Premium Block Blob Storage, which is currently in limited public preview, unlocks a new level of performance in public cloud object storage. It uses a combination of solid-state drives in our storage clusters and enhancements to our blob storage software to provide high throughput and very fast response times. In this blog post we will take a closer look at some of these performance enhancements.
Low and consistent latency
Many enterprise applications and the users that use them require very fast response times. Storage response time, also known as latency, is often a significant portion of the overall time users must wait for a response.
At Microsoft Ignite 2018, I did a storage latency demonstration, comparing Premium Blob Storage to our Standard Blob Storage. The demo reads a random selection of 10,000 objects from a population of 1,000,000 64KB objects measuring time to last byte. Each object is a binary random byte array.
In the demo, the average latency for Standard is 61.4ms compared to Premium at 5.3ms, which is more than an order of magnitude better.
It is equally important to consistently provide low latency, which we measure by looking at the 99th percentile. In the demo, the
This post was co-authored by Catherine Burke, Niko Pamboukas, Yingnong Dang, Omar Khan, and Alistair Speirs from the Microsoft Azure team.
We’re committed to ensuring that you can run your workloads reliably on Azure. One of the areas we’re investing heavily into optimizing reliability is using the combination of machine learning and live migration to predict and proactively mitigate potential failures.
Since early 2018, Azure has been using live migration in response to a variety of failure scenarios such as hardware faults, as well as regular fleet operations like rack maintenance and software/BIOS updates. Our initial use of live migration to handle failures gracefully allowed us to reduce the impact of failures on availability by 50 percent.
To further push the envelope on live migration, we knew we needed to look at the proactive use of these capabilities, based on good predictive signals. Using our deep fleet telemetry, we enabled machine learning (ML)-based failure predictions and tied them to automatic live migration for several hardware failure cases, including disk failures, IO latency, and CPU frequency anomalies.
We partnered with Microsoft Research (MSR) on building our ML models that predict failures with a high degree of accuracy before they occur.
In August 2018, we informed Azure Stack users of a new capability coming to Microsoft Azure Stack though the blog post “Enhance security and simplify network integration with Extension Host on Azure Stack”. This capability further enhances security and simplifies network integration for Azure Stack. We are proud to share that Extension Host will be enabled by the next Azure Stack update, 1811. Please note, this update does require that two additional certificates be imported in advance.
Skipping the required preparation will result in a failure during pre-update validation checks, and will result in a halt to the updating process. There is still time to acquire these two certificates which are necessary for the integration of Azure Stack update 1811.
A validation check is already part of the newly released 1809 hotfix and it shows a warning alert in the operator portal if the required certificates have not yet been imported. This is a reminder with a call to action to follow the preparation guidance in order to be prepared for the upcoming Azure Stack update 1811.
New deployments of Azure Stack have been requiring these two additional certificates since September 2018. Please check with your Azure Stack hardware partner
Azure Monitor for virtual machines (VMs) offers a myriad of monitoring features that help you view VMs from various perspectives. A few of those perspectives are:
The health of the VM and its components. This helps you to monitor, diagnose, and localize issues in operating systems, core components, and services such as Domain Name System (DNS), Dynamic Host Configuration Protocol (DHCP), and more. Also, it identifies performance issues with CPU, memory, disk, and network adapter on your VMs. Performance trends. These trends show you the performance with CPU, memory, disk, and network adapter performance metrics over time. Maps – Connection topology and metrics. This gives you a visual map that shows the processes running on your VMs and the interaction between them, as well as important events and alerts.
Azure Monitor for VMs evaluates a various canned set of conditions called health criteria on your VMs in near real-time and triggers an alert if any health criterion goes to critical/warning state (alerts are turned on by default, but you can alter the behavior). For example, if the CPU utilization health criterion is in a critical state, an alert titled “CPU utilization too high” will fire. You can manage the health
When we started running the Azure IoT on Serverless Hackathon, we knew we were about to see a lot of cool solutions coming out of it. What we couldn’t anticipate was the high level of creativity shown on them, not just on a technological level, but especially on the problems these solutions were designed to solve. When thinking about Internet of Things (IoT) solutions, we can think by default about reading information from sensors and some traditional data processing. But developers from all over the world went above and beyond and presented very helpful solutions that ranged from improving safety and security, to helping with global problems such as pollution or recycling.
Each of the more than 30 projects were creative solutions aiming to solve day-to-day problems. While all of them are great examples of how we can improve our lives through technology, we wanted to highlight the three winners and the popular choice awardee.
Please join us on congratulating the winners for building innovative and powerful projects that leverage the power of Microsoft Azure to help make the world a better place. Congratulations on a great job!
