Kategorie: Microsoft Azure

Advancing Microsoft Teams on Azure—operating at pandemic scale

“The COVID-19 pandemic has reset what it means to work, study, and socialize. Like many of us, I have come to rely on Microsoft Teams as my connection to my colleagues. In this post, our friends from the Microsoft Teams product group—Rish Tandon (Corporate Vice President), Aarthi Natarajan (Group Engineering Manager), and Martin Taillefer (Architect)—share some of their learnings about managing and scaling an enterprise-grade, secure productivity app.” – Mark Russinovich, CTO, Azure

 

Scale, resiliency, and performance do not happen overnight—it takes sustained and deliberate investment, day over day, and a performance-first mindset to build products that delight our users. Since its launch, Teams has experienced strong growth: from launch in 2017 to 13 million daily users in July 2019, to 20 million in November 2019. In April, we shared that Teams has more than 75 million daily active users, 200 million daily meeting participants, and 4.1 billion daily meeting minutes. We thought we were accustomed to the ongoing work necessary to scale service at such a pace given the rapid growth Teams had experienced to date. COVID-19 challenged this assumption; would this experience give us the ability to keep the service running amidst a previously unthinkable growth period?

A solid foundation

Teams is built on a microservices architecture, with a few hundred microservices working cohesively to deliver our product’s many features including messaging, meetings, files, calendar, and apps. Using microservices helps each of our component teams to work and release their changes independently.

Azure is the cloud platform that underpins all of Microsoft’s cloud services, including Microsoft Teams. Our workloads run in Azure virtual machines (VMs), with our older services being deployed through Azure Cloud Services and our newer ones on Azure Service Fabric. Our primary storage stack is Azure Cosmos DB, with some services using Azure Blob Storage. We count on Azure Cache for Redis for increased throughput and resiliency. We leverage Traffic Manager and Azure Front Door to route traffic where we want it to be. We use Queue Storage and Event Hubs to communicate, and we depend on Azure Active Directory to manage our tenants and users.

 

 

While this post is mostly focused on our cloud backend, it’s worth highlighting that the Teams client applications also use modern design patterns and frameworks, providing a rich user experience, and support for offline or intermittently connected experiences. The core ability to update our clients quickly and in tandem with the service is a key enabler for rapid iteration. If you’d like to go deeper into our architecture, check out this session from Microsoft Ignite 2019.

Agile development

Our CI/CD pipelines are built on top of Azure Pipelines. We use a ring-based deployment strategy with gates based on a combination of automated end-to-end tests and telemetry signals. Our telemetry signals integrate with incident management pipelines to provide alerting over both service- and client-defined metrics. We rely heavily on Azure Data Explorer for analytics.

In addition, we use an experimentation pipeline with scorecards that evaluate the behavior of features against key product metrics like crash rate, memory consumption, application responsiveness, performance, and user engagement. This helps us figure out whether new features are working the way we want them to.

All our services and clients use a centralized configuration management service. This service provides configuration state to flip product features on and off, adjust cache time-to-live values, control network request frequencies, and set network endpoints to contact for APIs. This provides a flexible framework to “launch darkly,” and to conduct A/B testing such that we can accurately measure the impact of our changes to ensure they are safe and efficient for all users.

Key resiliency strategies

We employ several resiliency strategies across our fleet of services:

Active-active fault tolerant systems: An active-active fault tolerant system is defined as two (or more) operationally-independent heterogenous paths, with each path not only serving live traffic at a steady-state but also having the capability to serve 100 percent of expected traffic while leveraging client and protocol path-selection for seamless failover. We adopt this strategy for cases where there is a very large failure domain or customer impact with reasonable cost to justify building and maintaining heterogeneous systems. For example, we use the Office 365 DNS system for all externally visible client domains. In addition, static CDN-class data is hosted on both Azure Front Door and Akamai.
Resiliency-optimized caches: We leverage caches between our components extensively, for both performance and resiliency. Caches help reduce average latency and provide a source of data in case a downstream service is unavailable. Keeping data in caches for a long time introduces data freshness issues yet keeping data in caches for a long time is the best defense against downstream failures. We focus on Time to Refresh (TTR) to our cache data as well as Time to Live (TTL). By setting a long TTL and a shorter TTR value, we can fine-tune how fresh to keep our data versus how long we want data to stick around whenever a downstream dependency fails.
Circuit Breaker: This is a common design pattern that prevents a service from doing an operation that is likely to fail. It provides a chance for the downstream service to recover without being overwhelmed by retry requests. It also improves the response of a service when its dependencies are having trouble, helping the system be more tolerant of error conditions.
Bulkhead isolation: We partition some of our critical services into completely isolated deployments. If something goes wrong in one deployment, bulkhead isolation is designed to help the other deployments to continue operating. This mitigation preserves functionality for as many customers as possible.
API level rate limiting: We ensure our critical services can throttle requests at the API level. These rate limits are managed through the centralized configuration management system explained above. This capability enabled us to rate limit non-critical APIs during the COVID-19 surge.
Efficient Retry patterns: We ensure and validate all API clients implement efficient retry logic, which prevents traffic storms when network failures occur.
Timeouts: Consistent use of timeout semantics prevents work from getting stalled when a downstream dependency is experiencing some trouble.
Graceful handling of network failures: We have made long-term investments to improve our client experience when offline or with poor connections. Major improvements in this area launched to production just as the COVID-19 surge began, enabling our client to provide a consistent experience regardless of network quality.

