Tag: ms azure hosting provider

Microsoft Azure Cloud Services, Uncategorized

Advancing Azure service quality with artificial intelligence: AIOps

We are going to share our vision on the importance of infusing AI into our cloud platform and DevOps process. Gartner referred to something similar as AIOps (pronounced “AI Ops”) and this has become the common term that we use internally, albeit with a larger scope. Today’s post is just the start, as we intend to provide regular updates to share our adoption stories of using AI technologies to support how we build and operate Azure at scale.

Why AIOps?

There are two unique characteristics of cloud services:

  • The ever-increasing scale and complexity of the cloud platform and systems
  • The ever-changing needs of customers, partners, and their workloads

To build and operate reliable cloud services during this constant state of flux, and to do so as efficiently and effectively as possible, our cloud engineers (including thousands of Azure developers, operations engineers, customer support engineers, and program managers) heavily rely on data to make decisions and take actions. Furthermore, many of these decisions and actions need to be executed automatically as an integral part of our cloud services or our DevOps processes. Streamlining the path from data to decisions to actions involves identifying patterns in the data, reasoning, and making predictions based on historical data, then recommending or even taking actions based on the insights derived from all that underlying data.

 Infusing AI into cloud platform and DevOps – with AI at the center of Customers, Engineering, and Services.
Figure 1. Infusing AI into cloud platform and DevOps.

The AIOps vision

AIOps has started to transform the cloud business by improving service quality and customer experience at scale while boosting engineers’ productivity with intelligent tools, driving continuous cost optimization, and ultimately improving the reliability, performance, and efficiency of the platform itself. When we invest in advancing AIOps and related technologies, we see this ultimately provides value in several ways:

  • Higher service quality and efficiency: Cloud services will have built-in capabilities of self-monitoring, self-adapting, and self-healing, all with minimal human intervention. Platform-level automation powered by such intelligence will improve service quality (including reliability, and availability, and performance), and service efficiency to deliver the best possible customer experience.
  • Higher DevOps productivity: With the automation power of AI and ML, engineers are released from the toil of investigating repeated issues, manually operating and supporting their services, and can instead focus on solving new problems, building new functionality, and work that more directly impacts the customer and partner experience. In practice, AIOps empowers developers and engineers with insights to avoid looking at raw data, thereby improving engineer productivity.
  • Higher customer satisfaction: AIOps solutions play a critical role in enabling customers to use, maintain, and troubleshoot their workloads on top of our cloud services as easily as possible. We endeavor to use AIOps to understand customer needs better, in some cases to identify potential pain points and proactively reach out as needed. Data-driven insights into customer workload behavior could flag when Microsoft or the customer needs to take action to prevent issues or apply workarounds. Ultimately, the goal is to improve satisfaction by quickly identifying, mitigating, and fixing issues.

 

AI for Cloud: AI Ops and AI-Serving Platform showing example use cases in AI for Systems, AI for DevOps, and AI for Customers.

Figure 2. AI for Cloud: AIOps and AI-Serving Platform.

AIOps

Moving beyond our vision, we wanted to start by briefly summarizing our general methodology for building AIOps solutions. A solution in this space always starts with data—measurements of systems, customers, and processes—as the key of any AIOps solution is distilling insights about system behavior, customer behaviors, and DevOps artifacts and processes. The insights could include identifying a problem that is happening now (detect), why it’s happening (diagnose), what will happen in the future (predict), and how to improve (optimize, adjust, and mitigate). Such insights should always be associated with business metrics—customer satisfaction, system quality, and DevOps productivity—and drive actions in line with prioritization determined by the business impact. The actions will also be fed back into the system and process. This feedback could be fully automated (infused into the system) or with humans in the loop (infused into the DevOps process). This overall methodology guided us to build AIOps solutions in three pillars.

AIOps methodologies: Data (Customer/System/DevOps), insights (Detect/Diagnose/Predict/Optimize), and actions (Mitigate/Avert future pain/Optimize usage config/Improve architecture & process).
Figure 3. AIOps methodologies: Data, insights, and actions.

