Operations | Monitoring | ITSM | DevOps | Cloud

Imagine you are building an e-commerce app. Every time a customer places an order, a lot happens behind the scenes. For example, you need to charge their card, update inventory, create a shipping label, and send a confirmation email. You could try to write one giant program that does everything in the correct order, but that quickly becomes a tangled mess — especially if something fails halfway through (say, payment succeeds but inventory update fails).

For years, enterprises have turned to Oracle Cloud Infrastructure (OCI) for what it does best: powering mission-critical applications with unmatched performance, security, and predictable economics. OCI has historically staked its reputation on being the go-to platform for organizations running complex, data-intensive workloads, from core databases and ERP systems to large-scale compute clusters, while putting extra focus on security and predictable pricing.

The Problem: AI workloads are dynamic, unpredictable, and expensive. Data prep can choke your pipeline, training jobs hog GPUs without awareness, and inference, the most latency-sensitive phase, is notoriously hard to scale efficiently. Worse, traditional infrastructure tools treat GPU as a static commodity, ignoring model intent, workload shape, and sharing capabilities.

Getting started with monitoring for Microsoft Azure environments can be a lengthy and manual process. Many tools require users to create app registrations, assign permissions, and enable log forwarding or telemetry data collection across multiple portals and scripts. These fragmented steps slow down onboarding and introduce opportunities for misconfiguration, making it harder for teams to quickly achieve full visibility.

On Oct. 20, the Internet woke up and seemingly chose violence. For more than 12 hours, Amazon Web Services (AWS) went down. From banking platforms to hospital communications to mobile ordering apps, digital services came to a screeching halt. The cause? Two programs are trying to write a DNS entry simultaneously, failing, and leaving the entry blank. Thus began the incredibly costly failure cascade.

Before 2022, Alex Hanna worked on Google’s Ethical AI team. Today, she’s the director of research at the Distributed AI Research Institute, a transition sparked by Google’s handling of a paper exposing AI’s growing environmental footprint. So, how bad is it, really? That depends on who you ask. Take Jesse Dodge, a senior research analyst at the Allen Institute for AI. Jesse told NPR that a single ChatGPT query can use as much electricity as keeping a light bulb on for 20 minutes.

Almost all companies have some kind of online presence, and if yours doesn’t, it definitely should. In order to make a splash online, your business needs content, webpages, photos, documents, PDFs, videos, you name it. How do you keep track of all the various media? Where do you store it and back it up? What’s the most convenient and safe way to collaborate and share it with employees and freelancers? Enter all-in-one cloud storage services for businesses like Dropbox and Box.

Cloud network monitoring software is a type of software designed to monitor and manage the performance, availability, and security of networks and network devices in cloud environments. These tools use various techniques to gather information about network traffic, bandwidth utilization, application performance, and other metrics related to network health and availability.