Distributed Computational Graph for Server Load Forecasting
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Solution Overview
Problem
Current distributed computing systems face challenges in accurately forecasting server load and optimizing resource allocation across remote servers and datacenters, due to limitations in forecasting server load and rigidity in implementation, which restricts their versatility and flexibility in handling varying server loads.
Innovation Solution
A system and method for optimization and load balancing using a distributed computational graph, multi-dimensional time-series databases for continuous load simulation, and traditional databases for discrete load simulation, combining real-time data and historical records to provide precise load forecasting and resource mapping for computer clusters, datacenters, or servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current distributed computing systems use traditional load balancing methods, then system structure is simple, but load forecasting accuracy is insufficient
Solution Approach 1:
The system segments the computational workflow into a distributed computational graph where tasks are divided into vertices and dependencies into edges. This segmentation allows independent analysis and optimization of individual tasks while maintaining overall system coordination, thereby improving load forecasting accuracy without overwhelming system complexity.
Solution Approach 2:
The patent introduces multi-dimensional time-series databases that add temporal and spatial dimensions to load forecasting. By analyzing historical data across multiple dimensions (time, resource type, task category), the system achieves more accurate predictions while organizing complexity into structured dimensional frameworks rather than unmanageable monolithic structures.
2Adaptability or versatility
If distributed systems use rigid operating systems or structures, then implementation is straightforward, but flexibility in handling varying server loads is restricted
Solution Approach 1:
The system implements dynamic load balancing by continuously analyzing multi-dimensional time-series data and adjusting task distribution in real-time. The computational graph and resource mapping are not static but adapt dynamically to changing server loads, providing flexibility while maintaining ease of operation through automated adjustment algorithms.
Solution Approach 2:
The patent creates a universal load balancing framework that can handle diverse task types and resource configurations through the distributed computational graph model. This universal approach accommodates varying server loads across different applications and hardware configurations without requiring implementation-specific modifications, thus providing flexibility without sacrificing operational simplicity.
3Productivity
If systems lack continuous load simulation and forecasting capabilities, then resource allocation is simple, but efficiency of operations over multiple devices is reduced
Solution Approach 1:
The system performs preliminary load simulation and forecasting using multi-dimensional time-series databases before actual task execution. By predicting future server loads and task completion times in advance, the system can proactively optimize resource allocation across multiple devices, improving operational efficiency while managing complexity through predictive rather than reactive allocation strategies.
Solution Approach 2:
The patent implements continuous feedback loops where load forecasting results from multi-dimensional time-series analysis are fed back into the distributed computational graph optimization process. This feedback mechanism enables iterative improvement of resource allocation efficiency across multiple devices while automating the complexity management through closed-loop control rather than manual intervention.
Data Source
AI summary
A system and method have been devised for optimization and load balancing for computer clusters, comprising a distributed computational graph, a server architecture using multi-dimensional time-series databases for continuous load simulation and forecasting, a server architecture using traditional databases for discrete load simulation and forecasting, and using a combination of real-time data and records of previous activity for continuous and precise load forecasting for computer clusters, datacenters, or servers.