Data Stream Processor Anomaly Detection in Distributed Systems
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Solution Overview
Problem
Conventional error detection and resolution techniques in distributed computing systems are inadequate for high-speed data channels, leading to increased computing resource costs, manpower requirements, and performance degradation, particularly in detecting spikes or abnormalities in response times.
Innovation Solution
A data stream processor with a data stream anomaly manager is configured to detect anomalies in real-time by accessing stream characteristics and comparing them against thresholds, generating anomaly resolution data to counteract detected issues, and alerting entities for timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional error detection techniques are used in high-speed data channels, then system throughput is maintained, but anomaly detection precision deteriorates
Solution Approach 1:
The patent segments the data stream into multiple parallel streams, each processed by dedicated processing elements. This allows simultaneous high-speed throughput processing while maintaining precise anomaly detection capabilities in each segment through specialized processing pipelines that can archive and analyze multiple attributes without bottlenecking the overall system speed.
2Reliability
If conventional trouble-shooting techniques are applied to numerous computing devices, then system coverage is maintained, but detection efficiency deteriorates
Solution Approach 1:
The patent introduces intermediary components including buffer memory elements and processing pipelines that act as mediators between numerous computing devices and the anomaly detection system. These intermediaries aggregate, buffer, and pre-process data from multiple sources, enabling comprehensive system coverage while maintaining high detection efficiency through centralized analysis of consolidated data streams.
3Measurement precision
If more computing resources are allocated to error detection, then anomaly detection capability is improved, but system resource cost increases
Solution Approach 1:
The patent implements dynamic resource allocation where processing intensity and resource allocation adapt based on detected anomaly patterns and system conditions. Processing elements dynamically adjust their operational characteristics, allocating more resources when anomalies are detected and scaling back during normal operation, thereby improving detection capability while optimizing computing resource costs through responsive, condition-based resource management.
Data Source
AI summary
Various embodiments relate generally to electrical and electronic hardware, computer software and systems for controlling a data stream processor configured to detect and/or resolve anomalies in data streams including message data. In particular, a system, a device and a method may be configured to access multiple data streams and to detect an anomaly, in real-time or in substantially real-time, that is associated with at least one of the data streams accessed by a data stream processor. In some examples, a method can include one or more of receiving message data to facilitate a computerized rental of property, classifying subset of messages, fetching the classified messages to form multiple data streams, accessing the data stream to indemnity a stream characteristic, detecting an anomaly based on an identified stream characteristic, and generating anomaly resolution data to counteract the detected anomaly.