First place – Clean Water AI
Clean Water AI is an IoT
In September 2018, Azure Sphere was released for public preview. Today, we are pleased to announce the 18.11 update to the Azure Sphere Operating System, Azure Sphere Security Service, and Visual Studio development environment. This release includes substantial investments in our security infrastructure and our connectivity solutions, and it incorporates some of your feedback.
This is the first update to our public preview release, and we plan to release additional updates quarterly. Notification of software updates, new product features, tips and tricks, termination of support for older preview software, and other useful information will be posted on the Azure Updates website. Subscribe to Azure Update notifications through the RSS Feed to stay up to date with the latest Azure Sphere news.
Features in the 18.11 release
This release features strategic improvements in our internal security mechanism to allow devices that have been offline for an extended period to easily reconnect to the Azure Sphere Security Service. After manufacture, connected devices might spend months in a warehouse, during which root certificates stored on the device could expire. The Azure Sphere Security Service now seamlessly handles expired root certificates to ensure that devices that are intermittently connected or are disconnected for long
Collecting insights using Azure Monitor
Azure Database for PostgreSQL and MySQL service is a fully managed, enterprise-ready cloud service which emits the performance metrics and telemetry log to Azure Monitor service. Using Azure Monitor, you can collect, analyze, and take action on telemetry data gathered from your cloud environments. This then helps you understand how the application is behaving, get deep insights into application and database behavior, get alerts, and build remediation action to respond to the alerts. Azure Monitor collects data from various monitored Azure resources, including Azure Database for PostgreSQL and MySQL. From the metrics and data collected, the tool service enables various operations including streaming data into external partner systems.
You are able to monitor your applications running on Azure Database for PostgreSQL or MySQL via the Azure portal. For each of the Azure Database for PostgreSQL or a MySQL server, a full suite of metrics is available to monitor throughput, storage, availability, and latency. Here are the various metrics available for you in Azure Database for PostgreSQL and MySQL to get insights on server’s behavior. You can also set alerts to monitor these metrics and log data using the Azure Portal or Azure CLI. For
Deploying complex SAP landscapes into a public cloud is not an easy task. While SAP basis teams tend to be very familiar with the traditional tasks of installing and configuring SAP systems on-premise, additional domain knowledge is often required to design, build, and test cloud deployments.
There are several options to take the guesswork out of tedious and error-prone SAP deployment projects into a public cloud:
One way to get started is the SAP Cloud Appliance Library (CAL), a repository of numerous SAP solutions that can be directly deployed into a public cloud. However, apart from its cost, CAL only contains pre-configured virtual machine (VM) images, so configuration changes are hard or impossible. A free alternative has been to use SAP Quickstart Templates offered by most public cloud providers. Typically written in a shell script or a proprietary language, these templates offer some customization options for pre-defined SAP scenarios. For example, Azure’s ARM templates offer one-click deployments of SAP HANA and other solutions directly in Azure Portal.)
While both solutions are great starting points, they usually lack configuration options and flexibility required to build up an actual, production-ready SAP landscape.
Based on feedback from actual customers who move their SAP
This post was co-authored by Leon Welicki, Principal Group PM Manager, Microsoft Azure.
In October 2018, we started a monthly blog series to help you find everything that is new in the Microsoft Azure portal and the Azure mobile app in one place. We are constantly working to make it easier for you to manage your Azure environment, and we want you to be able to stay up to speed with everything that’s new. You’ll always find the most recent version of this blog at http://aka.ms/AzurePortalUpdates, so be sure you add it to your favorites and come back every month.
This month, we’re introducing a new way for you to switch between different Azure accounts without having to log-off and log-in again, or working with multiple browser tabs. We’ve also made enhancements to the way you find what you need in the Azure Marketplace, to the management experience for Site Recovery, Access Control, and database services.
Here’s the list of November updates to the Azure portal: Portal shell and UI
Many different customers across industries want to have insights into the emotional moments that appear in different parts of their media content. For broadcasters, this can help create more impactful promotion clips and drive viewers to their content; in the sales industry it can be super useful for analyzing sales calls and improve convergence; in advertising it can help identify the best moment to pop up an ad, and the list goes on and on. To that end, we are excited to share Video Indexer’s (VI) new machine learning model that mimics humans’ behavior to detect four cross-cultural emotional states in videos: anger, fear, joy, and sadness.
Endowing machines with cognitive abilities to recognize and interpret human emotions is a challenging task due to their complexity. As humans, we use multiple mediums to analyze emotions. These include facial expressions, voice tonality, and speech content. Eventually, the determination of a specific emotion is a result of a combination of these three modalities to varying degrees.
While traditional sentiment analysis models detect the polarity of content – for example, positive or negative – our new model aims to provide a finer granularity analysis. For example, given a moment with negative sentiment, the