Operations | Monitoring | ITSM | DevOps | Cloud

Troubleshooting issues in a complex service mesh environment, such as traffic failures or authorization problems, often requires the expertise of an SRE or DevOps professional. However, Komodor simplifies this process. Komodor provides developers with the necessary visibility to diagnose service mesh issues on their own. It helps developers easily identify blocked connections and understand the root cause without having to review logs or configuration files.

Modular architecture: Decouple compute from storage so each can scale independently. This makes it easier to adapt to growing or shifting workloads over time. Future-ready hardware: Select GPUs and CPUs not just for current workloads but with an eye on scalability, including support for newer accelerator types. Scalable design: Ensure the system allows seamless addition of compute nodes or storage without a full redesign.

Storage is often underestimated: In infrastructure discussions, compute and networking get most of the attention, while storage is treated as secondary. For AI workloads, that can be a costly oversight. Data throughput for specialized hardware: AI infrastructure powered by GPUs can process massive volumes of data at unprecedented speeds. This puts immense pressure on the storage system to keep up. Scale-out performance: An on-prem, scale-out, software-defined storage setup allows you to meet high performance demands, grow capacity as needed, and stay in control of infrastructure costs.

Learn more about the OPEA project from @LinuxfoundationOrg with Katherine Druckman from Intel.

Rob Zuber sits down with Tara Hernandez, VP of Developer Productivity at MongoDB and former Netscape engineer who helped create early continuous integration systems, to explore strategic frameworks for build vs. buy decisions in modern software delivery.