AI for systems

Today, we’re introducing several AIOps solutions that are already in use and supporting Azure behind the scenes. The goal is to automate system management to reduce human intervention. As a result, this helps to reduce operational costs, improve system efficiency, and increase customer satisfaction. These solutions have already contributed significantly to the Azure platform availability improvements, especially for Azure IaaS virtual machines (VMs). AIOps solutions contributed in several ways including protecting customers’ workload from host failures through hardware failure prediction and proactive actions like live migration and Project Tardigrade and pre-provisioning VMs to shorten VM creation time.

Of course, engineering improvements and ongoing system innovation also play important roles in the continuous improvement of platform reliability.

  • Hardware Failure Prediction is to protect cloud customers from interruptions caused by hardware failures.  Microsoft Research and Azure have built a disk failure prediction solution for Azure Compute, triggering the live migration of customer VMs from predicted-to-fail nodes to healthy nodes. We also expanded the prediction to other types of hardware issues including memory and networking router failures. This enables us to perform predictive maintenance for better availability.
  • Pre-Provisioning Service in Azure brings VM deployment reliability and latency benefits by creating pre-provisioned VMs. Pre-provisioned VMs are pre-created and partially configured VMs ahead of customer requests for VMs. As we described in the IJCAI 2020 publication, As we described in the AAAI-20 keynote mentioned above,  the Pre-Provisioning Service leverages a prediction engine to predict VM configurations and the number of VMs per configuration to pre-create. This prediction engine applies dynamic models that are trained based on historical and current deployment behaviors and predicts future deployments. Pre-Provisioning Service uses this prediction to create and manage VM pools per VM configuration. Pre-Provisioning Service resizes the pool of VMs by destroying or adding VMs as prescribed by the latest predictions. Once a VM matching the customer’s request is identified, the VM is assigned from the pre-created pool to the customer’s subscription.

AI for DevOps

AI can boost engineering productivity and help in shipping high-quality services with speed. Below are a few examples of AI for DevOps solutions.

  • Incident management is an important aspect of cloud service management—identifying and mitigating rare but inevitable platform outages. A typical incident management procedure consists of multiple stages including detection, engagement, and mitigation stages. Time spent in each stage is used as a Key Performance Indicator (KPI) to measure and drive rapid issue resolution. KPIs include time to detect (TTD), time to engage (TTE), and time to mitigate (TTM).

 Incident management procedures including Time to Detect (TTD), Time to Engage (TTE), and Time to Mitigate (TTM).
Figure 4. Incident management procedures.

As shared in AIOps Innovations in Incident Management for Cloud Services at the AAAI-20 conference, we have developed AI-based solutions that enable engineers not only to detect issues early but also to identify the right team(s) to engage and therefore mitigate as quickly as possible. Tight integration into the platform enables end-to-end touchless mitigation for some scenarios, which considerably reduces customer impact and therefore improves the overall customer experience.

  • Anomaly Detection provides an end-to-end monitoring and anomaly detection solution for Azure IaaS. The detection solution targets a broad spectrum of anomaly patterns that includes not only generic patterns defined by thresholds, but also patterns which are typically more difficult to detect such as leaking patterns (for example, memory leaks) and emerging patterns (not a spike, but increasing with fluctuations over a longer term). Insights generated by the anomaly detection solutions are injected into the existing Azure DevOps platform and processes, for example, alerting through the telemetry platform, incident management platform, and, in some cases, triggering automated communications to impacted customers. This helps us detect issues as early as possible.

For an example that has already made its way into a customer-facing feature, Dynamic Threshold is an ML-based anomaly detection model. It is a feature of Azure Monitor used through the Azure portal or through the ARM API. Dynamic Threshold allows users to tune their detection sensitivity, including specifying how many violation points will trigger a monitoring alert.

  • Safe Deployment serves as an intelligent global “watchdog” for the safe rollout of Azure infrastructure components. We built a system, code name Gandalf, that analyzes temporal and spatial correlation to capture latent issues that happened hours or even days after the rollout. This helps to identify suspicious rollouts (during a sea of ongoing rollouts), which is common for Azure scenarios, and helps prevent the issue propagating and therefore prevents impact to additional customers.

AI for customers

To improve the Azure customer experience, we have been developing AI solutions to power the full lifecycle of customer management. For example, a decision support system has been developed to guide customers towards the best selection of support resources by leveraging the customer’s service selection and verbatim summary of the problem experienced. This helps shorten the time it takes to get customers and partners the right guidance and support that they need.

