Operations | Monitoring | ITSM | DevOps | Cloud

A 503 status code reveals an issue that typically appears when the site’s server is not reachable. The 2 main reasons are that the server is down for maintenance or that is overloaded.

A lot of things have changed in recent years. From the way of working to executing IT operations, the business strategies have changed overnight with arising advances like Machine Learning, Automation, and Artificial intelligence. The technologies have changed present-day applications and IT operations, and with AI and ML on board, IT industries operate more perplexing undertakings and resolve issues across complex infrastructures.

AIOps or Artificial Intelligence for IT Operations refers to a set of technologies that augment human decisions with autonomous decisions driven by AI and machine learning that learn patterns, relationships from data. AIOps is the term originally coined by Gartner, and pictorially illustrated in the following way.

Engineers who support production environments are tasked with resolving new issues as quickly and efficiently as possible. But as they look to carry out these responsibilities, their remediation workflows tend to take on the following pattern: For example, someone on your team might discover in a log analysis tool that a user is flooding a key service by making an abnormal number of requests.

Many organizations leverage AWS to build fully managed, event-driven applications, which break down complex workloads into APIs, event streams, and other decentralized services in order to improve performance and scalability. This type of architecture relies primarily on AWS Lambda functions to process synchronous and asynchronous requests as they move between a workload’s resources, such as Amazon API Gateway and Amazon Kinesis.

Over the last couple years, cloud transformation has become increasingly critical, evolving from a preferable priority to an urgent imperative. In our rapidly changing world, organizations have had to innovate at unprecedented rates — and those most successful are harnessing the power of cloud to move faster and smarter. But it’s more than a simple migration.

Before continuous integration came to be, setting up builds was no fun because the complexity and overhead involved in a release cycle was compounded by inflexible, manual processes. The release cycle was slow and often resulted in breaking changes. Continuous integration and continuous delivery (CI/CD) has changed much of that through pipelines that automate how we build and test software—today, we can deploy, have builds fail, and resolve any errors faster than ever.

In an earlier blog post, we had discussed how server performance monitoring is not just about monitoring CPU, memory, and disk resources anymore. There is more to server performance monitoring than just three resources or metrics. That blog post covered several key performance indicators (KPIs) that IT teams must track to ensure that their servers are performing well. In this blog post, we focus on another KPI – server uptime.