Operations | Monitoring | ITSM | DevOps | Cloud

Virtual machines (VMs) are one of the on-demand, scalable computing resources that Microsoft Azure offers. They enable streamlined allocation of resources in backup, disaster recovery, and deployment environments. A VM is typically chosen when you need more control over your computing environment. However, Azure VMs come with the challenge of simultaneously monitoring them across multiple environments when the VMs are spread unevenly, with some being overutilized and others underutilized.

Over the past decade, organizations have been moving to a hybrid cloud model. A hybrid cloud is a form of cloud computing where applications are deployed on a combination of public clouds and private infrastructure. With a hybrid cloud, organizations can choose the most optimal deployment strategy for their applications while maintaining control over certain aspects, such as running user-facing services from a public cloud while storing sensitive data on-premise.

Organizations around the world are increasingly relying on the cloud to capitalize on its speed, ease of management and scalability, and the business value it provides to transform and grow their business. It’s an ever-growing market that is currently estimated at 266.4 billion dollars—a whopping 982.9 percent increase in growth compared to a decade ago when it was worth a little over 24.6 billion dollars.

Serverless computing has taken off in recent years as engineering organizations have shied away from the complexity and cost that comes with managing physical servers and even virtual machines hosted on a cloud like AWS. If you are interested in serverless options on AWS, you should be comparing Lambda and ECS Fargate to figure out what fits your use case best. In this blog post we will compare these two AWS services in the following areas...

Chaos Engineering was conceived as a direct response to the complexity and nondeterministic nature of cloud-based applications. Thoughtful fault injection closes the gap between traditional testing methodologies and modern approaches to software engineering like microservices, continuous delivery, and DevOps.

Today, the AWS Lambda platform has added a new arrow to its quiver – the ability to integrate with Amazon Elastic File System (EFS) natively. Until now, a Lambda function was limited to 512MB of /tmp directory storage. While this is sufficient for most use cases, it’s often prohibitive for use cases such as Machine Learning, as Tensorflow models are often GBs in size and cannot fit into the limited /tmp storage.

There are many challenges that engineering teams face when attempting to incorporate a multi-cloud approach into their infrastructure goals. Kubernetes does a good job of addressing some of these issues, but managing the communication of clusters that span multiple cloud providers in multiple regions can become a daunting task for teams. Often this requires complex VPNs and special firewall rules to multi-cloud cluster communication.

Most of the IT industry is floating on the cloud these days. From hosting websites to storing data, organizations have started to use the cloud for various purposes, so why not for log monitoring? In this blog, I’ll explain how cloud-based log monitoring is beneficial for you and how you can use SolarWinds® Papertrail™ for it.