If you have seen the Azure Cloud Design Patterns, many of these concepts may be familiar to you.  We also use the Polly library extensively in our microservices, which provides implementations for some of these patterns.

Our architecture had been working out well for us, Teams use was growing month-over-month and the platform easily scaled to meet the demand. However, scalability is not a “set and forget” consideration, it needs continuous attention to address emergent behaviors that manifest in any complex system.

When COVID-19 stay-at-home orders started to kick in around the world, we needed to leverage the architectural flexibility built into our system, and turn all the knobs we could, to effectively respond to the rapidly increasing demand.

Capacity forecasting

Like any product, we build and constantly iterate models to anticipate where growth will occur, both in terms of raw users and usage patterns. The models are based on historical data, cyclic patterns, new incoming large customers, and a variety of other signals.

As the surge began, it became clear that our previous forecasting models were quickly becoming obsolete, so we needed to build new ones that take the tremendous growth in global demand into account. We were seeing new usage patterns from existing users, new usage from existing but dormant users, and many new users onboarding to the product, all at the same time. Moreover, we had to make accelerated resourcing decisions to deal with potential compute and networking bottlenecks. We use multiple predictive modeling techniques (ARIMA, Additive, Multiplicative, Logarithmic). To that we added basic per-country caps to avoid over-forecasting. We tuned the models by trying to understand inflection and growth patterns by usage per industry and geographic area. We incorporated external data sources, including Johns Hopkins’ research for COVID-19 impact dates by country, to augment the peak load forecasting for bottleneck regions.

Throughout the process, we erred on the side of caution and favored over-provisioning—but as the usage patterns stabilized, we also scaled back as necessary.

Scaling our compute resources

In general, we design Teams to withstand natural disasters. Using multiple Azure regions helps us to mitigate risk, not just from a datacenter issue, but also from interruptions to a major geographic area. However, this means we provision additional resources to be ready to take on an impacted region’s load during such an eventuality. To scale out, we quickly expanded deployment of every critical microservice to additional regions in every major Azure geography. By increasing the total number of regions per geography, we decreased the total amount of spare capacity each region needed to hold to absorb emergency load, thereby reducing our total capacity needs. Dealing with load at this new scale gave us several insights into ways we could improve our efficiency:

We found that by redeploying some of our microservices to favor a larger number of smaller compute clusters, we were able to avoid some per-cluster scaling considerations, helped speed up our deployments, and gave us more fine-grained load-balancing.
Previously, we depended on specific virtual machine (VM) types we use for our different microservices. By being more flexible in terms of a VM type or CPU, and focusing on overall compute power or memory, we were able to make more efficient use of Azure resources in each region.
We found opportunities for optimization in our service code itself. For example, some simple improvements led to a substantial reduction in the amount of CPU time we spend generating avatars (those little bubbles with initials in them, used when no user pictures are available).

Networking and routing optimization

Most of Teams’ capacity consumption occurs within daytime hours for any given Azure geography, leading to idle resources at night. We implemented routing strategies to leverage this idle capacity (while always respecting compliance and data residency requirements):

Non-interactive background work is dynamically migrated to the currently idle capacity. This is done by programming API-specific routes in Azure Front Door to ensure traffic lands in the right place.
Calling and meeting traffic was routed across multiple regions to handle the surge. We used Azure Traffic Manager to distribute load effectively, leveraging observed usage patterns. We also worked to create runbooks which did time-of-day load balancing to prevent wide area network (WAN) throttling.

Some of Teams’ client traffic terminates in Azure Front Door. However, as we deployed more clusters in more regions, we found new clusters were not getting enough traffic. This was an artifact of the distribution of the location of our users and the location of Azure Front Door nodes. To address this uneven distribution of traffic we used Azure Front Door’s ability to route traffic at a country level. In this example you can see below that we get improved traffic distribution after routing additional France traffic to the UK West region for one our services.

 
Figure 1: Improved traffic distribution after routing traffic between regions.

Cache and storage improvements

We use a lot of distributed caches. A lot of big, distributed caches. As our traffic increased, so did the load on our caches to a point where the individual caches would not scale. We deployed a few simple changes with significant impact on our cache use:

We started to store cache state in a binary format rather than raw JSON. We used the protocol buffer format for this.
We started to compress data before sending it to the cache. We used LZ4 compression due to its excellent speed versus compression ratio.

