Hello everyone. This is Gina Abraham. Today I would be presenting modern reliability framework for high speed infrastructure. This is a comprehensive approach to reduce service disruptions and improve incident detection and resolution in complex distributed systems. So key takeaways on the infrastructure growth. Over the past few years, we have seen an unprecedented growth in infrastructure components. Systems are scaling at 10 x the previous rate, creating both opportunities and significant challenges. As infrastructure grows, it becomes increasingly difficult to manage this scaling means that we need to adopt. Sophisticated management solutions to ensure that systems continue to operate smoothly without disruptions. One of the biggest challenges here is monitoring these systems. Distributed systems create visibility gaps, essentially, blind spots that mask potential failure points, making it harder to detect and address issues before they impact operations. So let's just look at some key SRE challenges. Site reliability engineering teams or SRE teams are often faced with, uh, several critical challenges in maintaining the reliability of modern systems. First, monitoring gaps are a significant issue. In fact, incomplete visibility is. Responsible for 68% of extended service outages. When we can't see everything happening in the system, failures can go undetected, leading to longer down times. Second, service degradation is another challenge. This refers to a gradual performance decline that is often undetected until it reaches a critical point. These small issues accumulate over time and cause major disruptions, if not address early. Lastly, detection delays are common. On average, there is 42 minute delay between when an incident occurs and when an alert is triggered. That's a lot of time for the issue to escalate. Now let's dive into the core components of our modern reliability framework. The framework aims to address the challenges mentioned earlier and provides powerful tools to improve system reliability. One key benefit is 85%. Faster detection. By using advanced monitoring and analytics, we can identify issues significantly faster than before. Next, the framework also reduces false alerts by 65%. This is important because false positives can overwhelm SRE teams and lead to alert fatigue. Reducing the effectiveness of response efforts. Additionally, 93% co coverage of system components with service level objectives ensures that we can define measurable reliability goals for nearly all aspects of the infrastructure to. Now let's look at how automated anomaly detection works. This section focuses on one of the most powerful tools of our framework, the automated anomaly detection. Dynamic thresholds are used to adapt to evolving traffic patterns and system behavior. This allows the system to automatically adjust its detection criteria based on current load, significantly reducing the occurrence of unnecessary alerts during expected fluctuations in system behavior. Machine learning powered analysis plays a critical role in identifying subtle deviations in system performance often before they evolve into major incidents. This proactive detection of anomalies allows us to react to issues earlier and with more precision. The framework also includes. Predictive alerts which provide early warnings based on emerging patterns. This allows teams to intervene before systems reach critical thresholds, potentially avoiding system-wide failures. System level objectives are a cornerstone of this framework, providing a clear set of goals for the. Reliability and performance of our systems. For instance, we are able to track core resources such as CPU, memory and IO with 99.9% accuracy, ensuring that we have a clear picture of how our infrastructure is performing at any given time. Additionally, the framework is capable of pre-production bottleneck detection, which allows us to identify 95% of potential issues before they even make it to production. This reduces the chances of performance degradation once a system is live. Another benefit is capacity planning, which allows teams to forecast resource needs 30 days ahead. Preventing unexpected performance degradation due to resource shortages. Managing complex systems for organizations that rely on multi-service architectures. It's important. To have tools that streamline incident detection and response alert consolidation reduces duplicate new notifications by 75% when multiple services are impacted by the same issue. We can avoid overwhelming SRE teams with redundant alerts, intelligent filtering, prioritizes alerts based. On context, ensuring that critical issues are addressed first and that resources are used efficiently. Dependency mapping automatically discovers and visualizes service relationships, making it easier to understand how different services are interconnected. This insight is crucial for identifying. And resolving issues that affect multiple parts of the system. The correlation engine connects incidents across services providing a unified view of service health, and making it easier to pinpoint root causes of problems. Cascading failures where one failure triggers a series of problems across interconnected systems are among the most damaging types of outages. One of the way ways we mitigate these failures is through early detection. These. Real time anomaly detection to catch deviations before they spread across the system. Circuit breaker is another critical mechanism. By deploying intelligent boundaries between services, we can isolate failing components and prevent them from contaminating the entire system. Load shedding algorithms dynamically route traffic to preserve mission critical services during periods of high load or degraded performance. This ensures that even when. A part of the system is under strain. Core services remain operational. Graceful recovery involves orchestrating service restoration in a way that respects dependency, minimizing the impact on system stability as services come back online. Now, let's look at how AI can. Help with observability. Observability here. Artificial inte intelligence plays a critical role in improving observability and helping teams stay on top of system health. Anomaly classification. Using AI offers 90% accuracy in identifying type of issue, reducing manual in investigation efforts, and speeding up the response time. Predictive maintenance can forecast failures 24 to 48 hours in advance, giving teams a head start on addressing potential issues before they cause real major disruptions. AI also aids in root cause analysis, reducing investigation time by 70%. Instead of manually correlating fail logs and metrics, AI tools can quickly pinpoint the root cause, allowing for faster resolution. Finally, AI can improve on-call efficiency, reducing unnecessary escalations by 60%, which minimizes. Fatigue and allows SRE teams to focus on critical tasks. Now let's walk through the implementation phases of the framework assessment phase. During this phase, we conduct a gap analysis to assess current monitoring capabilities and identify areas of improvement. We also organize workshops to define SLOs and for and form the implementation team deployment phase in the, in the deployment phase. We start, we set up the core observability platform and begin service. Instrumentation. This is also when we start training our machine learning models to recognize patterns in the system. Lastly, the optimization phase. The final phase focuses on refining the system. This involves tuning alerts to ensure they are both precise and actionable. Automating run books to streamline incident response and con and. Continuously improving the platform based on feedback and new data. In conclusion, thank you for your time and attention. This framework is designed to tackle the modern challenges of distributed systems and provide proactive solutions for ensuring reliability and minimizing service disruptions.