Documenting RESTful Java Web Services using Swagger Regular expressions in AWK programming: What, Why, and How Other tutorials How to create and configure an Azure Virtual Machine Getting started with Digital forensics using Autopsy How to build an Arduino based ‘follow me’ drone This week’s opinions, analysis, and insights Data Insights Top 7 libraries for geospatial analysis Introducing Dask: The library that makes scalable analytics in Python easier Facebook’s Wit.ai: Why we need yet another chatbot development framework? Top 5 tools for reinforcement learning Development & Programming Insights Is web development dying? Top 5 open source static site generators AI for game developers: 7 ways AI can take your game to the next level   Other Insights Abandoning Agile SDLC puts process at the center of software engineering ERP tool in focus: Odoo 11 12 common malware types you should know Anatomy of a Crypto Ransomware What else happened in May? Last Week on Packt Hub - 18 May 2018 That week on Packt Hub – 11 May 2018 That week on Packt Hub – 4 May 2018  

This year’s ICRA 2018 conference features interactive sessions, keynotes, exhibitions, workshops, and much more. Following are some of the interesting keynotes on machine learning, robotics, and more. Note: International Conference on Robotics and Automation (ICRA) is an international forum for robotics researchers to represent their work. It is a flagship conference of IEEE Robotics and Automation Society. This conference held at the Brisbane Convention and Exhibition Center from the 21st to 25th May, 2018  brings together experts in the field of robotics and automation. The conference includes delegates in the frontier of science and technology in robotics and automation. Implementing machine learning for safe, high-performance control of mobile robots Traditional algorithms are designed based on their a-priori knowledge leveraged from the system and its environment. This knowledge also includes system dynamics and an environment map. Such an approach can allow system to work successfully in a predictable environment. However, if the system is unaware of the environment details, it may lead to high performance losses. In order to build systems that can work efficiently in unknown and uncertain instances, the speaker, Prof. Angela Schoellig, introduces systems that are capable of learning amidst an operation and adapt the behaviour accordingly. Angela presents several approaches for online, data-efficient, and safety-guaranteed learning for robot control. In these approaches, the algorithms can: Leverage insights from control theory Make use of neural networks and Gaussian processes, which are state-of-the-art and probabilistic learning methods. Take into account any prior knowledge about system dynamics. The speaker has also demonstrated how using such novel robot control and learning algorithms can be safe and effective in real-world scenarios. You can check Angela Schoellig’s video below on how she demonstrated these algorithms on self-flying and -driving vehicles, and mobile manipulators. Meta-learning and the art of Learning to Learn Pieter Abbeel, in his talk about meta-learning (learning to learn) explains how reinforcement learning and imitation learning have been successful in various domains such as Atari, Go, and so on. You can also check out 6 Key Challenges in Deep Learning for Robotics by Pieter Abbeel presented at the NIPS 2017 conference. Humans have a default potential to learn from past experiences and can learn new skills far more quickly than machines. Pieter explains some of his recent experiments on meta-learning, where agents learn imitation or the reinforcement learning algorithms and using the algorithms as base can learn from past instances just like humans. Due to meta learning, machines can now acquire any skill just by having a single demonstration or few trials. He states that meta-learning can be applied to general examples such as omniglot and mini-imagenet, which are standard few-shot classification benchmarks. To know about meta-learning from the ground up, you can check out our article, What is Meta Learning?. You can also read our coverage on Pieter Abbeel’s accepted paper at the ICLR 2018. Robo-peers: Robust Interaction in Human-Robot Teams Richard Vaughan in this keynote explains how robots would behave in natural surroundings, i.e among humans, animals, and other peer robots. His team has worked on behaviour strategies for mobile robots. These strategies enable the robots to have sensing capabilities and also allow them to behave sophisticated like humans and