Real-Time Statistical Engine Overload Protection
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Solution Overview
Problem
Real-time statistical engines in contact centers face overload conditions due to spikes in data arrival rates, leading to increased latency, data loss, and inaccurate statistics, especially in non-scalable or partially scalable systems.
Innovation Solution
Implementing an overload protection system with a state-based overload protector that detects load thresholds, downgrades non-critical entities to reduce computational load, and gradually restores normal operations when conditions improve, ensuring continuous real-time data provision without significant data loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the real-time statistical engine processes all incoming data at high rate, then data accuracy and completeness are maintained, but system overload occurs causing increased latency and data loss
Solution Approach 1:
The patent applies partial action by selectively processing only critical entities during overload conditions. The system identifies and prioritizes important entities while reducing or skipping processing of less critical ones, allowing the system to maintain functionality and stability during high load without completely sacrificing data accuracy for essential metrics.
Solution Approach 2:
The system dynamically changes processing parameters based on load conditions. When overload is detected, the engine adjusts parameters such as sampling rates, aggregation intervals, and entity priorities. This allows flexible adaptation to maintain system stability while preserving data accuracy for critical entities where possible.
2Quantity of substance
If the system maintains high processing capacity for all entities, then complete data coverage is achieved, but responsiveness and latency increase during overload conditions
Solution Approach 1:
The patent segments entities into different priority levels and processing categories. By dividing the entity set into critical, important, and optional groups, the system can apply different processing strategies to each segment, maintaining high responsiveness for critical entities while reducing overall processing load during overload conditions.
Solution Approach 2:
During overload, the system performs partial processing on lower-priority entities while maintaining full processing capacity for high-priority entities. This selective approach ensures responsiveness is preserved for critical data while still providing some level of coverage for less important entities.
3Productivity
If additional computing resources are allocated to handle increased load, then processing capacity increases, but system scalability is limited in non-scalable or partially scalable systems
Solution Approach 1:
The patent implements dynamic load management that automatically adjusts processing behavior based on real-time system conditions. The engine monitors load metrics and dynamically changes processing strategies, entity priorities, and resource allocation without requiring additional hardware or infrastructure, enabling non-scalable systems to adapt to varying load conditions.
Solution Approach 2:
The system changes operational parameters such as aggregation windows, sampling frequencies, and entity inclusion criteria based on load conditions. This allows the same hardware infrastructure to effectively handle varying processing capacities by adjusting software parameters rather than requiring physical scalability.
4Measurement precision
If the system processes all entities with full computational resources, then data quality is maintained, but load management becomes complex and data loss occurs during extreme overload
Solution Approach 1:
The patent applies preliminary action by pre-classifying entities into priority categories and pre-defining processing strategies for different load conditions. This upfront preparation simplifies real-time decision-making during overload, as the system only needs to apply pre-determined rules rather than making complex decisions under pressure, reducing both complexity and data loss risk.
Data Source
AI summary
A method for providing overload protection to a real-time computational engine configured to compute a plurality of values corresponding to a plurality of entities includes, when an overload protector is in an overload state: identifying one or more entities in a normal status and having high corresponding load contributions; downgrading the identified one or more entities; in response to detecting that a load level is below a low threshold, transitioning the overload protector to a recovery state and beginning a cool down period; and, when the overload protector is in the recovery state: upgrading a first group of entities of the one or more downgraded entities to a normal status; determining whether the cool down period has ended; and in response to determining that the cool down period has ended: upgrading all downgraded entities to the normal status.