We were able to achieve a 65 percent reduction in payload size, 40 percent reduction in deserialization time, and 20 percent reduction in serialization time. A win all around.

Investigation revealed that several of our caches had overly aggressive TTL settings, resulting in unnecessary eager data eviction. Increasing those TTLs helped both reduce average latency and load on downstream systems.

Purposeful degradation (feature brownouts)

As we didn’t really know how far we’d need to push things, we decided it was prudent to put in place mechanisms that let us quickly react to unexpected demand spikes in order to buy us time to bring additional Teams capacity online.

Not all features have equal importance to our customers. For example, sending and receiving messages is more important than the ability to see that someone else is currently typing a message. Because of this, we turned off the typing indicator for a duration of two weeks while we worked on scaling up our services. This reduced peak traffic by 30 percent to some parts of our infrastructure.

We normally use aggressive prefetching at many layers of our architecture such that needed data is close at hand, which reduces average end-to-end latency. Prefetching however can get expensive, as it results in some amount of wasted work when fetching data that will never be used, and it requires storage resources to hold the prefetched data. In some scenarios we chose to disable prefetching, freeing up capacity on some of our services at the cost of higher latency. In other cases, we increased the duration of prefetch sync intervals. One such example was suppressing calendar prefetch on mobile which reduced request volume by 80 percent:
 

Figure 2: Disable prefetch of calendar event details in mobile.

Incident management

While we have a mature incident management process that we use to track and maintain the health of our system, this experience was different. Not only were we dealing with a huge surge in traffic, our engineers and colleagues were themselves going through personal and emotional challenges while adapting to working at home.

To ensure that we not only supported our customers but also our engineers, we put a few changes in place:

Switched our incident management rotations from a weekly cadence to a daily cadence.
Every on-call engineer had at least 12 hours off between shifts.
We brought in more incident managers from across the company.
We deferred all non-critical changes across our services.

These changes helped ensure that all of our incident managers and on-call engineers had enough time to focus on their needs at home while meeting the demands of our customers.

The future of Teams

It is fascinating to look back and wonder what this situation would have been like if it happened even a few years ago. It would have been impossible to scale like we did without cloud computing. What we can do today by simply changing configuration files could previously have required purchasing new equipment or even new buildings. As the current scaling situation stabilizes, we have been returning our attention to the future. We think there are many opportunities for us to improve our infrastructure:

We plan to transition from VM-based deployments to container-based deployments using Azure Kubernetes Service, which we expect will reduce our operating costs, improve our agility, and align us with the industry.
We expect to minimize the use of REST and favor more efficient binary protocols such as gRPC. We will be replacing several instances of polling throughout the system with more efficient event-based models.
We are systematically embracing chaos engineering practices to ensure all those mechanisms we put in place to make our system reliable are always fully functional and ready to spring into action.

By keeping our architecture aligned with industry approaches and by leveraging best practices from the Azure team, when we needed to call for assistance, experts could quickly help us solve problems ranging from data analysis, monitoring, performance optimization and incident management. We are grateful for the openness of our colleagues across Microsoft and the broader software development community. While the architectures and technologies are important, it is the team of people you have that keeps your systems healthy.

 

Related post: Azure responds to COVID-19.
Related article: Growing Azure’s capacity to help customers, Microsoft during the COVID-19 pandemic.
Quelle: Azure

Town of Cary innovates flood prediction with IoT

This post was co-authored by Daniel Sumner, Worldwide Industry Director, Government—Smart Infrastructure at Microsoft.

According to Flood Safety, flooding is the most common type of natural disaster worldwide. It affects tens of millions of people around the world each year and causes, on average, more than $200 billion in damages. Many communities face flood-related challenges, and the Town of Cary in North Carolina, United States, is no different. Its flood-prone areas are affected by heavy rains, which are often exacerbated by the yearly Atlantic hurricane season. When the town sees excessive rainfall, its personnel often find themselves scrambling to address overflowing stormwater systems, but even a burst water main can create a spontaneous flood event.

Town of Cary parking lot during a flood event.

As a leader in innovative city solutions, the Town of Cary was already committed to using smart technology, data, and analytics to optimize city functions, drive economic growth, and improve the quality of life. Chief Information Officer, Nicole Raimundo, Smart City Strategist, Terry Yates, and Stormwater Operations Manager, Billy Lee, saw another opportunity: use technology to predict and manage flood events.

Envisioning a flood prediction solution

In October 2019, Cary’s leaders met with partners Microsoft and SAS, IoT division, to envision a new solution. The team started by assessing the current situation.

During storm events, Cary had no visibility into the river levels or how quickly the water was rising. Traditionally, the town relied on citizens to alert them of floods through phone calls, text messages, and other means. The town staff processed these requests manually dispatching public work personnel to erect barriers and close roads and first responders to emergencies.