have robust interactions with the world and other agents around them. Richard further described certain series of vision-mediated Human-Robot Interactions conducted within groups of driving and flying robots. The mechanisms used were simple but highly effective. Form Building Robots to Bridging the Gap between Robotics and AI Robots posses smart, reactive and user-centered programming systems using which they can physically interact with the world. In current scenarios, every layman is capable of using cutting-edge robotics technology for complex tasks such as force-sensitive assembly and safe physical human-robot interaction. Franka Emika’s Panda, the first commercial robot system, is an example of of a robot with such abilities. Sami Haddadin, in this talk offers to bridge the gap between model-based nonlinear control algorithms and data-driven machine learning via a holistic approach. He explains that neither pure control-based nor end-to-end learning algorithms are a close match to human-level general purpose machine intelligence. Two recent results reinforce this statement: i.) Learning of exact articulated robot dynamics by using the concept of first order principle networks. ii.) Learning human-like manipulation skills by combining adaptive impedance control and meta learning Panda was, right from the beginning, released with consistent research interfaces and modules to enable the robotics and AI community to build on the developments in the field until then and to push the boundaries in manipulation, interaction and general AI-enhanced robotics. Sami believes this step will positively enable the community to address the immense challenges in robotics and AI research. Socially Assistive Robots: The Next-Gen Healthcare Helpers Goldie Nejat puts down her concern by stating that the world’s elderly population is rising and so is dementia, a disease with hardly any cure. She says that robots here, can become a unique strategic technology. She further adds that they can become a crucial part of the society by helping the aged population in their day-to-day activities. In this talk she presents intelligent assistive robots, which can be used to improve the life of the older populations. The population also includes those suffering from dementia. She discusses how the assistive robots, Brian, Casper, and Tangy socially have been designed to autonomously provide cognitive and social interventions. These robots also help with activities of daily living, and lead group recreational activities in human-centered environments. These robots can serve as assistants to individuals as well as groups of users. They can personalize their interactions as per the needs of the users. These robots can also be integrated into everyday lives of other people outside the aged bracket. Read more about the other keynotes and highlights on robotics on the ICRA’s official website How to build an Arduino based ‘follow me’ drone AI powered Robotics : Autonomous machines in the making Tips and tricks for troubleshooting and flying drones safely

After 400+ improvements, the latest CRYENGINE 5.5 preview 3 is live. Typically, CRYENGINE follows three preview cycles before the stable release. The latest, preview 3, is packed with code interface changes, improvements to animations and graphics, changes to the AI and core system along with other additional fixes. The developer team at CRYENGINE has worked hard on the previews released earlier to ensure improved efficiency for the fellow game developers across the globe. The feedback provided by the CRYENGINE community played a pivotal role in this process. Here’s a quick rundown of all the new major features and updates in CRYENGINE 5.5 preview 3: Code Interface changes Following are the changes made to the code interface: The new CryGamePlatform plugin (wrapping Steam and PSN APIs) replaces CryLobby. The r_WindowType has replaced the r_Fullscreen and  r_FullscreenWindow CVars. The Windows key has been enabled in the engine by default. You have to use g_disableWinKeys=1 to return to prior behavior. The IID function is no longer needed by the Entity Components. The ICryPlugin interface has been renamed to IEnginePlugin and moved to the Cry namespace. There is no need for the plugins to implement the GetName and GetCategory functions. This is because the default implementations have been added. CryAction, which is a game framework is currently being removed. Its vital parts will move onto the other parts of the engine. Get the full information at the CRYENGINE code interface changes documentation. Animation and Graphics improvements