Midserver Data Aggregation for Cloud Telemetry
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Solution Overview
Problem
Large business enterprises face challenges in reliable data collection, management, and security when transferring data to cloud-based services due to heterogeneous data transfer issues, lack of standardized protocols, security concerns, and scalability problems with current data collection models, leading to network slowdowns and increased security risks.
Innovation Solution
A system and method utilizing mid-servers with virtual and physical worker agents to collect, aggregate, analyze, and securely transmit data from multiple computing devices and peripherals to cloud-based services, employing transformation sequences and secure connections to prepare data for transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If each computing device sends data to cloud-based service on separate connections, then data collection is straightforward, but security risks increase and network bandwidth is inefficiently utilized
Solution Approach 1:
The patent consolidates multiple separate data connections from numerous computing devices into a single aggregated connection through a midserver. This merging approach maintains reliable data collection from all devices while reducing security risks by minimizing the number of exposed connection endpoints at the network edge.
Solution Approach 2:
The midserver acts as an intermediary component between the computing devices and the cloud-based service. It receives data from multiple devices, aggregates and transforms it, then transmits through a single secure connection to the cloud service, thereby reducing security exposure while maintaining data collection reliability.
2Ease of manufacture
If multiple separate connections are used for data transmission, then data collection is simple, but bandwidth efficiency decreases due to unprioritized data transfer
Solution Approach 1:
The midserver performs preliminary data aggregation, transformation, and prioritization before transmission to the cloud service. By preprocessing data and establishing connection priorities in advance, the system optimizes bandwidth utilization while maintaining the simplicity of data collection from multiple devices.
3Ease of manufacture
If current data collection models are used, then implementation is straightforward, but scalability is poor when adding new data sources
Solution Approach 1:
The midserver provides a universal platform that can handle multiple data sources, transformation sequences, and target formats through a single system. This multi-functional approach enables easy scalability when adding new computing devices or data sources, as they can be integrated through the same standardized midserver infrastructure without requiring separate collection models.
4Reliability
If transformation sequences are applied to data streams, then data consistency is improved, but processing time increases
Solution Approach 1:
The transformation sequences are applied continuously to data streams as they pass through the midserver, rather than in batch processing. This continuous transformation approach maintains data consistency while minimizing processing delays by avoiding interruptions in the data flow.
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. The system and method make use of a plurality of virtual and physical worker agents which can be dynamically instantiated by a transformation engine to carry out one or more transformation sequences, based on pipeline instructions, to a received data stream to prepare the data for transmission as a target data stream format.