Welcome everyone. My name is, and I'm excited to talk to you today about network ion harnessing EBPF for Cloud-native observability superpowers. We are embarking on a journey to explore how this powerful technology EBPF is fundamentally changing how we observe and understand our cloud native environments, essentially granting US observability superpowers. As organizations increasingly adapt containerization and microservices, particularly with Kubernetes, the networking layer that invisible mesh connecting everything becomes incredibly complex. It often turns into a critical blind spot for operations and SRE teams, we find that standard monitoring tech tools frequently struggle to provide the necessary depth and real time insight required in these highly dynamic systems. This talk aims to demystify EBPF, which stands for Extended Berkeley Packet Filter, and demonstrate how it provides deep surgical visibility directly from the Lenux kernel itself. A key advantage will explore is how it achieves this level of detail without imposing the typical performance overhead. We often associate it with traditional methods like deep packet inspection or extremely verbal s logging. Over the next 20 to 25 minutes, my goal is to bridge the gap between this powerful low level kernel technology and the practical actionable strategies you can implement in your own environments. Let's f first clearly define the problem we are addressing the Kubernetes networking blind spot in cloud native architectures, especially when employing service measures are dealing with hundreds of microservices. The communications pathway between them become incredibly intricate. These invisible connections and complex dependencies are often dynamic and not fully captured by static configurations or even infrastructure as code definitions. This is where traditional monitoring approaches often fall short. Many rely on sampling, grabbing data points intermittently or aggregating metrics over time. While useful for high level trends, this inherently loses the fine grain detail needed to understand transient issues or the exact sequence of events during network problem. This leads to limited visibility. Operation teams are left grappling with troubleshooting challenges, trying to diagnose complex distributed issues without detailed realtime insights into the actual network traffic flow between parts, services and nodes. It's like trying to debug a tangle mess of wire with the lights partially off. So what is EBPF, how it helps us see through this complexity? EBPF stands for Extended Berkeley packet filter. While the original EBPF was primarily for filtering network packets, EBPF is a revolutionary technology that acts like a lightweight. Sandboxed virtual machine inside the Linux kernel itself. This allows us to run custom event driven programs whenever specific kernel event suckers like network packets being processed, system calls being made, or kernel functions being entered or exited. This kernel level integration is key. We are getting data right from the source. The extended part signifies its program loyalty. Going far beyond simple filtering. This enables surgical precision because EBPF programs run in the kernel context, they can access data structures and react to events with minimal overhead. Capturing detailed information at microsecond resolution without relying on sampling, think specific protocol details, latency measurements for individual requests or tracking. Specific system calls related to network activity, all without the heavy cost of traditional packet capture or tracing tools. Crucially. EBPF prioritizes safe execution before any EBPF program is loaded. It undergoes a rigorous verification process by the kernel. This verifier checks for loops. Auto bond, memory access, and other potential issues to ensure the program cannot crash or compromise the kernel, this safety guarantee is what makes EBPF suitable for running directly in the production systems at scale. Let's crystallize the differences between by comparing EBP of based monitoring side by side with more traditional methods. Looking at data collection, traditional approaches typically use sampling at intervals or rely on agents pulling metrics periodically. EBPF in contrast, performs continuous event driven capture directly at the Len Kernel level. This fundamental difference signifies significantly impacts performance traditional agents. Especially those doing deep inspection or relying on user space processing can introduce noticeable overhead. EBPF being kernel native and highly efficient boosts a near zero performance penalty for many observability tasks. Then there is visibility depth. Traditional monitoring often provides aggregated or service level metrics useful for, but often lacking granularity. EBPF allows us to dive much deeper accessing packet level details, system call information, and fine grain latency measurements. Implementation also differs. Traditional methods often involve deploying user space agents or site cars while EPPF integrates directly with the kernel, often managed by a platform or agent that leverages kernel capabilities. Finally, realtime analysis. Traditional methods are