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Mark Russinovich

Mark Russinovich

Chief Technology Officer and Technical Fellow, Microsoft Azure

Latest posts

Showing 11 – 20 of 61 posts found

Advancing anomaly detection with AIOps—introducing AiDice 

We introduce AiDice, a novel anomaly detection algorithm developed jointly by Microsoft Research and Microsoft Azure that identifies anomalies in large-scale, multi-dimensional time series data. AiDice captures incidents quickly and provides engineers with important context that helps them diagnose issues more effectively, providing the best experience possible for end customers.

Published • 4 min read

Azure confidential computing with NVIDIA GPUs for trustworthy AI 

Confidential computing technology encrypts data in memory and only processes it once the cloud environment is verified, helping protect data from cloud operators, malicious admins, and privileged software. Today, we are excited to announce the next chapter in a strategic partnership between NVIDIA and Microsoft that brings confidential computing to state-of-the-art NVIDIA GPUs.

Published • 6 min read

Advancing Azure Virtual Machine availability monitoring with Project Flash 

Today, we’re excited to announce the completion of the project’s first two milestones—the preview of VM availability data in Azure Resource Graph, and the private preview of a VM availability metric in Azure Monitor.

Published • 5 min read

Advancing service resilience in Azure Active Directory with its backup authentication service 

The most critical promise of our identity services is ensuring that every user can access the apps and services they need without interruption. We’ve been strengthening this promise to you through a multi-layered approach, leading to our improved promise of 99.99 percent authentication uptime for Azure Active Directory (Azure AD).

Published • 6 min read

Key foundations for protecting your data with Azure confidential computing 

The exponential growth of datasets has resulted in growing scrutiny of how data is exposed—both from a consumer data privacy and compliance perspective. In this context, confidential computing becomes an important tool to help organizations meet their privacy and security needs surrounding business and consumer data.

Published • 6 min read

Advancing reliability through a resilient cloud supply chain 

Microsoft’s cloud supply chain is essential to deliver the infrastructure—servers, storage, and networking gear—that enables cloud reliability and growth. Our vision is for cloud capacity to be available like a utility so that customers can seamlessly turn it on when and where they need it.

Published • 6 min read

Advancing Azure Virtual Machine availability transparency 

Now, in addition to getting a fast notification when a VM’s availability is impacted, customers can expect a root cause to be added at a later point once our automated Root Cause Analysis (RCA) system identifies the failing Azure platform component that led to the VM failure.

Published • 6 min read

Advancing application reliability with the Azure Well-Architected Framework 

We created the Azure Well-Architected Framework to help improve the quality of your workloads, and reliability is one of its five core pillars so for the latest post in our series, I have asked Cloud Advocate David Blank-Edelman to run through how best to approach using the framework to guide your conversations and design decisions in this space.

Published • 10 min read

Advancing resiliency threat modeling for large distributed systems 

All service engineering teams in Azure are already familiar with postmortems as a tool for better understanding what went wrong, how it went wrong, and the customer impact of the related outage. For today’s post in our Advancing Reliability blog series, we share insights into our journey as we work towards advancing our postmortem and resiliency threat modeling processes.

Published • 7 min read

Advancing safe deployment with AIOps—introducing Gandalf 

The continuous monitoring of health metrics is a fundamental part of this process, and this is where AIOps plays a critical role. In the post that follows, we introduce how AI and machine learning are used to empower DevOps engineers, monitor the Azure deployment process at scale, detect issues early, and make rollout or rollback decisions based on impact scope and severity.