Edge Server Code Synchronization for Low-Latency Stream Processing
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Solution Overview
Problem
Current edge server networks experience latency and increased bandwidth due to real-time streaming data being processed at central servers, leading to delayed data delivery and inefficient network usage.
Innovation Solution
Implementing edge servers that synchronize code versions and perform real-time processing locally, pushing processed data directly to client devices while forwarding data to central servers for additional processing, and managing code updates to ensure consistent transform operations across edge servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time streaming data is processed at central servers, then data processing capability is improved, but latency increases and bandwidth usage increases
Solution Approach 1:
The patent segments the centralized processing architecture into distributed edge servers that perform local real-time processing. Edge servers are deployed at network edges closer to data sources and consumers, dividing the monolithic central server function into multiple distributed nodes. This segmentation enables data to be processed locally without traversing the entire network to central servers, thereby reducing latency while maintaining processing capability through distributed computation across multiple edge nodes.
Solution Approach 2:
The patent introduces a spatial dimension to the processing architecture by deploying edge servers at multiple geographic locations near data sources and consumers. Instead of a single centralized processing point, the system creates a multi-dimensional processing network where data can be handled at the nearest edge server. This dimensional expansion allows simultaneous local processing at multiple points, reducing the distance data must travel and thereby reducing latency while preserving processing throughput.
2Productivity
If real-time streaming data is processed at central servers, then data processing capability is improved, but bandwidth usage increases
Solution Approach 1:
The patent extracts the real-time processing function from central servers and relocates it to edge servers positioned at network perimeters. By taking out the processing capability from the centralized location and placing it at distributed edge locations, the system eliminates the need for data to traverse long network paths to central servers. This extraction of processing function to the edge reduces network bandwidth consumption for real-time data while maintaining processing throughput through distributed computation.
Solution Approach 2:
The patent enables edge servers to autonomously perform real-time data processing locally without requiring continuous communication with central servers. Edge servers self-manage the processing of real-time streaming data, transforming and filtering data at the source before it enters the core network. This self-service capability at the edge eliminates unnecessary network traffic to central servers, reducing bandwidth usage while preserving processing capability through autonomous local computation.
3Loss of time
If edge servers perform local real-time processing, then latency is reduced, but system complexity increases
Solution Approach 1:
The patent creates universal edge server nodes that can perform multiple functions: real-time data processing, data transformation, filtering, and local data storage. These multi-functional edge servers handle diverse data types and processing requirements through a standardized platform. By making edge servers universal and multi-functional, the system reduces the need for specialized hardware or complex custom configurations at each node, thereby managing system complexity while enabling widespread deployment of low-latency processing capabilities across the network.
Solution Approach 2:
The patent employs parameter changes to manage edge server complexity, including standardized processing parameters, configurable transformation rules, and adjustable data retention policies. Edge servers operate with configurable parameters that can be remotely updated without changing the underlying system architecture. This parameter-based control allows flexible adaptation to different data types and processing requirements while maintaining a consistent, manageable system structure, thereby reducing complexity despite the distributed nature of the system.
4Adaptability or versatility
If edge servers use different transform code versions, then adaptability is improved, but data consistency deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where edge servers report their transform code versions and processing results to a central management system. The management system tracks which edge servers have which code versions and coordinates updates across the network. This feedback loop enables the system to maintain awareness of code version distribution, allowing flexible deployment of different code versions to different edge servers based on local requirements while ensuring that data consistency is maintained through coordinated version management and transformation standardization enforced by the feedback mechanism.
Solution Approach 2:
The patent performs preliminary actions by pre-coordinating transform code updates across the edge server network before deployment. The central management system prepares and distributes updated transform code to edge servers in a controlled sequence, ensuring that all edge servers transition to compatible versions together. This preliminary coordination prevents data consistency issues that would arise from uncoordinated version updates, allowing the system to maintain adaptability through flexible update scheduling while preserving data consistency through advance planning and coordinated deployment.
Data Source
AI summary
Systems and methods provide synchronizing edge server code among a plurality of edge servers. Systems and methods provision, to a plurality of edge servers, an updated version of transform code adapted to perform real-time processing on real-time streaming data that are received by the plurality of edge servers, receive real-time transformed data from at least one of the plurality of edge servers, detect that the received real-time transformed data from the at least one of the plurality of edge servers was generated using a previous version of transform code and perform one or more transform operations corresponding to the updated version of transform code on the received real-time transformed data, until the received transformed data is in a form consistent with the updated version of provisioned transform code.