Dynamic Workload Balancing for Real-Time Stream Analytics
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Solution Overview
Problem
Data streaming systems face bottlenecks in timely information delivery due to lack of coordination between intermediate processing sites, leading to inefficient processing throughput and inadequate data analytics, which can result in delayed information delivery to consumers despite increased hardware and network connectivity.
Innovation Solution
Implementing dynamic workload balancing using real-time stream data analytics, where analytic nodes collect histograms to detect skewness and a coordinating node computes an optimal data distribution map to dynamically rebalance workloads across processing units, ensuring efficient data distribution and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If intermediate processing sites are replicated over the network for processing throughput efficiency, then processing throughput is improved, but coordination between different types of intermediate sites is lacking导致 overall processing efficiency deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where intermediate processing sites report their workload status and performance metrics to a coordination system. This feedback enables dynamic adjustment of data distribution policies to balance load across replicated processing sites, resolving the contradiction between improved throughput and coordination complexity.
Solution Approach 2:
The patent introduces a coordination system that acts as an intermediary between different types of intermediate processing sites. This mediator collects analytics data from various sites, computes optimal data distribution strategies, and enforces coordinated routing policies, thereby enabling efficient coordination without direct complex interactions between all processing sites.
2Productivity
If more hardware and network connectivity are added to improve processing throughput, then network capacity is improved, but useful data analytics gathering and sharing remains insufficient
Solution Approach 1:
The patent performs preliminary analytics processing at intermediate sites before data reaches the consumer. By gathering and analyzing data metrics, workload patterns, and performance indicators at the source and intermediate points, the system makes informed routing decisions that optimize both throughput and analytics quality without requiring additional hardware.
Solution Approach 2:
The patent combines data processing and analytics gathering functions into a unified system architecture. Intermediate processing sites simultaneously perform data enhancement tasks and collect analytics metrics, merging these functions to improve overall system efficiency without adding separate analytics infrastructure.
3Speed
If data is delivered rapidly from a processing efficient site to the consumer, then processing speed is improved, but the next intermediate site may become overloaded causing further delays
Solution Approach 1:
The patent implements dynamic workload balancing that continuously monitors the capacity and load status of intermediate processing sites. The system dynamically adjusts data routing in real-time based on current conditions, redirecting data flows to prevent overload at any single site while maintaining optimal delivery speed through coordinated multi-site processing.
Data Source
AI summary
Router threads read real-time stream data as that data is received at processing nodes along a processing path for the data. The data is initially parsed into workloads. Each router thread maintains real-time analytics for the workloads and reports deviations in the analytics to a workload coordinator. The workload coordinator dynamically and in real time provides updated data distribution maps based on the reported deviations, and the router threads use the data distribution maps to determine a next processing unit to forward the workloads, where the next processing unit includes an instance of a next processing node in the processing path for the workload. The next processing node performs additional processing on the workloads along the processing path.