AI-serving platform

To achieve greater efficiencies in managing a global-scale cloud, we have been investing in building systems that support using AI to optimize cloud resource usage and therefore the customer experience. One example is Resource Central (RC), an AI-serving platform for Azure that we described in Communications of the ACM. It collects telemetry from Azure containers and servers, learns from their prior behaviors, and, when requested, produces predictions of their future behaviors. We are already using RC to predict many characteristics of Azure Compute workloads accurately, including resource procurement and allocation, all of which helps to improve system performance and efficiency.

Looking towards the future

We have shared our vision of AI infusion into the Azure platform and our DevOps processes and highlighted several solutions that are already in use to improve service quality across a range of areas. Look to us to share more details of our internal AI and ML solutions for even more intelligent cloud management in the future. We’re confident that these are the right investment solutions to improve our effectiveness and efficiency as a cloud provider, including improving the reliability and performance of the Azure platform itself.

 

Note blog reference: https://azure.microsoft.com/en-in/blog/advancing-azure-service-quality-with-artificial-intelligence-aiops/

Microsoft Azure Cloud Services

THE TOP 10 REASONS WHY SMBs SHOULD INVEST IN THE CLOUD

Small to medium-sized businesses (SMBs) are lagging behind their enterprise counterparts when it comes to cloud adoption. With the new year (and new decade) fast underway, a recent Microsoft study showed that more than 96% of enterprises are using the cloud, compared to only 78% for SMBs. And while the use of cloud-based productivity apps like Office 365 has steadily grown among these smaller companies, their continued reliance on legacy software in key business applications such as ERP or accounting is impeding them from competing effectively with today’s top players.

Given this situation, moving to the cloud should be an obvious priority for SMBs, but many myths and misconceptions still exist regarding the benefits of cloud technology. Below are the ten most crucial and game-changing benefits that SMBs have reported after investing in cloud solutions.

1 –  Greater profit and ROI

Simply put, companies that move to the cloud make more money. And not by a small percentage, either. SMBs that invest in the cloud report up to 25% growth in revenue and up to 2x the profits over those who don’t. Embracing the cloud is simply a better path to faster growth. Additionally, cloud deployments provide a greater return on investment (ROI) than traditional on-premises software projects, especially in ERP and CRM. For example, Nucleus Research determined that companies that use Microsoft Dynamics 365 see a return of $16.97 for every $1 spent. That’s well above the average for on-premise ERP and CRM applications.

2 – Lower costs and CapEx

Cloud subscription models eliminate up-front capital expenditures (CapEx) like the high cost of hardware and software licenses for projects like ERP software implementations. They also eliminate server and infrastructure setup, update, and maintenance fees—not to mention the resources saved on software upgrades, energy costs, and underutilized computing resources

3 – Unparalleled business flexibility

Cloud software allows small businesses to remain always-on regardless of location. In today’s mobile and cloud-first world, the ability to be productive on any phone, tablet, or laptop provides the flexibility required to quickly adapt to changing information and business needs. This means more agile operations and happier customers

4 – Faster IT innovation

The hassle and cost of routine IT maintenance tasks can be effectively offloaded to the cloud, enabling IT resources to focus on more strategic tasks like addressing problems, improving user experiences, fostering user adoption and best practices, and getting more value out of systems and processes

5 – Seamless, automatic software updates

With cloud computing, all software updates are handled automatically, so critical systems always have the latest functionality and security features. This effectively ensures that all the benefits of a vendor’s ongoing R&D nvestments are transferred to their customer’s business, without that business having to dedicate any time or additional resources

6 – Cost-effective scalability

SMBs need increased flexibility to grow and scale without hassle. With the cloud, as an SMB adds users, generates more transactions, or adds more data, services dynamically scale to manage the workload. This eliminates the need to pay for more hardware or maintenance to support business growth. As a bonus, SMBs only use the energy they need for their cloud apps. Since servers aren’t running idle waiting to be utilized, operations become more energy efficient, reducing the carbon footprint of the business.

