Operations | Monitoring | ITSM | DevOps | Cloud

Artificial intelligence is certainly a hot topic right now, but what does it mean for the networking industry? In this post, Phil Gervasi looks at the role of AI and LLM in networking and separates the hype from the reality.

Microservices architecture has become increasingly popular in modern software development due to its scalability, resilience, and flexibility. However, with the benefits of microservices come the challenges of debugging and monitoring these distributed systems. Using the Istio service mesh, OpenTelemetry distributed tracing, and Apica’s Kubernetes-native observability platform, developers can easily collect and visualize performance data in real-time to identify and fix issues quickly.

Azure Service Bus, a cloud-based messaging service, facilitates communication between applications and services. Within Azure Service Bus queues and topic subscriptions, there exists an additional sub-queue known as a Azure Service Bus dead-letter queue (DLQ). The Dead-Letter Queue (DLQ) serves as a storage area for messages that the receiving application from the main queue cannot deliver or process successfully. These messages are referred to as “dead-lettered” messages.

Scaling to collect Windows Event logs with the Windows Event Forwarding Source can be tricky. Luckily, you can use a load balancer, and with some math to scale the number of workers to collect the amount of data you expect, you can use workers to collect Windows logs from a large number of endpoints. Endpoint logs are the lifeblood of observability in an incident response program.

Currently, there are about 1.13 billion websites around the world. Actually, 3 new websites are created every second. Even more so, it has become particularly powerful to pair a website with an ecommerce platform, thereby broadening customer reach and 24/7 accessibility. And this global e-commerce market is expected to total a whopping$6.3 trillion in 2023. 79% of shoppers already shop online at least once a month.

Artificial Intelligence (AI) is surely revolutionizing numerous industries. The AI market is projected to grow from $150 billion in 2023 to $1,345 billion in 2030, at a whopping 36.8% Compound Annual Growth Rate (CAGR). And at least 35% of companies are already using AI in their business, and an additional 42% are exploring it. However, the exhaustive list of AI business applications is still in the early stages.

Up until recently, machines mainly focused on analyzing large, existing amounts of data and finding patterns for a multitude of use cases. This is called “traditional AI.” But lately, machines have also started creating new content. And this is now known as “generative AI.” And given the rise of ChatGPT and its peers, generative artificial intelligence (AI) has quickly emerged as one of the most transformative technologies in recent years.

Operating Kubernetes reliably and efficiently involves adhering to a set of best practices. These practices help ensure the stability, scalability and maintainability of your Kubernetes clusters and their applications. It's crucial for platform teams (responsible for the infrastructure) and software development teams (responsible for deploying applications) to work together in applying these practices.