The team came away with a vision for building a flood prediction system leveraging Azure IoT and SAS Analytics for IoT. Raimundo explained the need for the change.

“We felt strongly that the existing system wasn’t serving citizens in flood-prone areas well. We knew we needed a scalable solution to get us from reactive to proactive and ultimately predictive. The scalability of Azure IoT platform became a critical component of our IoT architecture. In addition, we required a robust set of analytical tools that could deliver insight from both real-time and historical data and SAS Analytics for IoT offered that.” —Nicole Raimundo, Chief Information Officer, Town of Cary

“There are thousands of cities that are similar to the Town of Cary that are looking to deploy solutions to solve urban issues such as flooding. Leveraging the Azure IoT platform and SAS Analytics for IoT these cities can move from being reactive to proactive and, ultimately, predictive in a cost-effective, scalable manner.” —Daniel Sumner, Worldwide Industry Director, Government—Smart Infrastructure at Microsoft

Defining project goals

Cary, Microsoft, and SAS agreed to several project goals outlined below.

Improve the situational awareness of town staff.
Automate stormwater personnel notifications and work order generation.
Alert citizens of flooding events.
Provide data to downstream regional and state entities.
Analyze captured data and predict future flood events.

A key requirement for the Town of Cary was that their new flood prediction system needed to integrate with existing business systems. These included using the SAS Visual Analytics dashboard integrated with ArcGIS for real-time visualization, Salesforce for alerts, automated notifications and work orders, and data sharing for regional partner response systems.

“The Azure IoT platform has been a critical piece of our technology ecosystem and accelerates our ability to scale.” —Terry Yates, Smart City Strategist, Town of Cary

Through a series of work sessions with the partners in February 2020, the team created a project plan and system architecture. Then the implementation work began.

Town of Cary working session with Microsoft and SAS resources.

Implementing the solution

The Town of Cary installed water level sensors at various points along the Walnut Creek stream basin and rain gauges at several Town of Cary owned facilities.

Water sensors were placed at strategic locations.

Below are highlights of how the solution was built.

Microsoft Azure IoT Hub enabled a highly secure and reliable communication to ingest stormwater levels over an Firstnet LTE wireless connection. The team used Azure IoT Hub to provision, authenticate, and manage the two-way communication to the sensors.
SAS Analytics for IoT combined streaming sensors or gauges and weather data for real-time scoring, dashboarding, and historical reporting.
SAS Visual Analytics provided interactive dashboard, reports, business intelligence, and analytics. The dashboard is integrated with ESRI ArcGIS for additional geographic analysis and data visualization.
Microsoft Azure Logic Apps seamlessly integrated with Salesforce and other third-party applications.
Microsoft Azure Synapse Analytics provides data warehousing for Big Data analytics.

Evaluating results

The solution’s initial phase has been running for several months with positive results.

Town staff can now visualize flooding events in real-time.
Stormwater personnel receive notifications and can generate work orders automatically.
A mechanism has been established to share data with regional partners.

“We’re still connecting some of the dots, but we’re already seeing real benefits in the automation of formerly manual processes. Previously, we might get a call from a citizen, which would cause us to dispatch public works or emergency services depending on the type of flooding. Now the data triggers alerts that automatically notify stormwater personnel, who can react and address the flooded areas. It’s much more efficient and could ultimately save lives.” —Nicole Raimundo, Chief Information Officer, Town of Cary

Lee explained how exciting it is to be able to visualize water flow and using the SAS Visual Analytics dashboard which is fully integrated with the ESRI ArcGIS.

“Now we can see a storm event in real time. We can pull up the dashboard and see how much rain we’re getting. We can see the stream levels rising and share this data with our regional partners. It’s amazing to see the data in real-time.” —Billy Lee, Stormwater Operations Manager, Town of Cary

Town of Cary storm water IoT dashboard.

Applying analytics

As the Atlantic region nears the peak of hurricane season, Cary’s leaders are looking forward to better predicting potential flood events. Leveraging SAS Analytics for IoT and SAS Event Stream Processing (ESP), the Town of Cary has enhanced their ability to acquire and manage new data from Azure IoT, generate and deploy predictive models, manage the lifecycle of those models over time, and achieve greater insight they can take action on.

“Using Microsoft Azure IoT with the capabilities to integrate the water sensor data, Accuweather data from Azure Maps, and SAS analytics we are able to create a digital twin of the watershed. This allows the Town of Cary to be proactive in addressing floodwater issues so action can be taken ahead of the storm or flooding event.” —Brad Klenz, Distinguished IoT Analytics Architect, SAS

In the case of the flood detection and management solution, the Town of Cary can better identify anomalies, such as rising water, through the integration of weather forecasting data, real-time sensor data measuring water and rain levels to deliver advanced warnings and future predictions of flooding events both within the Town of Cary and downstream to surrounding municipalities.