limited by their collection intervals, whereas EBPF can observe and react to events at the microsecond level, enabling true realtime insights and even automated actions. The shift to EBPF isn't just an academic exercise. It delivers tangible. Significant improvements in real world operations. One of the most dynamic impacts consistently reported is a massive reduction. In meantime to resolution MTTR for network related incidents. Organizations frequently see MTTR slashed by over 70% complex baffling issues that previously took hours or even days to diagnose. Can often be pinpointed in minutes with deep visibility. EBPF provides beyond reactive troubleshooting. E-B-P-F-M powers preventative detection by continuously monitoring granular network behavior and establishing baselines. Teams can detect septal anonymous, like increased latency, unusual connection patterns, or rising error rates before they even escalate into user facing outages. This real time visibility enables proactive responses and adjustments. Furthermore, this deep understanding of network traffic directly translates to cost optimization by precisely, identifying inefficient communication pattern. Like Charity services sending unnecessary data across availability zones or external networks, our nations can optimize traffic flow and resource allocation. As mentioned, one enterprise saved over $200,000 annually by optimizing cloud networking costs. Based on this EBPF insights, a standard capability enabled by. EBPF is the ability to automatically discover and visualize the intricate web of service dependencies in real time. Because EBPF sees a all network traffic at kernel level, it can build a complete topology map of which services are communicating with other how often, and with what latency. This is incredibly powerful because it often reveals undocumented legacy or simply. Unexpected relationships that aren't defined in Kubernetes manifests or service discovery systems. We move beyond static diagrams to a live view of reality. We can also perform temporal analysis, watching how this dependencies and communication patterns evolve over time. This helps understand application behavior under different loads or during deployments. By analyzing traffic patterns, like data volumes and communication frequencies, we can pinpoint bottlenecks, identify overly chatty services, or discover opportunities for caching or communication optimization. Crucially, EBPF allows us to trace complex dependency chains. We can follow a request as it hops between multiple service, even across different namespace or clusters. Providing the true end-to-end visibility needed to troubleshoot distributed systems effectively. EBPF continuous granular data stream is perfectly suited for sophisticated realtime anomaly detection. The process often starts with baseline establishment, where EBP powered monitoring systems observe traffic over time to learn. What constitutes normal behavior for specific services and communication paths? This could include typical latency ranges, error rates, protocols used, or connection frequencies. Once a baseline is established, the system performs deviation detection. It continuously compares live traffic, again as the learn baseline automatically. Identifying statistically significant deviations or abnormal patterns without requiring engineers to manually set and maintain potentially brittle static thresholds. When an anomaly is flagged, EBPF provides rich contextual alerts. Instead of just saying high latency detected, an alert might be specific like latency between service A and service B increased by 300 milliseconds. Affecting the specific parts via this network path. This context, drama drastically speeds up diagnosis. Furthermore, this realtime detection can trigger automated response mechanisms based on pre-define rules or specific traffic patterns detected by EBPF. Example, a certain surge in D-N-S-R-S. You could trigger automated workflows for remediation. Like restarting a part, rerouting traffic, or collecting more detailed diagnostics. Adapting. EPPF might seem daunting given its kernel level nature, but there are practical approaches to get started. The key recommendation is to start small. Don't try to boil the ocean. Begin with single Kubernetes cluster. Maybe even just spec, just a specific namespace or application, focus on addressing a particular pain point first, perhaps improving troubleshooting for a critical service, or simply mapping the dependencies of complex application. Next, you need to select tools. There's a vibrant ecosystem. Open source projects like Cilium, which uses EPPF effect extensively for networking. And security Pixie from New Relic focused on auto instrumented. Observability are Hubble. Part of cilium for network visibility are great starting points. Commercial platforms often built upon these, offering more integrated experience, longer data retention, and enterprise support. You'll need to enable kernel support. Most modern Linux distributions and cloud provider images support the necessary kernel version. Generally four point 18 or later. For broad features, though, newer is often better. Ensure your notes, meet the prerequisites for