Failover System for Event Stream Processing
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Solution Overview
Problem
Current computing systems face challenges in handling large data processing and failover mechanisms, particularly in event stream processing environments, where failures can lead to service interruptions and data loss, especially in critical operations like manufacturing or drilling.
Innovation Solution
The implementation of a failover system for Event Stream Processing (ESP) that allows seamless transition between active and standby nodes within the ESP system, ensuring continuous data processing without interruption, and the use of censored regression models to correct and unconstrain historical data for accurate demand forecasting in resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a failover system is implemented to ensure continuous data processing, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements a standby node that replicates the active node's data processing capabilities. When the active node fails, the standby node immediately takes over without requiring complex reconfiguration or data recovery procedures, thus improving reliability while managing complexity through straightforward replication
Solution Approach 2:
The standby node is pre-configured and maintained in readiness before any failure occurs. This preliminary preparation ensures that failover can happen immediately when needed, eliminating the need for complex real-time decision-making or configuration during failure events
2Measurement precision
If historical data is corrected using censored regression models for accurate demand forecasting, then measurement precision is improved, but computational requirements increase
Solution Approach 1:
The patent transforms constrained historical data into unconstrained data by applying censored regression models that adjust statistical parameters. This allows accurate demand forecasting to be achieved by modifying data representation rather than requiring computationally intensive processing of raw constrained data
Data Source
AI summary
Exemplary embodiments are generally directed to methods, mediums, and systems for correcting censored or constrained historical data with various possible types of computing devices, including cloud-based devices, personal computing devices, and edge-based devices. The corrected data may be used in forecasting, for example to forecast demand for a limited resource. In some embodiments, the data is modeled at a higher level of granularity than an individual record. The aggregated demand may then be pro-rated over a group of categories or users where a given category of users that might be small or nonexistent over a certain time frame may be better accommodated. Moreover, it may be easier or more efficient to make assumptions and employ computing resources at the aggregate level.