Animation Attachments such as a pistol, shotgun etc, were wrongly attached in cases where compute skinning is used. This is also resolved. Race-condition crash in PoseAlignerChain when using ground alignment IK has been fixed. Graphics A new Profile section has been added which helps detect GetOrCreateInputLayout's shader reflection request. A new annotating HLSL shader is added in Vulkan that includes resource layout descriptors. HLSLcc local shader compilation is another newly added feature in Vulkan. Transformation/rotation issues for objects with sub-objects have been fixed. New screen fill stats are added. Now there are Enhanced grouping capabilities in new ActivateRandom feature. This supports all possibilities of old SecondGen features. AI system and Core/System changes AI system Islands connectivity, a brand new feature added, takes navigation annotations into account. Registering smart object as an off-mesh link that doesn’t work after creating a new level has also been fixed. Core New CryAPIExamples module is introduced to compile Doxygen snippets. Simple IEntityAudioComponent examples have been added. Plugin load from disk if shared libraries are unsupported has been disabled. This helps fix the PS4 startup issue. Preview drawing is added to the Physic Constraint Components. Apart from these changes and improvements, there have also been additional fixes. CVar is added to draw full extent of last cached shadow cascade, an unused ColorGrading technique is removed that was giving compiler errors, etc. You can access the 5.5.0 Preview 3 Release through the CRYENGINE launcher and GitHub. These are just a few selected updates to the CRYENGINE 5.5 preview 3. The full list of over 400+ changes, updates, and other known issues is available on the CRYENGINE release notes documentation. What’s new in Unreal Engine 4.19? Put your game face on! Unity 2018.1 is now available Working with Unity Variables to script powerful Unity 2017 games  

PostgreSQL team announces the first beta release of PostgreSQL 11 which contains sneak peek into all the features that will be available in the release candidate of PostgreSQL 11 which is likely to be released in late 2018. The major features are centered around database simplicity, handling large datasets, and various performance bottlenecks. We can expect some minor changes before the final release is out. Since it is still in beta release, it is strongly advised you don't run them in the production environment to avoid any hassle. PostgreSQL is an open source relational database management system which has grown in popularity over the years. With the constant development of more than 30 years, PostgreSQL is one of the popular database used today. It has been called the DBMS of 2017 because of its powerful database management system that offers better reliability, robustness, and performance measures. Some of the noteworthy changes available in PostgreSQL 11 Beta 1: Partitioning improvements Partitioning plays an integral part in splitting a large dataset into smaller pieces in order to carry out complex operations with ease. PostgreSQL 11 contains several new features and improvements to working with data in partitions: New feature, hash partitioning, allows you to partition using a hash key You can now use UPDATE statements to a partition key in order to move the affected rows to the appropriate partitions PostgreSQL 11 supports enhanced partition elimination during query processing and execution thus leading to improved SELECT query performance Complete support for PRIMARY KEY, FOREIGN KEY, triggers, and indexes on partitions A new feature has been added which allows the query to distribute grouping and aggregation to partitioned tables before the final aggregation. However, in order to enable the settings, you need to set enable_partitionwise_aggregate = on in your configuration file, since it is disabled by default. Parallelism improvements New features have been added to build a smooth parallel query infrastructure to manage and carry out workloads efficiently and effectively thus providing significant performance enhancements. We now have parallelized hash joins and CREATE INDEX for B-tree indexes We can use parallelized features on certain queries with UNION SQL stored procedures A new feature SQL stored procedures is introduced by the PostgreSQL team which allows users to use embedded transactions such as BEGIN, COMMIT/ROLLBACK and more within a procedure. Just-In-Time compilation Now you can optimize the execution of code, and operations; and even make required changes during the run time. Thus it