the tools you choose. Finally and crucially, integrate workflows. The insights from EPPF are most valuable when they feed into your existing process. Connect the data to your primary observability platforms like Grafana, Datadog, et cetera. Integrate alerts into your response systems like PagerDuty or Ops Gen, and use the visibility to inform your CSAD and deployment strategies. Let's illustrate the potential with the real world case study from the demanding financial services sector, a global payment process dealing with stringent performance and reliability requirements implemented EBPF based observability. Across their extensive Kubernetes platform, they were facing challenges managing the complexity of over 200 interdependent microservices where network issues could have immediate and significant financial impact. The results were striking. They achieved 70% faster troubleshooting critical payment processing issues, which previously could take hours of painstaking lock creation and guesswork. We are now consistently resolved in minutes. Thanks. Thanks to the granular visibility provided by EBPF. They gained complete visibility into their service mesh successfully mapping the intricate dependence between all 200 plus microservices, uncovering previously unknown interactions. Perhaps most impressively, they leveraged EPPF capabilities for automated remediation. By detecting specific network anomaly patterns in real time, they were able to trigger automated self-healing actions for approximately 85% of common network issues. They encountered significantly improving platform resilience. This case highlights how EBPF delivers not just insights, but also enables automation and improve stability in complex mission critical systems. While operational stability and faster troubleshooting are major wins. EBPF also unlock significant cost optimization benefits by providing unparalleled insight into exactly how your network resources are being consumed. A major driver for cloud costs is often network egress by providing EPPF to precisely identify and quantify traffic. Flowing between different availability zones or out to internet organizations can pinpoint inefficiencies. Optimizing this cross zone traffic patterns based on EBP of data has led to substantial savings with examples showing reduced egress scores of up to 43% sim. Similarly, EBPF can reveal unnecessary or overly chatty communications between services. That might be consuming valuable CPU memory and network bandwidth. Eliminating or optimizing these communications guided by EPPF insights leads to lower resource usage overall, sometimes by as much as 28%. Furthermore, having precise real-time visibility into actual service traffic loads enables much faster scaling decision. Instead of relying on cores, grain metrics or guesswork, you can rightsize your deployments based on observed demand leading to more efficient resource utilization and potentially faster scaling actions, improving both cost and performance. One report indicated 52% faster scaling decisions driven by these precise insights. Hopefully, you are now seeing the potential of EBPF and wondering how to get started. In your own environment, here's a practical path forward. First, learn, dive into the fundamentals. There are fantastic resources online. Check out like example, e bpf.io, vendor blocks, conference talks, and documentation. For specific projects like cilium or pixie, understand the core concepts like tropes, maps, the verifier, and common use cases. Second, experiment theory is great, but hands-on experience is the key. I. Set up a non-production environment, maybe a kind cluster on your laptop or a dedicated dev cluster. Deploy some of the open source tools we mentioned. Try instrumenting a sample application and explore the data you get back. Third, measure. Once you have something running, quantify the benefits in your context. Can you troubleshoot a known issue faster? Using the EBP of data, can you identify an optimization opportunity? Gather metrics to build a case for wider adoption. Finally, once you have demonstrated value and build confidence, you can scale plan a phase rollout to your production environment, integrate EBPF observability strategically into your standard operating procedures. Start small, learn, measure, and then scale with confidence. So that brings us to the end of our exploration. Of network excavation using EBPF. We have discussed the challenges of observability in modern cloud native systems and seen how E-B-P-F-A powerful solution by offers a powerful solution by providing safe, efficient kernel level visibility. We have touched upon its real world impact from drastically reducing troubleshooting times, and enabling proactive anomaly detection to visualize complex dependencies and drive significant cost optimization. The key takeaway is that EBPF is no longer just an niche kernel technology. It's becoming a fun foundational component for robust, efficient, and observable cloud native platforms. I strongly encourage you to investigate the tools and techniques we discuss today and consider how they provide superpowers for your own team and systems. Thank you for your time and attention.