Operations | Monitoring | ITSM | DevOps | Cloud

The concept of Round Robin comes from sports. And it has nothing to do with anyone called Robin, but the french word ruban (ribbon). In a Round Robin tournament, all participants face each other by taking turns. When applied to on-call schedules, a Round Robin escalation policy means that responders assigned to a level will take turns responding to alerts. When is this strategy useful and when isn’t?

According to Amazon, EC2 Spot Instances can save you up to 90% of your spending on On-Demand Instances. It’s been proven that Reserved EC2 instances are cheaper than On-Demand ones. But, Spot Instances can offer even bigger discounts. Despite these cost benefits, Spot Instances have many caveats that make them unsuitable for some operations and processes. Even Amazon emphasizes this. Although this EC2 can save you a lot of money, some situations are best for On-Demand or Reserved Instances.

In today's competitive and uncertain tech landscape, engineering organizations are constantly seeking ways to optimize costs without compromising on performance. Efficient resource management and cost reduction have become crucial for businesses aiming to stay alive and ahead. At Cycle, our goal is to offer a robust solution that enhances efficiency while delivering significant cost savings to our users.

June was a hot month — both in terms of the temperature in North America and because of the FinOps news that has been rolling down the pipeline! (Around here, there’s always excitement in the air when a major FinOps event goes off without a hitch.)

March 5th was a big day for incident.io as we released our on-call product to the world. Nine months of listening to our customers, coding, fixing, testing, and polishing came together for our biggest product launch to date. Releasing On-call was a huge milestone and represented the next step in our journey as a company.

Amazon Web Services (AWS) is renowned for providing a comprehensive ecosystem that supports the computational and data storage needs essential for developing, deploying, and managing applications across different regions, ensuring that users experience fast and seamless service.

Reliability testing and observability are similar in one important way: engineering teams know they should be doing it, but they’re not sure how to start, or they don’t have the right resources, or they need to focus on competing priorities like feature development and incident response. In an ideal world, reliability and observability would be automated processes that configure, monitor, and run themselves.

Kubernetes has several components that produce logs and events containing information on everything that has happened in a Kubernetes cluster. Keeping track of all this data becomes extremely challenging when you run Kubernetes at a very large scale. With so many components generating logs, organizations need a centralized place to see it all. But this is only half your problem. You also need to correlate logs coming from different components to draw the right conclusions and take effective actions.