stands out as a perfect framework which gives you a leeway to allow future optimizations in the workflow. In case you are building PostgreSQL 11 from source, you can enable JIT compilation using the --with-llvm flag. Window functions In PostgreSQL 11, window functions will support all options in SQL:2011 standard SCRAM authentication PostgreSQL 11 supports channel binding for SCRAM authentication, thus providing the required security feature to prevent man-in-the-middle attacks. PostgreSQL team has upgraded this feature since SCRAM authentication was already available. This was used to improve the storage and transmission of passwords on the basis of standard protocol. Simplicity and user experience improvements Although PostgreSQL provides a healthy set of features, not all of them can be easy-to-use in development and production environments. The PostgreSQL team has therefore brought many new improvements to better the user experience. Now you can quit the PostgreSQL command-line (psql) using keywords like quit and exit. Additional improvements and features Many other new improvements and features have been added to the PostgreSQL 11. You can refer the release notes for complete list of new and changed features in the roadmap. If you want to contribute to the project and want to test this new release in order to find bugs and issues, download PostgreSQL 11 Beta 1, from their official page. You can find existing open issues in the PostgreSQL wiki. In case you want to report any bugs or issues you can use report bugs form available on the PostgreSQL website. How to perform data partitioning in PostgreSQL 10 New updates to Microsoft Azure services for SQL Server, MySQL, and PostgreSQL 2018 is the year of graph databases. Here’s why

After just 6 months of releasing the alpha version of Kubernetes containerd integration, the community has declared that the upgraded containerd 1.1 is now generally available. Containerd 1.1 can be used as the container runtime for production Kubernetes clusters. It works well with Kubernetes 1.10 and also supports all Kubernetes features. Let’s look at the key upgrades in the new Kubernetes Containerd 1.1 : Architecture upgrade Containerd 1.1 architecture with the CRI plugin In the current version 1.1, the cri-containerd daemon is changed to a containerd CRI plugin. This CRI plugin is made default and is built-in containerd 1.1. It interacts with containerd through direct function calls. Kubernetes can now be used by containerd directly as this new architecture makes the integration more stable and efficient, and eliminates another grpc hop in the stack. Thus, the cri-containerd daemon is no longer needed. Performance upgrades Performance optimizations have been the major focus in the Containerd 1.1. Performance was optimized in terms of pod startup latency and daemon resource usage which are discussed in detail below. Pod Startup Latency The containerd 1.1 integration has lower pod startup latency than Docker 18.03 CE integration with dockershim. Following graph is based on the results from the ‘105 pod batch startup benchmark’ (The lower, the better) Pod Startup Latency Graph CPU and Memory Usage The containerd 1.1 integration consumes less CPU and memory overall compared to Docker 18.03 CE integration with dockershim at a steady state with 105 pods. The results differ as per the number of pods running on the node. 105 is the current default for the max number of user pods per node. CPU Usage Graph Memory Usage Graph On comparing Docker 18.03 CE integration with dockershim, the containerd 1.1 integration has 30.89% lower kubelet cpu usage, 68.13% lower container runtime cpu usage, 11.30% lower kubelet resident set size (RSS) memory usage,  and 12.78% lower container runtime RSS memory usage. What would happen to Docker Engine? Switching to containerd would not mean that one will be unable to use Docker Engine. The fact is that Docker Engine is built on top of containerd. The next release of Docker Community Edition (Docker CE) will allow using containerd version 1.1. Docker engine built over Containerd Containerd is being used by both Kubelet and Docker Engine. This means users choosing the containerd integration will not only get new Kubernetes features, performance, and stability improvements, but also have the option of keeping Docker Engine around for other use cases. Read more interesting details on the Containerd 1.1 on Kubernetes official blog post. Top 7 DevOps tools in 2018 Everything you need to know about Jenkins X, the new cloud native CI/CD solution on Kubernetes What’s new in Docker Enterprise Edition 2.0?

