Introduction

At ShitOps, we continuously strive to push the boundaries of infrastructure automation and optimization. Today, we present our groundbreaking technical solution to an urgent problem faced by enterprises in the USA with Bring Your Own Device (BYOD) policies: efficient traffic engineering that dynamically balances network loads across heterogeneous user devices including Nintendo Wii, iPads, Samsung phones, and even legacy Microsoft Excel-based network spreadsheets.

The Problem

In BYOD environments, enterprise networks suffer from inconsistent traffic patterns caused by an influx of diverse devices. Traditional traffic engineering solutions often fail to dynamically accommodate this heterogeneity, resulting in network congestion, reduced productivity, and unsatisfactory user experiences.

Moreover, the existing infrastructure comprises legacy components such as Microsoft Excel-based traffic logs used by network admins for manual adjustments, and nginx servers deployed as ingress controllers that require more intelligent routing based on real-time device telemetry.

Our Visionary Solution

To solve this multifaceted problem, we've architected an ultra-scalable, multi-cloud compatible Kubernetes native platform that incorporates Kafka messaging streams managed by Strimzi operators for fault-tolerant, distributed telemetry ingestion. This platform seamlessly integrates with Microsoft Excel via Azure Logic Apps to leverage pre-existing legacy spreadsheets as intelligent configuration data sources.

The key pillars of our architecture are:

Architecture Flow

sequenceDiagram participant Wii as Nintendo Wii participant Sam as Samsung Devices participant iPad as iPad Devices participant Kafka as Kafka Cluster participant K8s as Kubernetes Cluster participant TF as TensorFlow ML Pods participant nginx as nginx Ingress participant Excel as Microsoft Excel participant Admin as Network Admin Wii->>Kafka: Send telemetry data Sam->>Kafka: Send telemetry data iPad->>Kafka: Send telemetry data Kafka->>K8s: Stream telemetry to ML pods K8s->>TF: Run traffic engineering algorithms TF->>nginx: Update routing rules Admin->>Excel: Edit policy spreadsheets Excel->>Azure Logic Apps: Trigger config updates Azure Logic Apps->>nginx: Apply overrides nginx->>Devices: Route traffic accordingly

Implementation Details

Device Telemetry Hooks

Each device type contributes telemetry through custom JNI wrappers that transform device-specific APIs into Kafka message formats. For Nintendo Wii, we have reverse engineered network stack hooks. Samsung and iPad devices use their respective MDM APIs.

Kafka and Strimzi

We deploy a Kubernetes-native Kafka solution with Strimzi, ensuring fault tolerance and autoscaling of the traffic telemetry ingestion pipeline across multiple availability zones in the USA.

Traffic Engineering Model

TensorFlow models running in Kubernetes pods analyze traffic patterns and predict optimal network path utilization, achieving extremely fine-grained control layered atop nginx ingress controllers.

nginx Dynamic Reloading

Custom nginx controller extensions fetch routing decisions from the ML pods every 30 seconds, dynamically adjusting ingress routing paths without downtime.

Microsoft Excel Legacy Integration

Network administrators maintain traffic policies in Excel spreadsheets. Via Azure Logic Apps, changes in Excel trigger automatic workflows that propagate configuration updates to nginx, creating a seamless blend of cutting-edge tech with familiar legacy tools.

Benefits

Conclusion

Our innovative approach leverages the latest cloud-native technologies and legacy integrations, transforming heterogeneous BYOD network management. This solution represents the pinnacle of traffic engineering in modern enterprise environments, driving unparalleled productivity and user satisfaction.

Stay tuned to ShitOps engineering blog for more breakthrough innovations!