Hello everyone. Today I'm excited to talk about something that sits at the center of every IOT ecosystem. Messaging at scale as iot device is multiply into the tens of billions, the ability to move data reliably, quickly, and intelligently becomes one of the biggest engineering challenges of the next decade. This talk focuses on data driven strategies for achieving low latency, high throughput, and strong operational resilience, the three pillars needed to support real world iot systems at scale. My name is Kate Ani, and I am a software development engineer at AWS. I specialize in building distributed systems, real time messaging platforms and IOT infrastructure. That must scale reliably under extreme traffic conditions. Most of the insights I'm sharing today come from P practical work, implementing high performance messaging pipelines used in enterprise deployments. We all know iot is growing, but the scale is often underestimated By 2025, IOT systems are projected to generate 1 75 zetabytes of data. There's an almost. Unimaginable number. IOT systems also demand sub hundred millisecond latency for anything real time autonomous vehicles, robotics, industrial automation, or medical devices, right? And beyond data volume, we are looking at 1 million plus concurrent device connections in even moderately large deployments, smart cities, industrial plants, logical networks. This combination, massive data, strict latency, millions of connections. This creates a perfect storm that traditional architecture simply cannot handle. We need a new way of thinking about scaling here. Most traditional architectures today, they rely on reactive scaling model. Your traffic goes up, your metrics spike, and then the system responds afterwards. But in iot, this doesn't work. We have latency demands, so responses must stay under a hundred milliseconds, even across distributed regions, throughput requirements, millions of messages per second must be processed without backlog. And then operational resilience. Uptime must remain high even when traffic surges or partial failures occur. IOT requires precision, proactive scaling and data driven optimizations, not brute force. Our approach today is organized around a three pillar framework. The first one is latency reduction. So we want to use techniques like multi-tier caching asynchronous designs between microservices and intelligent message prioritization. The second one is throughput maximization. This can be achieved with advanced load balancing, predictive auto-scaling and q partitioning. And then the third one, that is operational resilience. It can be built through continuous monitoring. Anomaly detections and then machine learning driven tuning these pillars together provide a blueprint for modern iot messaging pipelines. To reduce latency across billions of messages, you must remove friction at every step. Multi-year caching at the edge region and central layers dramatically reduce lookup overhead. You are asynchronous event driven frameworks. They can avoid blocking behaviors and then keep message flows very smooth. And then intelligent prioritization ensures any urgent events like actuator commands or safety alerts that occur, they never wait behind lower priority telemetry. The, this strategy targets the biggest cause of slowdowns that is unnecessary waiting in the system. The optimizations aren't theoretical they are producing real outcomes. So manufacturing, iot deployment, predictive maintenance, workloads, solut drop to catch. Failures be before downtime occurred. And then for smart city sensor networks, infrastructure systems reached consistent sub millisecond processing for traffic and environmental monitoring. And then we saw that for large scale chat platforms, message delivery performance improved to near instant, even at peak global volume. This example show how small latency gains at the micro level become huge wins at scale. IOT systems can generate busty traffic that's known it, they can send millions of messages in seconds to keep up. We have to focus on throughput and that can be done via advanced load balancing. So load balancing that chooses the best nodes based on health and capacity. And then predictive autoscaling should use machine learning today to prepare for surgeries before they happen. So constant traffic analysis and looking at patterns from previous days across the entire network. Then for q partitioning where messages are separated by priority or destination to enable parallel processing. The idea is not just to scale, but to scale intelligently. In real world iot systems, traffic spikes are unavoidable. For example, think about firmware, rollouts or sensor storms, or network reconnection events, or just regular daily peak cycles. The architecture can withstand three x traffic surges with zero downtime. And what are the key mechanisms to achieve that? The first one is early horizontal scaling before the limits are hit. So if we do machine learning based analysis of the system, and that should alert your system or make it capable enough to horizontally scale before the actual event hits. Circuit breakers should be placed to prevent cascading failures. And then intelligent message buffering to avoid data loss. This gives iot systems the resilience needed for mission critical environments. Operational resilience is the difference between a minor issue and a majors outage. Continuous monitoring tracks performance metrics in real time across all components. And then anomaly detection should help spot early warning signals before users notice this degradation. And then ML driven tuning should learn from past incidents and optimize parameters automatically. A combination of these three should help or should deliver. 99.9995% uptime, even in resource constraint environments in iot uptime isn't just a metric. It is. It directly impacts safety, automation, and business continuity. So assuming we implement all of the measures that we just talked about, what does the future look like? We are entering a new era of messaging optimization, powered by ai. We should be talking about self-healing systems. We should have AI agents. They should detect, diagnose, and fix performance issues in real time without any human intervention. Edge computing integration. Processing data closer to the devices reduces latency significantly rather than having network hops across countries or continents. And that should enable new use cases for our customers. And then sustainability focused algorithms. Modern systems can reduce energy consumption by up to 30% while still maintaining the high performance that we expect from these systems. The next generation of iot messaging will be and should be autonomous, distributed, and energy aware. Through real world deployments we see recurring architectural patterns that consistently deliver results. Event driven microservices for decoupled independently scalable workflows, and then message brokers for clustering of high availability and redundancy. We should have regional failers for geo resiliency and disaster scenarios. And then protocol optimization, especially within with MQ TT and COP for constrain devices. These design principles from form the backbone of resilient IOT messaging systems. But then how do we manage or balance optimization with complexity? One important lesson that we learned is optimization should always be balanced. And then too much complexity can cause fragility. There are three guidelines. Always start simple as with everything. Implement one optimization at a time, and then measure its actual impact on the system and monitor it. Monitor everything. As I said, quality data produces better decisions. Wait for the data, let it arrive and gather the data. And then you want to iterate continuously. You want to understand the traffic patterns that evolve. And then with the traffic patterns that are evolving, your optimizations should evolve. The goal is sustainable scalability, not over-engineering. So let me wrap up with three key takeaways. First one is systematic frameworks. Outperform reactive scaling data driven methods deliver predictable repeatable improvements. Real world strategies already work at enterprise scale, so caching, predictive scaling, and machine learning based monitoring consistently delivers better performance today. And then future ready architectures integrate AI and edge computing, this combination. Should enable efficient, resilient, sustainable I ecosystems with ai. This is the path forward for iot messaging infrastructures that truly scales it has to grow with ai. And with that, thank you all for joining the session. I hope the strategies we discuss give you a clearer path towards building fast, reliable, and scalable iot messaging systems. Thank you for your time.