Operations | Monitoring | ITSM | DevOps | Cloud

If data centres were a country, they’d rank fifth in electricity consumption by 2026. Over the past few years, the resulting carbon footprint of the technology industry has sparked the fast-growing green software movement, led by the Green Software Foundation. How can we continue to innovate software in a way that also minimises its impact on the environment? This has been a fascinating problem I’ve been exploring for a few years now.

VictoriaMetrics has achieved Red Hat OpenShift Certification, awarded to Red Hat partners who meet requirements for delivering a scalable, supported, and secure operator designed for enterprise cloud deployments. VictoriaMetrics available on the Red Hat OpenShift OperatorHub The program certified VictoriaMetrics as a solution that allows for portability and operational efficiency across hybrid and multi-cloud environments.

AI in asset management is redefining how organizations add validate and govern assets in 2026. What was once a slow manual and error prone process is now becoming intelligent automated and highly accurate. As enterprises scale across locations and asset types the pressure to maintain clean asset data from day one has increased dramatically. This is where AI in asset management is making a measurable impact. In the first hundred words itself it is clear that AI in asset management is no longer optional.

In 2015, only about 40% of websites used HTTPS. Today HTTPS is used over 95% of the time. The ACME protocol made that shift possible. The Automatic Certificate Management Environment (ACME) protocol enables software to automatically prove domain control to a certificate authority without any human involvement. No more generating CSRs by hand. No more copy-pasting into web forms. No more waiting for validation emails. ACME largely solved certificate issuance.

AI agents are fundamentally different beasts to monitor compared to traditional applications. A single user request can trigger a cascade of 10+ internal operations: sub-agent transfers, tool executions, LLM calls, API requests, each with unpredictable latency and failure modes. When something goes wrong (and with LLMs, things go wrong in creative ways), you need to see the entire execution flow to debug effectively.

Broken checkouts lead to lost transactions, drain revenue, undermine customer trust, and damage brand credibility. Unfortunately, most companies don't realize their checkout is failing until sales drop or customers start complaining. According to statistics, technical issues cause checkout abandonment in at least 17% of cases. This means nearly one-fifth of lost conversions are preventable. For any online business, even a small checkout failure can result in significant revenue loss.

Today’s higher education institutions operate complex digital ecosystems that were unimaginable a decade ago. Behind every college lies a portal of interconnected systems for registration, financial aid, course management, and campus services. The students using those systems are digital natives who can order food in seconds on their phones or have packages delivered the same day they order them.

You’ve optimized your code, implemented caching strategies, and configured your CDN perfectly. Your analytics dashboard shows respectable load times, and your development team reports everything is running smoothly. Yet, conversion rates remain stagnant, bounce rates climb during peak hours, and your competitors consistently outperform you in user experience metrics. What’s missing?

This blog is based on Haylee Millar's talk at the Symfony 2024 conference. Haley is a Product Engineer at Upsun. We utilized AI tools for transcription and to enhance the structure and clarity of the content. When faced with an aging system that needs new features, many development teams find themselves at a crossroads. Do you patch the old system and risk technical debt, or do you take the leap into microservices architecture?