“Cary sits on top of several rain basins. We will now be able predict flooding and share this information with our regional neighbors. This data and predictability will have a huge economic impact, not just in the Town of Cary, but for many municipalities, including local businesses and citizens, downstream.” —Nicole Raimundo, Chief Information Officer, Town of Cary

Advice to other cities

The Town of Cary has implemented a series of smart city initiatives, and its flood prediction solution shows amazing promise. What advice would Raimundo and Yates provide to other cities looking to implement similar projects?

“It’s really about selecting the right partners that understands your platform strategy vision for building solutions on a future-proof scalable architecture and that offer a flexible and open set of tools.” —Nicole Raimundo, Chief Information Officer, Town of Cary

Yates encouraged his peers to get the buy-in of all stakeholders.

“Include all departments, all subject matter experts in the digital transformation process and especially people working out in the field. You’ll need everyone’s buy-in and participation to be successful.” —Terry Yates, Smart City Program Strategist, Town of Cary

Next steps

Learn more about Azure IoT, SAS Analytics for IoT, and Microsoft for smart cities.
Quelle: Azure

Six reasons customers trust Azure to run their SAP solutions

As global organizations across every industry adjust to the new normal, SAP solutions are playing an increasingly vital role in addressing immediate needs and paving a path to a resilient future. Now more than ever, companies are realizing the value of running their SAP solutions in the cloud. While some are using advanced analytics to process their SAP data to make real-time business decisions, others are integrating their SAP and non-SAP data to build stronger supply chains. Whether it’s meeting urgent customer needs, empowering employees to make quick decisions, or planning for the future, customers running SAP solutions in the cloud have been well prepared to face the new reality. Check out how Walgreens delivers superior customer service with SAP solutions on Microsoft Azure.

Many organizations running their SAP solutions on-premises have become increasingly aware of the need to be more agile and responsive to real-time business needs. According to an IDC survey, 54 percent of enterprises expect the future demand for cloud software will increase. As global organizations seek agility, cost savings, risk reduction, and immediate insights from their ERP solutions, here are some reasons many of the largest enterprises choose Microsoft Azure as their trusted partner when moving their SAP solutions to the cloud.

1. Running SAP solutions on Azure delivers immediate insights and increased agility

“Now that we have SAP in the cloud … we have a platform for digital innovation in the cloud … With Azure, we’ve lifted our entire IT landscape up to a higher level where we can drive experimentation with much less risk and much less cost.”—Sarah Haywood, Chief Technology Officer and Vice President of Technology at Carlsberg Group

Organizations running SAP solutions on Azure gain real-time and predictive insights that empower them to break into new ways of doing business. Azure offers the ability to tap into more than 100 cloud services, access SAP Cloud Platform, apply intelligent analytics, and also integrate with an organization’s existing productivity and collaboration tools such as Microsoft 365, Microsoft Teams, Microsoft Power Apps, and Microsoft Power BI.

With Azure, organizations can integrate their SAP and non-SAP data through an extensive portfolio of Azure data services and create real-time dashboard views of the current operations using SAP and Microsoft business intelligence tools. Using intelligent analytics deepens real-time and predictive insights to improve decision-making by responding dynamically as business conditions change, and how that change impacts your customers or products. Integration with Teams and Microsoft 365 improves team collaboration and enhances user experience and productivity. Using Microsoft Power Automate, Power Apps, and Power BI, organizations can create customized workflows, apps, and business insight reports without having to write any code.

 

2. An ever-evolving and growing set of Azure cloud services drives continuous innovation

“We are looking at drones, IoT, RFID sensors, artificial intelligence, chatbots, and every other futuristic technology you can think of to do mining better, and with Azure we have a broad foundation for exploring all that.”—Head of Enterprise IT Services, Rio Tinto

While Zuellig Pharma is building an app that uses Azure blockchain services and data from the SAP Business Suite on HANA to track and capture counterfeit products and illegal parallel imports in its region, Walgreens plans to use AI and machine learning to develop new customer offerings quickly and respond in real time to changes in the marketplace.

Customers such as Rio Tinto are using Azure’s secure and scalable IoT applications to pilot a solution to take real-time data from trucks, drills, smelters, and other equipment and analyze it to gain equipment health, preemptive maintenance, supply chain efficiency, and other operational intelligence. Additionally, with DevOps with GitHub and Azure Kubernetes Service, customers can build, manage, and deploy applications on a massive global network.

3. Running SAP solutions on Azure offers costs savings

“We chose to migrate to Azure for three main reasons: cost, strategy, and speed … We saw a big cost advantage with SAP HANA on Azure over the cloud we currently used”—David South, Director of Architecture at Coke One North America Services

A Forrester study showed customers achieved more than 100 percent ROI, a 50 percent reduction in data center costs, and a 100 percent reduction in SAP release delays by migrating their SAP systems to Azure. Moving to Azure not only eliminates capital expenditure and cost of underutilized hardware, but it also offers cost management tools such as on-demand scaling during peak usage periods, using cheaper storage, and optimizing disaster recovery environments.

