Cloud AIOps Service With Bidirectional Data Transport Layer
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Solution Overview
Problem
Traditional on-premises deployments of Artificial Intelligence for IT Operations (AIOps) services limit the ability to leverage insights from one customer site in real-time to benefit other customers, due to lack of connectivity and security concerns with conventional cloud-based connection methods.
Innovation Solution
A bidirectional intermediate data transport layer, such as HPE's Remote Device Access technology, is used to connect multiple customers to a shared, cloud-based AIOps service, maintaining data isolation between customers while enabling dynamic leveraging of insights across sites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AIOps services are deployed on-premises to ensure data security, then data security is improved, but the ability to share insights across customers in real-time deteriorates
Solution Approach 1:
The patent introduces an intermediary cloud-based AIOps service platform that receives data from multiple on-premises deployments, processes it centrally, and distributes insights back to customers. This mediator enables cross-customer insight sharing while maintaining the security architecture where customer data remains primarily on-premises, resolving the contradiction between data security and insight sharing capability
Solution Approach 2:
The patent transitions the AIOps service from a single-location on-premises deployment to a multi-dimensional cloud-based architecture that serves multiple customers simultaneously. By adding the cloud platform dimension, the system enables horizontal insight sharing across customers while maintaining vertical security boundaries for each customer's data
2Reliability
If AIOps services are deployed on-premises to ensure data security, then data security is improved, but real-time optimization across multiple sites deteriorates
Solution Approach 1:
The cloud-based AIOps platform acts as an intermediary that aggregates data from multiple on-premises sites, performs centralized real-time analysis, and pushes optimization recommendations back to each site. This enables cross-site real-time optimization while maintaining the secure on-premises data architecture
Solution Approach 2:
The patent creates a universal cloud-based AIOps platform that serves multiple customers and sites with a single deployment. This multi-functional platform can analyze data from diverse sources and provide optimized insights across different sites simultaneously, enabling real-time optimization capability that would be impossible with isolated on-premises deployments
Data Source
AI summary
Systems and methods are provided for utilizing a bidirectional intermediate data transport layer to connect multiple customers to a shared, cloud-based AIOps service. As a feature of the intermediate data transport layer, each customer (and their data) may be isolated from other customers. In various examples, the cloud-based AIOps service may receive sensor data from customer systems via the intermediate data transport layer. The cloud-based AIOps service may analyze this sensor data (e.g. detect anomalies, perform root cause analyses, find optimal conditions, etc.), and modify the operation of one or more of the customer systems (e.g. modify the settings/configuration of a sensor on a piece of connected infrastructure) via the intermediate data transport layer. In certain examples, in addition to (or instead of) modifying the operation of one or more of the customer systems, the cloud-based AIOps service may provide an on-premises notification to a customer.