Hello, everyone. Welcome to corner 42 cloud native 2025. I am Arun Pandian, a technology infrastructure specialist, and I'm going to talk about how a unified multi cloud monitoring solution can deliver comprehensive observability across diverse cloud service providers. So in the agenda, I will talk about the importance of multi cloud monitoring, and then the core components off multi cloud observability and what are the tools that can be integrated and deployed in multi cloud monitoring, best practice that can be leveraged, and at last the future trends in cloud observability. I'll start with the complexities in managing multiple clouds. Organizations are rapidly adopting multiple cloud platforms to leverage their unique strengths. But they also inherit a set of operational challenges that can significantly impact their ability to monitor, secure, and optimize these multi cloud environments effectively. First challenge is data silos and fragmentation. Each cloud provider typically comes with its own monitoring and logging systems or solutions. As a result, these solutions can create fragmented data and making it difficult to derive holistic insights without a centralized approach. The second is the latency and performance bottlenecks. So organizations collect or gather real time data from globally dispersed networks, and those networking complexities can introduce service delays. The third one is compliance and security visibility gaps. So each provider offers distinct controls and default configurations. So uniformly enforcing security and compliance policies across all the cloud environments can be challenging. And at last, the scalability and cost management, the volume of the logs and metrics grows, grows exponentially on a daily basis. so the expense of, storing, managing and analyzing the data, can become complex. The next I want to talk about, the importance of, unified multicloud monitoring. The first is the consistent, we can achieve the consistent operational visibility. A centralized monitoring approach, enable, comprehensive visibility and understanding of what's happening within, within multiple cloud environments. So this holistic view makes it easier for teams to track performance trends, detect anomalies, and maintain healthy application or service levels. The next is the Simplified Incident Response and Lucas Analysis. So whenever an issue occurs, it is essential to resolve it quickly to meet the needs. the, MTTR metric, the mean time to resolve and the unified monitoring solution help us to consolidate logs, metrics and events at one particular place, to reduce the time spent on, in analyzing the data. So this streamlined approach, help, teams, to identify the issues or problems quickly, drive resolution. and even avoid prolonged service descriptions. The next is the enhanced reliability and infrastructure resilience. So if we, by continuously monitoring or tracking the key performance indicators, the KPIs, we can proactively fix issues before they become critical. So the insights that are gathered highlight patents, highlight the patents or irregularities. That, help the teams, to, intelligently scale, decisions, an auto scaling and then also support the preventive maintenance. And, next one is the streamlined security and compliance monitoring. So security and regulatory requirements can become complicated when multiple cloud products are involved. So a unified view is required to help, standardizing monitoring practices across all the cloud platforms. and detect suspicious activities instantly and meet the compliance requirements. And at last, the cost optimization and resource efficiency. the multi cloud monitoring solution, offer comprehensive visibility into cost and resource consumption that allows teams to make informed decisions about the capacity planning, cost allocation and optimization approaches. The next I will go over the core components of, the core components that are involved in a multi cloud observability solution. The first one is, the data collection. Here we can aggregate the logs, metrics, traces from diverse cloud environments and, utilizing, agent based or agent less solutions to capture every critical event. So this continuous stream of raw telemetry data, that supports, the analysis and also support to create a, robust observability, solution or system. The next one is data processing and correlation. here, if you use the advanced analytics, machine learning and even correlation, we can transform the data into context rich, insights by identifying the patents, anomalies, and dependencies across, distributed services. So this refined data set, gives, teams the ability to diagnose issues quickly. Accelerate the root cause analysis and, and decision making in complex multi cloud, landscapes. The other component is observability and visualization. So with interactive dashboards, health checks, and alerting capabilities, so we can achieve the real time visibility into application performance and infrastructure status. so these unified views help, teams to spot trends, isolate bottlenecks, and maintain seamless services. application, delivery, by, showcasing the system's relationship and dependencies. The next is automated, remediation in incident response. so whenever the anomalies or threshold features occur or detected, automated playbooks or self healing mechanisms, are enabled. So that helps us to resolve, issues before they escalate. So this integrated incident response mechanism or approach. Ensure minimal downtime by, orchestrating the alerts, on call schedules and, collaborative workflows for rapid problem containment. And at last, the security and compliance monitoring. so we can continuously scan the telemetry for suspicious activities or policy violations. reinforcing proactive threat detection and compliance adherence, in multi cloud, setups. So through seamless integrations with, security frameworks and automated compliance checks, teams can uphold a stringent government, governance, mitigate risks and satisfy