Cloud mining has become into one of the biggest trends in Bitcoin and cryptocurrency. The reason is simple: it makes mining Bitcoin incredibly easy. By using cloud, rather than hardware to mine bitcoin, you can avoid the stress and inconvenience of managing hardware. Instead of using the processing power from hardware, you share the processing power of the cloud space (or more specifically the remote data center). In theory, cloud mining should be much more profitable than mining with your own hardware. However, it's easy to be caught out. At best some schemes are useless - at worst, they could be seen as a bit of a pyramid scheme. For this reason, it's essential you do your homework. However, although there are some risks associated with cloud mining, it does have benefits. Arguably it makes Bitcoin, and cryptocurrency in general, more accessible to ordinary people. Provided people get to know the area, what works and what definitely doesn't it could be a positive opportunity for many people. How to start cloud mining Let's first take a look at different methods of cloud mining. If you're going to do it properly, it's worth taking some time to consider your options. At a top level there are 3 different types of cloud mining. Renting out your hashing power This is the most common form of cloud mining. To do this, you simple 'rent out' a certain amount of your computer's hashing power. In case you don't know, hashing power is essentially your hardware's processing power; it's what allows your computer to use and run algorithms. Hosted mining As the name suggests, this is where you simply use an external machine to mine Bitcoin. To do this, you'll have to sign up with a cloud mining provider. If you do this, you'll need to be clear on their terms and conditions, and take care when calculating profitability. Virtual hosted mining Virtual hosted mining is a hybrid approach to cloud mining. To do this, you use a personal virtual server and then install the required software. This approach can be a little more fun, especially if you want to be able to build your own Bitcoin mining set up, but of course this poses challenges too. Depending on what you want to achieve any of these options may be right for you. Which cloud mining provider should you choose? As you'd expect from a trend that's growing rapidly, there's a huge number of cloud mining providers out there that you can use. The downside is that there are plenty of dubious providers that aren't going to be profitable for you. For this reason, it's best to do your research and read what others have to say. One of the most popular cloud mining providers is Hashflare. With Hashflare, you can buy a number of different types of cryptocurrencies, including Bitcoin, Ethereum, and Litecoin. You can also select your 'mining pool', which is something many providers won't let you do. Controlling the profitability of cloud mining can be difficult, so having control over your mining pool could be important. A mining pool is a bit like a hedge fund - a group of people pool together their processing resources, and the 'pay out' will be split according to the amount of work put in in order to create what's called a 'block', which is essentially a record or ledger of transactions. Hashflare isn't the only cloud mining solution available. Genesis Mining is another very high profile provider. It's incredibly accessible - you can begin a Bitcoin mining contract for just $15.99. Of course, the more you invest the better the deal you'll get. For a detailed exploration and comparison of cloud mining solutions, this TechRadar article is very useful. Take a look before you make any decisions! How can I ensure cloud mining is profitable? It's impossible to ensure profitability. Remember - cloud mining providers are out to make a profit. Although you might well make a profit, it's not necessarily in their interests to be paying money out to you. Calculating cloud mining profitability can be immensely complex. To do it properly you need to be clear on all the elements that are going to impact profitability. This includes: The cryptocurrency you are mining How much mining will cost per unit of hashing power The growth rate of block difficulty How the network hashrate might increase over the length of your mining contract There are lots of mining calculators out there that you can use to calculate how profitable cloud mining is likely to be. This article is particularly good at outlining how you can go about calculating cloud mining profitability. Its conclusion is an interesting take that's worth considering if you are interested in starting cloud mining: is "it profitable because the underlying cryptocurrency went up, or because the mining itself was profitable?" As the writer points out, if it is the cryptocurrency's value, then you might just be better off buying the cryptocurrency. Read next A brief history of Blockchain Write your first Blockchain: Learning Solidity Programming in 15 minutes “The Blockchain to Fix All Blockchains” – Overledger, the meta blockchain, will connect all existing blockchains

