Midserver Architecture Consolidating Telemetry Data
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Solution Overview
Problem
Current data collection and transmission methods for cloud-based services in large business enterprises face challenges such as unreliable data collection, security concerns, bandwidth issues, and the inability to scale with new data sources, leading to complex and disorganized data management and security optimization.
Innovation Solution
A midserver architecture is introduced, integrating with both business enterprise and cloud-based infrastructure to collect, aggregate, analyze, transform, and securely transmit data from multiple computing devices and peripherals, using containerized services to process and prioritize data before transmission, thereby reducing the number of connections and enhancing security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate connections are used for data collection from thousands of computing devices, then data collection coverage is improved, but network security risks and system complexity increase
Solution Approach 1:
The patent consolidates thousands of separate data collection connections into a single midserver that aggregates all telemetry data. This merging approach maintains comprehensive data collection coverage while reducing system complexity by replacing numerous individual connection handlers with one centralized collection point.
Solution Approach 2:
The midserver acts as an intermediary component between the cloud-based service and thousands of computing devices. It receives, aggregates, and processes telemetry data from multiple sources before transmitting to the cloud service, thereby simplifying the overall system architecture while maintaining extensive data collection capabilities.
2Quantity of substance
If unfiltered data is transmitted from multiple devices, then data completeness is improved, but security concerns and network bandwidth consumption increase
Solution Approach 1:
The midserver performs preliminary filtering, aggregation, and processing of telemetry data before transmission to the cloud-based service. By pre-processing data at the edge (midserver location), the system maintains data completeness while eliminating unnecessary noise and potential security threats before they reach the central cloud service.
Solution Approach 2:
The midserver applies localized data processing and filtering rules specific to different device types and data sources. This allows the system to maintain comprehensive data collection while applying context-appropriate filtering to reduce security risks and bandwidth consumption based on the local characteristics of each data source.
3Speed
If constant streaming of telemetry data is implemented, then real-time monitoring capability is improved, but network bandwidth consumption and data transmission costs increase
Solution Approach 1:
The midserver implements periodic batching of telemetry data transmissions instead of continuous constant streaming. It aggregates data over defined time intervals and transmits in optimized batches, maintaining effective real-time monitoring capability while significantly reducing network bandwidth consumption compared to uninterrupted constant streaming.
Solution Approach 2:
The midserver maintains continuous data collection and processing operations while optimizing transmission timing. It continuously monitors and aggregates telemetry data in real-time, then transmits at optimal intervals when bandwidth conditions are favorable, preserving monitoring continuity while reducing overall bandwidth consumption.
Data Source
AI summary
A system and method that uses midservers located between the business enterprise computer infrastructure and the cloud-based infrastructure to collect, aggregate, analyze, transform, and securely transmit data from a multitude of computing devices and peripherals at an external network to a cloud-based service.