By running SAP solutions on Azure, organizations replace expensive, manual, and error-prone processes with automated, flexible processes, and with a single ticket-to-solution experience, enterprises empower employees to focus on value-added activities by putting data in their hands.

4. Running SAP solutions on Azure offers immense flexibility and scalability

“Moving to Azure gives us the scalability we need … running SAP on Azure gives us the agility and flexibility we need to disrupt the healthcare industry in a way that improves our customers’ access to the products and services they need.”—Dan Regalado, Vice President of Global Technology Transformation and Strategic Partnerships, Walgreens

Customers across every industry run their largest production SAP landscapes on Azure because it is a proven cloud platform certified by SAP to run their most mission-critical SAP applications. Azure offers the industry’s most performant and scale-able cloud infrastructure—offering 192 GB to 12 TB SAP HANA certified VMs in more regions than any other public cloud provider along with support for both Linux and Windows OS. Azure offers on-demand scalability and agility that reduces the time to market —customers can spin up or spin down resources as needed. For instance, Daimler AG reduced operational costs by 50 percent and increased agility by spinning up resources on-demand in 30 minutes with SAP S/4HANA and Azure. 

Azure also offers access to more than 1,000 pre-built integrations, out-of-the-box business services, SAP HANA services, and apps built by SAP and our partners. Customers such as Tate and Lyle appreciate that with Azure, they get access to compute, network, and storage resources preconfigured for SAP HANA that they didn’t have to build, install, or manage.

5. SAP solutions on Azure offer best-in-class security, compliance, and business continuity

“If you go to the Microsoft Trust Center, you can see the tremendous investment Microsoft makes in security certifications and compliance. It would have been very costly for Kennametal to implement that level of security within our own environment. Instead, we get to inherit it from Microsoft.”—John Johnston, Senior Manager, Global Information Security and Compliance, Kennametal

Azure’s intelligent security services are backed by a $1 billion annual investment in enterprise-grade security and compliance offers and 3,500 cybersecurity professionals. Azure has the most compliance offerings of any public cloud. Azure offers the best-in-class security services such as Azure Sentinel for SIEM, Azure security center for threat monitoring, and Azure Active Directory for identity management. Additionally, customers can leverage built-in availability and recovery options such as Azure Backup and Azure Site Recovery to ensure business continuity and data protection. Microsoft teams work closely with partners to ensure that critical systems remain online during migration and offer a robust set of joint planning workshops, migration programs such as FastTrack, POCs, and training and certifications.

6. Organizations benefit from the trusted partnership between SAP and Microsoft

“We needed a provider that enjoys a close partnership with SAP, understands our needs, and can accelerate our migration and expand our capabilities. Azure answered every need.”—Joshua Sefchek, Manager of Cloud and Enterprise Services, Toyota Material Handling North America

After decades of working together to serve our customers, SAP and Microsoft deepened their relationship by signing the Embrace initiative. As part of Embrace, SAP will lead with Azure to move on-premise SAP ERP and SAP S/4HANA customers to the cloud through industry-specific best practices, reference architectures, and cloud-delivered services. Our engineering teams co-residing in Germany and Redmond, Washington work together to develop joint reference architectures, product integration roadmaps, and best practices; our industry teams are jointly developing industry-specific transformation roadmaps, and our support teams have developed collaborative support models.

SAP and Microsoft have been partners for more than 25 years and are also mutual customers. Microsoft is the only cloud provider that’s been running SAP for its own finance, HR, and supply chains for the last 20 years, including SAP S/4HANA. Likewise, SAP has chosen Azure to run a growing number of its own internal system landscapes, including those based on SAP S/4HANA. Microsoft IT and SAP IT generously share their learnings from running SAP solutions on Azure with our customers. Check out the latest MSIT webinar and SAP IT webinar for some best practices.

More than 95 percent of Fortune 500 companies run their business on Azure. Our experience and history give us a powerful understanding of the needs of enterprise customers. Together with SAP, customers have trusted us with their most critical workloads for decades because we understand what it takes to support our customers in their journey to the cloud.

We look forward to seeing you this month at the virtual SAPPHIRE and ASUG events. Learn more about SAP solutions on Azure and read today’s announcement about new offerings to help our SAP customers optimize costs and increase agility. 
Quelle: Azure

New general purpose and memory-optimized Azure Virtual Machines with Intel now available

Today we're announcing the availability of new general purpose and memory-optimized Azure Virtual Machines based on the 2nd generation Intel Xeon Platinum 8272CL (Cascade Lake). This custom processor runs at a base speed of 2.5GHz and can achieve all-core turbo frequency of 3.4GHz. It features Intel® Deep Learning Boost Technology, Intel® Advanced Vector Extensions 512 (Intel® AVX-512), Intel® Turbo Boost Technology 2.0, and Intel® Hyper-Threading Technology.