7 – Improved collaboration and productivity

Digital, cloud-based workspaces offer the opportunity to collaborate more effectively and remove data silos to enable greater employee productivity. Additionally, cloud-based office productivity suites and all-in-one business management solutions possess integration capabilities that simply can’t be matched by on-premises software.

Cloud computing also allows teams to be productive, regardless of their location. This enables businesses to offer flexible working arrangements that create a healthier work/life balance and happier employees without sacrificing productivity.

8 – Seamless software integration

Cloud applications are typically compatible with Application Programming Interfaces (APIs) that simplify integration, while automation tools like Microsoft Flow facilitate stitching them together without any custom code. Data and systems can be connected like never before, resulting in new levels of speed and efficiency.

9 – Superior security and data protection

Small businesses are the most common victims of security breaches. In a recent study by ComScore, over 40% of small businesses were worried about data security before moving to the cloud. After making the switch, 94% of businesses reported security benefits they had been unable to achieve with their previous on-premises resources.

Furthermore, physical hardware protection has always been a challenge for SMBs. Laptops get lost or stolen all the time. In addition to the replacement costs, there is the even greater cost of losing important or sensitive data. When storing and backing up data in the cloud, however, data is available and protected regardless of what happens to personal devices.

10 – Increased competitiveness

Moving to the cloud gives SMBs access to enterprise-class technologies that were previously only available to the industry’s top players. With the cloud, any business can run on the exact same systems used by the largest, most sophisticated companies in the world, enabling them to innovate and act faster than competitors that manage on-premises legacy systems.

In conclusion, with cloud software now available that is purpose-built for SMBs to run their sales, marketing, service, accounting, operations, supply chain, and project management activities—all from a single, connected solution infused with AI and advanced analytics—there’s never been a better time for small and medium-sized businesses to make the move to the cloud.

Connect with our cloud expert for any query or requirement at –  info@tridentinfo.com

Microsoft Azure Cloud Services

Plan migration of physical servers using Azure Migrate

Previously, Azure Migrate: Server Assessment only supported VMware and Hyper-V virtual machine assessments for migration to Azure. At Ignite 2019, we added physical server support for assessment features like Azure suitability analysis, migration cost planning, performance-based rightsizing, and application dependency analysis. You can now plan at-scale, assessing up to 35K physical servers in one Azure Migrate project. If you use VMware or Hyper-V as well, you can discover and assess both physical and virtual servers in the same project. You can create groups of servers, assess by group and refine the groups further using application dependency information.

While this feature is in preview, the preview is covered by customer support and can be used for production workloads. Let us look at how the assessment helps you plan migration.

clip_image002

Azure suitability analysis

The assessment checks Azure support for each server discovered and determines whether the server can be migrated as-is to Azure. If incompatibilities are found, remediation guidance is automatically provided. You can customize your assessment by changing its properties, and recomputing the assessment. Among other customizations, you can choose a virtual machine series of your choice and specify the uptime of the workloads you will run in Azure.

Cost estimation and sizing

Assessment also provides detailed cost estimates. Performance-based rightsizing assessments can be used to optimize on cost; the performance data of your on-premise server is used to recommend a suitable Azure Virtual Machine and disk SKU. This helps to optimize on cost and right-size as you migrate servers that might be over-provisioned in your on-premise data center. You can apply subscription offers and Reserved Instance pricing on the cost estimates.

clip_image004

Dependency analysis

Once you have established cost estimates and migration readiness, you can plan your migration phases. Using the dependency analysis feature, you can understand which workloads are interdependent and need to be migrated together. This also helps ensure you do not leave critical elements behind on-premise. You can visualize the dependencies in a map or extract the dependency data in a tabular format. You can divide your servers into groups and refine the groups for migration by reviewing the dependencies.clip_image006

Assess your physical servers in four simple steps

  • Create an Azure Migrate project and add the Server Assessment solution to the project.
  • Set up the Azure Migrate appliance and start discovery of your server. To set up discovery, the server names or IP addresses are required. Each appliance supports discovery of 250 servers. You can set up more than one appliance if required.
  • Once you have successfully set up discovery, create assessments and review the assessment reports.
  • Use the application dependency analysis features to create and refine server groups to phase your migration.

When you are ready to migrate the servers to Azure, you can use Server Migration to carry out the migration, get in touch with us our team will help you.

Translate »