Microsoft has brought its Sharepoint software to Virtual reality headsets. This new technology, called SharePoint Spaces will allow SharePoint users to visualize and interact with data and product models using Virtual reality. SharePoint Spaces were announced at the opening keynote of SharePoint Virtual Summit, an online event coverage of the SharePoint Conference. Sharepoint is a team collaboration software tool by Microsoft, used by Office 365 users for organizing documents, managing content, and building sites internally. SharePoint Spaces will expand on SharePoint capabilities by creating mixed reality experiences for businesses viewable in VR headsets and in any browsers that support WebVR. It has templates for VR experiences such as a gallery of 3D models or 360-degree videos. Possible use cases of SharePoint spaces, as shared by Microsoft include: Recruiting and onboarding SharePoint Spaces can be used for 360-degree virtual induction of new recruits. Instead of being informed verbally, the employee can learn about the organizational structure, and campus layout through an interactive, and immersive experience. They can also access information about the products of the company. Learning Combining virtual reality with SharePoint can also be used to enhance learning techniques. Microsoft says “With SharePoint spaces, learning comes to life as you gain broad perspective with a panoramic view of a topic and learning objectives.” SharePoint Spaces allow readers and learners to explore personalized, and dynamic content by experiencing with your senses. Product development This tool can also be used for creating 3D prototypes, especially necessary in the product development landscape. Experts can study and evaluate data, content, and processes from every angle. They can also attach annotations, and visualize improvements. Microsoft will soon open a preview version of SharePoint Spaces. It will later be made available to all Office 365 commercial users. SharePoint has also incorporated AI for powerful content collaboration in Office 365. This includes: Personalized and intelligent search in the SharePoint mobile app, Personalized Office.com, Enhanced image capabilities, and Cognitive services for business process automation. Complete details about SharePoint innovations are available on the Microsoft blog. Amazon open sources Amazon Sumerian, its popular AR/VR app toolkit Verizon launches AR Designer, a new tool for developers Google open sources Seurat to bring high precision graphics to Mobile VR

OpenStack Foundation successfully launched the version 1.0 of its first non-OpenStack project, Kata Containers. Kata containers is a result of the combination of two leading open source virtualized container projects, Intel’s Clear Containers and Hyper’s runV technology. Kata Containers enable developers to have a, lighter, faster, and an agile container management technology across stacks and platforms. Developers can have a more container-like experience with security and isolation features. Kata Containers deliver an OCLI compatible runtime with seamless integration for Docker and Kubernetes. They execute a lightweight VM for every container such that every container gets similar hardware isolation as expected from a virtual machine. Although, hosted by OpenStack foundation, Kata Containers are assumed to be platform and architecture agnostic. Kata Containers 1.0 components include: Kata Containers runtime 1.0.0 (in the /runtime repo) Kata Containers proxy 1.0.0 (in the /proxy repo) Kata Containers shim 1.0.0 (in the /shim repo) Kata Containers agent 1.0.0 (in the /agent repo) KSM throttler 1.0.0 (in the /ksm-throttler repo) Guest operating system building scripts (in the /osbuilder repo) Intel, RedHat, Canonical and cloud vendors such as Google, Huawei, NetApp, and others have offered to financially support the Kata Containers Project. Read more about Kata containers on their official website and on the GitHub Repo. Google Compute Engine Plugin makes it easy to use Jenkins on Google Cloud Platform What to expect from vSphere 6.7 Red Hat Enterprise Linux 7.5 (RHEL 7.5) now generally available

The American Civil Liberties Union (ACLU) has revealed that Amazon has been selling its facial recognition software, called Rekognition, to a number of law enforcement agencies in the U.S. Using a freedom of information requests, the ACLU obtained correspondence between the respective departments and Amazon. According to the ACLU, Rekognition is a dangerous step towards a surveillance state. It could, the organization argues, lead to serious infringement on civil liberties. Here's what ACLU had to say in a post published on Tuesday 22 May: People should be free to walk down the street without being watched by the government. By automating mass surveillance, facial recognition systems like Rekognition threaten this freedom, posing a particular threat to communities already unjustly targeted in the current political climate. Once powerful surveillance systems like these are built and deployed, the harm will be extremely difficult to undo. How is Rekognition currently being used? Two U.S. police departments are using Rekognition. In Oregon, the Washington County Sheriff's Office is using the facial recognition tool to identify persons of interest from a database of 300,000 mugshots. This is a project that has been underway for some