With this announcement, we are introducing two new Azure Virtual Machines families, one of which represents a brand-new product category in our portfolio:

The Azure Ddv4 and Ddsv4 and Edv4 and Edsv4 virtual machines, which include a local data temporary disk (now generally available)
The Azure Dv4 and Dsv4 and Ev4 and Esv4 virtual machines, a new category of virtual machines, which rely on remote disks and do not provide temporary local storage (now in preview).

The new virtual machine (VM) sizes deliver up to roughly 20 percent CPU performance improvement compared to their predecessors, the Dv3 and Ev3 VM families.

New Ddv4 and Ddsv4 and Edv4 and Edsv4 VMs are generally available

The new Ddv4 and Ddsv4 and Edv4 and Edsv4 VM sizes include fast, larger local solid state drive (SSD) storage and are designed for applications that benefit from low latency, high-speed local storage, such as applications that need fast reads and writes to temporary storage, or applications that need temporary storage for caches or temporary files. These new VM sizes offer 50 percent larger local storage, as well as better local disk IOPS for both Read and Write operations compared to the Dv3 and Dsv3 and Ev3 and Esv3 sizes with generation 2 (Gen 2) VMs. The new Ddv4 and Ddsv4 and Edv4 and Edsv4 VM sizes can be attached to standard HDD, standard SSD, premium SSD, or ultra SSD persistent disks.

The new Ddv4 and Ddsv4 VM sizes provide a good balance of memory-to-vCPU performance, with up to 64 vCPUs, 256 GiB of RAM, and include local SSD storage (up to 2,400 GiB). These VM families are ideal for development and testing, small to medium databases, and low-to-medium traffic web servers.

The new Edv4 and Edsv4 VM sizes feature a high memory-to-CPU ratio, with up to 64 vCPUs, 504 GiB of RAM, and also include local SSD storage (up to 2,400 GiB). These VM families are great for relational database servers and in-memory analytics.

New Dv4 and Dsv4 and Ev4 and Esv4 VMs now in preview

The Dv4 and Dsv4 and Ev4 and Esv4 VM sizes are new offerings that do not include the local temporary disk. These new VM families offer 20 percent CPU improvement over the Dv3 and Ev3 VM families. The new Dv4 and Ev4 VM sizes can be attached to standard HDD and standard SSD persistent disks. While the Dsv4 and Esv4 VM sizes can be attached to standard HDD, standard, SSD, premium SSD, or ultra SSD persistent disks. If you are currently using v3 VM sizes, switching to v4 sizes will provide you a better price-per-core performance option.

If you are currently using v3 VM sizes, switching to v4 sizes will provide you a better price-per-core performance option.

The new Dv4 and Dsv4 VM sizes provide a good balance of memory to vCPU performance, with up to 64 vCPUs and 256 GiB of RAM. These VM families are ideal for development and testing workloads, small-to-medium databases, and low-to-medium traffic web servers.

The new Ev4 and Esv4 VM sizes feature a high memory-to-CPU ratio, with up to 64 vCPUs and 504 GiB of RAM. These VM families are great for relational database servers and in-memory analytics.

Customers can request access to these new VMs (with no local temporary disk) currently in preview today by filling out this form. If you have any further questions or feedback, please reach out to us directly.

Working in collaboration with Intel while meeting our customer needs

“The launch of Azure D-v4 and E-v4-series virtual machines further extends the Microsoft IaaS portfolio to meet the diverse needs of our customers. Powered by 2nd Generation Intel® Xeon Scalable Processors, these virtual machines offer optimized application performance for web and data services, desktop virtualization and business applications moving to Azure.” —Jason Grebe, Intel CVP Cloud and Enterprise

With these new VM sizes, we are providing more customer value with better CPU performance.

“Silicon design workloads require high CPU performance, large number of cores, high memory-to-core ratios, and sufficient local storage. The newly introduced Edsv4 family meets all these requirements, making it an ideal choice for our use cases. Using the Edsv4 VMs, TSMC was able to successfully create a brand new Scale-Out/Scale-In silicon design strategy, helping designers achieve significant run-time speedup and cost optimization.” —Willy Chen, Deputy Director, Design & Technology Platform, TSMC

Frequently asked questions

Customers regularly ask what the differences are between the new VMs and the general purpose Dv3/Dsv3 or memory-optimized Ev3/Esv3 VM sizes that they’re currently using. The answer is that you’ll now have more options to choose from. The table below summarizes the key differences:

Customers also ask what happens if they still need a local temp disk for their VM. You can choose the Ddv4 and Ddsv4 or Edv4 and Edsv4 VM sizes for your application if a local disk is still required.