the, regulatory requirements. there's a reference or a sample architecture that can be even used. to design a unified, monitoring system for multi cloud environments. So the first layer is, data collection layer. the unified monitoring solution can support multiple cloud environments, including major service providers, AWS, Azure, Google Cloud, and even private cloud. So each cloud provider generates logs, metrics, and events that need to be monitored for performance, availability, and security. The next is the monitoring agents. So the agents are deployed across different cloud environments to collect the observability data. So these agents gather log metrics and even data from various cloud native services and infrastructure components. Then next is the data aggregation layer. Here, the data is categorized, like the observability data is categorized and processed by specialized components like log collectors, that handles the logs generated by the applications, infrastructure, and cloud services. And the other one is the metrics aggregators. Here, it processes and normalizes performance related metrics. the last one is the event stream processors. this helps manage and analyze event driven data streams for real time insights. And the next one is centralized monitoring and analytics. So all the collected and processed data is sent to a centralized monitoring and analytics platform. So this layer is responsible for, correlating different types of data, detecting anomalies, and even identifying the patents, patents for, proactive monitoring. The second last is the observability and alerting mechanism layer. So these layers generate alert alerts based on anomalies, security breaches, or failures, system failures. So they'll create real time dashboards for visualizing the system health and that is an entrance. And at last, we have the incident management. Here are the alerts from the system or. Are fed into ITS and tools, for ticketing and incident resolution. here the next is, I'll talk about the tools, that can be, integrated or deployed, in multi-cloud environments. first either we can use the open source, tools or like commercial SaaS platforms, tools. So open source tools such as like Prometheus, Grafana, Jagger, open Telemetry and SaaS platforms. So Datadog. New Relic, Grafana Cloud, Dynatrace, Plunk. in terms of deployment, the open source tools requires manual setup, configuration and maintenance. But the SaaS platforms are fully managed or completely managed by the service provider. it is also easily or quick to deploy and scalable. also, in terms of scalability, open source tools are horizontally scalable, but requires careful planning of capacity and resource allocation. But for SaaS platforms, it is automated elastic scaling, and also it supports elastic scaling in response to resource usage, and the vendor handles the capacity planning. for the cost wise, open source tools are typically free, with cost rising from the self hosting infrastructure, or storage, compute, networking, and even the maintenance and the scaling cost grow, with the usage. And also the SaaS platform's cost is subscription based, cost can increase with the data ingestion and storage. Next is, the best practices that we can follow. For, implementing, robust, unified multi cloud monitoring solution. yeah, first we can define a centralized observability strategy to establish a unified observability framework, that consolidates the logs, metrics, and traces from all the cloud environments. And next we can adopt, a multi layered data collection approach. to collect telemetry data across infrastructure applications and network layers. And we also can normalize and correlated data across cloud environments to standardize the diverse data formats. And even under correlate the metrics, logs and traces. to create actionable insights and prevent any blind spots. we can also implement a vendor agnostic observability platform to integrate seamlessly with cloud service providers to avoid any vendor lock in. And, it is, possible to embed observability into CICD workforce to, for proactive monitoring, automated anomaly detection, and even faster incident resolution. During any critical deployments. Also, we need to strengthen the security compliance monitoring, to safeguard the workloads or any application deployments across multi cloud environments. So the last I'm going to talk about the future trends in cloud monitoring and observability. The first is the AI driven observability and anomaly detection. So many organizations leverage some AI driven solutions like AI powered analytics and machine learning models. for transforming the observability by automatically detecting, anomalies, predictive failures, and even enabling, some self healing capabilities in, in multi cloud environment. the next is the observability in the serverless and edge computing. if, when the workload shifted to serverless architectures and edge computing, observability solutions should evolve to provide, real time visibility, into those, these ephemerals. EAL resources and highly distributed environments. The next is eeb, EBPF, and the kernel level monitoring is mainly applicable for the Linux, OS operating systems. so EBPF is revolutionizing the cloud monitoring by enabling deep, lowered visibility into system calls, network activities, and application behavior, directly at the Linux current level. the next is the shift level observability for DevOps and Sari. So if we, apply observability early in the software development life cycle, so it, allows DevOps and SRE teams to proactively detect issues, optimize the performance, and even ensure the reliability from the beginning, reliability of the systems from the beginning. And at last, the security driven observability, that's a COPS plus observability. So this allows, security, cloud security teams to integrate security insights into observability pipelines. to enhance threat detection, compliance enforcement and a proactive risk management across, cloud native applications. Thank you.