time. Chris Adzima, Senior Information Systems Analyst for the Washington County Sheriff’s Office, wrote a guest post on the AWS website outlining how they were using Rekognition in June 2017. Once the architecture was in place, the team built a mobile app to make the technology usable for officers. In Orlando, meanwhile, police have been using AWS for 'consulting and advisory services.' They are seeking to implement Rekognition in a project referred to in the documentation as 'Orlando Safety Video POC'. Orlando City police are paying $39,000 for AWS' time on the project. Civil liberties organizations pen an open letter to Jeff Bezos The ACLU, along with a number of other organizations, including the Electronic Frontier Foundation and Data for Black Lives, penned an open letter to Jeff Bezos to express their concern. In an appeal to Amazon's past commitment to civil liberties, the letter stated: In the past, Amazon has opposed secret government surveillance. And you have personally supported First Amendment freedoms and spoken out against the discriminatory Muslim Ban. But Amazon’s Rekognition product runs counter to these values. As advertised, Rekognition is a powerful surveillance system readily available to violate rights and target communities of color. The letter presents an impassioned plea for Amazon to consider the way in which it is its complicit with government agencies. It also offers a serious warning about the potential consequences of facial recognition technology in the hands of law enforcement. Amazon defends collaborating with police Amazon has been quick to defend itself. In a statement emailed to various news organizations, the company said "Our quality of life would be much worse today if we outlawed new technology because some people could choose to abuse the technology. Imagine if customers couldn’t buy a computer because it was possible to use that computer for illegal purposes? Like any of our AWS services, we require our customers to comply with the law and be responsible when using Amazon Rekognition.” However, the key issue with Amazon's statement is that the analogy with personal computers doesn't quite hold. Individuals aren't responsible for maintaining the law, and neither do they hold the same power that law enforcement agencies do. Technology might change how individuals behave, but that behavior must still comply with the law. The current scenario is a little different; the concern is around how technology might actually change the way the law functions. There isn't, strictly speaking at least, any way of governing how that happens. Whatever you make of Amazon's work with law enforcement, it's clear that we are about to enter a new era of disruption and innovation in public institutions. For some people, collaboration between public and private realms opens up plenty of opportunities. But there are many dangers that must be monitored and challenged. Read next: Top 10 Tools for Computer Vision [Link] Admiring the many faces of Facial Recognition with Deep Learning [Link]

After almost a year of Cloud ML Engine release, Google has finally announced the use of Cloud TPU for faster training and running of machine learning models on Cloud ML Engine. This beta release allow customers of Cloud ML and Google Cloud Platform to use the revolutionary TPUs and accelerate the TensorFlow based machine learning models. Key features of Cloud TPU: High-level Performance - Each Cloud TPU offers a potential of up to 180 teraflops of computing performance and 64 gigabytes of ultra-high bandwidth memory. Availability of Reference Models - Solve challenges faced in image classification and object detection applications on Cloud TPUs with access to models like RetinaNet and ResNet 50. Access to Custom Machine Types - Get an an advantage of balancing processor speeds, memory, storage resources by connecting to Cloud TPU from various custom Virtual Machine types. Key Benefits: Speed Up Machine Learning Workloads - The newly innovated Cloud TPUs are designed to help in accelerating machine learning workloads with TensorFlow. Each of the Cloud TPU are buckled up with 180 teraflops of computational power for the cutting-edge machine learning models. Such large amounts of processing speed can help you create the next research breakthrough across Machine Learning and AI technology. On-Demand Machine Learning Supercomputing - You can access to powerful and high-performance machine learning accelerators on demand with absolute zero capital investment. Easy Ramping on Google Cloud - Knowing that TensorFlow is open-source, you can simply push your machine learning workloads of TensorFlow on Cloud TPUs.You can use TensorFlow high-level APIs and move your machine learning models to CPUs, GPUs, and TPUs with few line of codes. The Cloud TPU also offers models and training environment which can easily suffice your image classification and machine translation needs. Read more about Cloud TPU features at the official  CLOUD TPU page. Tensor Processing Unit (TPU) 3.0: Google’s answer to cloud-ready Artificial Intelligence Nvidia Tesla V100 GPUs publicly available in beta on Google Compute Engine and Kubernetes Engine How to Build TensorFlow Models for Mobile and Embedded devices