For more frequently asked questions related to these VM sizes, refer to Azure VM sizes with no local temp disk.

Region availability for Ddv4, Ddsv4, Edv4, and Edsv4 VM sizes

The new Ddv4, Ddsv4, Edv4, and Edsv4 VM families are available in Pay-As-You-Go, Reserved Instance, and Spot in the following Regions. Prices vary by Region.

Get started today

Learn more about Ddv4 and Ddsv4-series or Edv4 and Edsv4-series (with local temporary disk) now generally available.
You can learn more about the Dv4, Dsv4-series or Ev4, Esv4-series VMs (without local temporary disk) that are currently in preview.
You can also request access to the new VMs currently in preview by filling out this form. If you have any further questions or feedback, please reach out to us directly.

Quelle: Azure

New features and insights in Azure Monitor

Customers need full stack observability for their apps and infrastructure across Azure and hybrid environments to ensure their workloads are always up and running, for which they rely on Azure Monitor. Over the past few months, we have released many new capabilities targeting to improve native integration with Azure, enable easier onboarding at scale, support enterprise security and compliance needs, provide rich full stack distributed tracing, and much more. In this blog, we're sharing the newest enhancements from Azure Monitor announced at Microsoft Build, including:

Preview of Azure Monitor Application Insights logs being available directly on Log Analytics Workspaces.  
General availability of Azure Monitor for Storage and Azure Cosmos DB, with previews for Key Vault and Azure Cache for Redis.

Be sure to read through the blog post to learn about the full list of announcements at the end.

Application Insights on Log Analytics Workspaces

Logs from Application Insights could previously only be stored separately for each monitored application, and you had to resort to cross-workspace queries to correlate with logs in Log Analytics Workspaces. Continuing the integration of Application Insights with Log Analytics, we are announcing a preview of a major milestone today in our Application Performance Management (APM) story. You can now choose to send your Application Insights logs to a common Log Analytics Workspace, keeping application, infrastructure, and platform logs altogether.

 
This would let you apply common role-based access control across your resources and not have to worry about cross-application or workspace queries anymore (even the Application Insights logs schema is now integrated and made consistent with other data tables in Log Analytics).

 

You will now be able to export both your metrics and logs data to firewalled Azure Storage accounts or stream to Azure Event Hub via Diagnostic Settings. Note that exporting to Storage or Event Hub is a premium feature that would start getting billed once it reaches general availability. You can also start optimizing cost through reserved capacity pricing at workspace level with Log Analytics.

One of the biggest benefits with this upgrade is that you will now be able to easily drive enterprise readiness for your application logs, with all the new enhancements coming soon in Azure Monitor Logs (including Customer Managed Key Encryption, Network Isolation with Private Link support, Business Continuity Disaster Recovery with Globally Distributed Workspaces, High Availability with Availability Zones, Global Availability with Satellite Regions, and much more).

Out-of-the-box insights for Azure Resources

Customers have asked us to provide more out of the insights like the ones we provide for virtual machines, containers, and network. We are now happy to provide you out of the box insights on more Azure resources using the platform metrics that Azure Monitor already collects. These insights are built on workbooks which is a platform for creating and sharing rich interactive reports. You can access any of these insights out-of-the-box and customize it even further. These can be accessed either directly from an individual resource blade, or at scale from Azure Monitor blade in Azure Portal.

Azure Monitor for Storage is now generally available, offering comprehensive monitoring of your Azure Storage accounts covering insights across health and capacity, with the ability to focus on hotspots and help you diagnose latency, throttling, and availability issues.
  
Azure Monitor for Azure Cosmos DB is also now GA, where you can access insights on usage, failures, capacity, throughput, and operations for your Azure Cosmos DB resources across subscriptions.
  
We also announced previews of Azure Monitor for Key Vault and Azure Monitor for Azure Cache for Redis which will provide similar out-of-the-box insights for these resources, helping you use them optimally.

More enhancements

In addition to the two highlights above, here are other exciting announcements from Microsoft Build:

Data Encryption at Rest with Customer Managed Keys (CMK) in Azure Key Vault, providing complete control over log data access with key revocation. Available only when using dedicated clusters with capacity reservation of more than 1TB/day.
Out-of-the-box support for Distributed Tracing in Java Azure Functions, providing richer data pertaining to requests, dependencies, logs, and metrics.
Application Insights Codeless Attach for Node.JS Apps on Azure App Services (Linux) with automatic dependency collection.
Notifications with enhanced visibility on all Azure resource changes across subscriptions with application change analysis.

Next steps with Azure Monitor

To learn more about Azure Monitor and monitoring best practices, check out the documentation and recorded sessions from our recent virtual series. If you have any questions or suggestions, reach out to us through our Tech Community forum.
Quelle: Azure