Shadow Production System for Service Disruption Simulation
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Solution Overview
Problem
Production systems face challenges in anticipating and minimizing service disruptions, such as unexpected spikes in demand, which can lead to system failures and customer dissatisfaction, making it difficult to understand vulnerabilities and take corrective actions without incurring unnecessary costs or disrupting live operations.
Innovation Solution
The creation of a shadow production system that mirrors the live production system allows for simulated service disruptions, enabling evaluation of recovery characteristics without affecting live operations. This involves replicating client traffic and processing requests on the shadow system to analyze performance differences and optimize recovery processes, including cache management and elasticity adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service disruptions are avoided to minimize costs and maintain system availability, then system reliability is improved, but the ability to evaluate and improve recovery characteristics deteriorates
Solution Approach 1:
The patent creates a shadow production system that replicates the live production system's environment, configuration, and workload. This copy allows recovery simulations to be performed on the shadow system without affecting the live system's availability. The shadow system mirrors production traffic and settings, enabling realistic recovery evaluation while the live system continues operating normally.
2Productivity
If scheduled reboot procedures are performed to optimize system performance, then system performance is improved, but service disruption costs increase
Solution Approach 1:
The patent performs recovery simulations in advance on the shadow production system before executing actual reboot procedures on the live system. This preliminary action allows the team to evaluate recovery characteristics, identify potential issues, and optimize recovery processes without causing actual service disruptions. The simulations prepare the system for future reboots, enabling faster and more efficient recovery when needed.
3Reliability
If unplanned service disruptions are allowed to occur to identify system vulnerabilities, then system resilience is improved, but customer satisfaction deteriorates
Solution Approach 1:
The shadow production system serves as an intermediary that absorbs the harmful effects of service disruption simulations. By directing simulated disruptions to the shadow system rather than the live system, the patent enables vulnerability identification and resilience testing without causing actual service outages that would dissatisfy customers. The shadow system acts as a safe testing ground for stress scenarios.
4Measurement precision
If recovery simulations are performed on the live production system to evaluate recovery characteristics, then recovery evaluation accuracy is improved, but system availability deteriorates
Solution Approach 1:
The patent creates a shadow production system that replicates the live production system's environment, configuration, and workload. This copy allows recovery simulations to be performed on the shadow system without affecting the live system's availability. The shadow system mirrors production traffic and settings, enabling realistic recovery evaluation while the live system continues operating normally.
Data Source
AI summary
The techniques described herein provide evaluations of a production system's ability to recover from a service disruption without actually disrupting service to the production system. In some examples, a live production system is at least partly duplicated to create a shadow production system that is a quarantined copy of the production system. Traffic between the production system, client devices, and possibly dependency services may be replicated onto the shadow production system while a recovery simulation service induces a specified type of service disruption onto the shadow production system. Behavior of the shadow production system during service disruption is used to identify performance differences and to evaluate expected recovery characteristics of the live production system.