Hybrid Cloud Data Control for Local Energy Data Processing
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Solution Overview
Problem
Energy companies face challenges in integrating cloud services with their domain-specific computing infrastructure due to data sensitivity, large data volumes, high latency, and fragmented ecosystems, leading to increased costs and complexity in data processing and storage.
Innovation Solution
A hybrid cloud environment is implemented with a cloud-service-managed control plane and data plane utilizing local compute resources and storage, enabling uniform management and deployment of cloud service functions to enforce data transmission and storage restrictions, process energy-related data, and extract features locally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud services are integrated with domain-specific computing infrastructure, then data processing capability is improved, but system complexity increases
Solution Approach 1:
The system is segmented into a control plane managed by cloud services and a data plane utilizing local domain-specific infrastructure. This segmentation allows independent optimization of each layer, reducing overall system complexity while maintaining enhanced data processing capability through coordinated operation of both planes.
Solution Approach 2:
A hybrid cloud environment acts as an intermediary layer between cloud services and domain-specific computing infrastructure. This intermediary manages the integration, handling communication and coordination protocols, thereby improving data processing capability without directly increasing the complexity of the core components.
2Reliability
If data transmission restrictions are enforced, then data security and compliance are improved, but data transmission efficiency deteriorates
Solution Approach 1:
Different quality requirements are applied to different data streams based on their security and compliance needs. The control plane enforces data transmission restrictions selectively, applying appropriate security measures only where required, thereby maintaining data security and compliance without unnecessarily restricting overall data transmission efficiency.
3Loss of time
If local compute resources are utilized, then latency is reduced, but infrastructure cost increases
Solution Approach 1:
The system dynamically allocates compute workloads between cloud services and local domain-specific infrastructure based on real-time requirements. Time-sensitive operations are processed locally to reduce latency, while less time-critical tasks utilize cloud resources, thereby optimizing the balance between latency reduction and infrastructure cost management.
Data Source
AI summary
A remote server computing system is configured to present a user interface with a plurality of deployment configuration options including compute configuration options and data storage configuration options for energy-related data within a hybrid cloud environment. The hybrid cloud environment comprises a cloud-service-managed control plane and a data plane utilizing local compute resources and storage. A data control policy is generated that provides cloud-service-managed governance over at least a portion of the data plane. The control plane is configured to enforce the data control policy by subjecting at least a portion of the energy-related data to a data transmission restriction or a local storage restriction. The data plane is used to deploy one or more cloud service functions configured to process at least the portion of the energy-related data and output one or more extracted features from at least the portion of the energy-related data to the data plane.