Tenant-Aware Event Ingestion for Dynamic Traffic Spikes
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Solution Overview
Problem
Existing systems face challenges in managing unpredictable user behavior across diverse web and mobile applications, leading to fluctuations in workload that affect data processing times and resource allocation, making it difficult to maintain fairness and consistency in processing times for different applications.
Innovation Solution
A method and system that dynamically create and allocate clone data ingestor instances based on predicted processing demand, using a tenant-aware traffic monitor and data ingestor clone orchestrator to manage user events, ensuring equitable processing times and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed number of data ingestor instances are used, then device complexity is reduced, but productivity decreases during traffic spikes
Solution Approach 1:
The system dynamically adjusts the number of data ingestor instances based on real-time traffic conditions. The load manager monitors workload metrics and automatically spawns or terminates instances to match demand, transforming a static resource allocation into a dynamic adaptive system that optimizes throughput during traffic spikes while maintaining manageable complexity through automated control
Solution Approach 2:
The data ingestor instances are self-managed through automated provisioning and orchestration. When traffic patterns indicate need for additional capacity, the system automatically creates and configures new instances without manual intervention, allowing the system to service its own scaling requirements and maintain high productivity during variable workloads
2Productivity
If more data ingestor instances are created to handle traffic spikes, then productivity is improved, but loss of time increases due to instance creation overhead
Solution Approach 1:
The system performs preliminary actions by pre-configuring instance templates and provisioning resources in advance before traffic spikes occur. Load prediction algorithms anticipate demand patterns and pre-spin instances or prepare resource pools, so when actual traffic spikes hit, instances are already available or can be activated immediately, eliminating provisioning delays during critical high-load periods
Solution Approach 2:
The system creates a cushion of pre-provisioned resources and standby instances before traffic demands arise. By maintaining a pool of pre-configured, ready-to-deploy instances and pre-allocating computational resources, the system cushions against the time loss that would otherwise occur during emergency instance creation, ensuring productivity is maintained during sudden traffic surges
3Adaptability or versatility
If data ingestor instances are dynamically scaled, then adaptability to varying workloads is improved, but device complexity increases
Solution Approach 1:
The system implements continuous feedback loops where the load manager monitors workload metrics, processing performance, and resource utilization in real-time. This feedback drives automated scaling decisions, allowing instances to be dynamically added or removed based on actual system state. The feedback mechanism enables high adaptability to varying workloads while managing complexity through closed-loop control that automatically adjusts to conditions without manual intervention
Solution Approach 2:
The data ingestor instances are designed as universal, multi-functional components that can handle various types of user event data across different applications and tenants. By creating versatile instances that can adapt to different workload types through configuration rather than requiring specialized instances for each scenario, the system achieves high adaptability while reducing the overall complexity of managing diverse resource types
4Productivity
If cloning mechanism is used to create instances quickly, then productivity is improved during spikes, but loss of substance increases due to resource duplication
Solution Approach 1:
The system uses a cloning mechanism to rapidly create data ingestor instances by copying proven, working instance configurations. Instead of building instances from scratch during traffic spikes, the system clones existing templates, instantly deploying functional instances that inherit all necessary configurations, dependencies, and settings. This copying approach achieves rapid instance deployment that maintains high productivity during spikes while avoiding the resource overhead of creating entirely new instances
Data Source
AI summary
A method for processing one or more user events is described. The method includes creating a data ingestor instance to process the one or more user events. A user event data associated with a tenant is retrieved, wherein the user event data is retrieved by polling a message store. Further, processing demand for the retrieved user event data is determined. Furthermore, a number of the data ingestor instance required to accommodate the processing demand is predicted. One or more clone ingestor instances are created dynamically based on the predicted number of data ingestor instance. Finally, allocate one or more clone ingestor instances for processing the one or more user events.