Unified Data Control Policy in Distributed Computing
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Solution Overview
Problem
Implementing unified data control policies in distributed and product-oriented computing platforms is challenging due to the exponential increase in data control policies over time, complexity in managing various user entities and cloud-based software products, and the distributed nature of the computing platform, which makes it difficult to ensure correct policy enforcement across distributed data service clusters.
Innovation Solution
A centralized policy information repository is implemented, distributing replicated policy information objects to each distributed data service cluster, along with distributed policy decision and enforcement objects, to evaluate and enforce data control policies uniformly across the platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data control policies are implemented in distributed computing platforms, then data security and control are improved, but system complexity and difficulty of policy management increase exponentially
Solution Approach 1:
The system segments policy management into distinct functional components: policy definition, policy evaluation, and policy enforcement. Each distributed data service cluster contains policy enforcement objects that independently evaluate and enforce policies locally, while a centralized policy management system handles policy creation and distribution. This segmentation reduces overall system complexity by distributing policy enforcement responsibilities across multiple independent units.
Solution Approach 2:
The patent introduces policy evaluation objects as intermediaries between the centralized policy management system and distributed data service clusters. These evaluation objects receive policy definitions from the central system, evaluate them against local data, and enforce decisions without requiring direct central control over each enforcement action. This intermediary layer simplifies central management while maintaining distributed enforcement capability.
2Stability of the object's composition
If unified data control policies are enforced across distributed data service clusters, then policy consistency is improved, but communication overhead and evaluation time increase
Solution Approach 1:
The system performs preliminary policy evaluation by pre-processing policy definitions and evaluating them against available data before actual enforcement actions. Policy evaluation objects continuously monitor and evaluate policies in advance, so when enforcement is needed, decisions are already prepared or can be made quickly based on pre-evaluated results. This reduces real-time evaluation delays while maintaining consistency.
Solution Approach 2:
Each distributed data service cluster contains self-contained policy enforcement objects that independently evaluate and enforce policies without requiring continuous central system intervention. These objects use locally available information and pre-received policy definitions to make enforcement decisions autonomously, reducing communication overhead and evaluation time while maintaining unified policy application across the distributed system.
Data Source
AI summary
Various examples herein described are directed to methods, apparatuses, and computer program products for implementing unified data control policies in a distributed and product-oriented computing platform that includes a plurality of distributed data service clusters and a centralized policy information repository. In some examples, the distributed data service cluster is associated with a distributed policy decision object and one or more distributed policy enforcement objects. In some examples, the distributed policy decision object generates control policy evaluation responses in response to control policy evaluation requests. Additional example embodiments provide, such as but not limited to, example methods for generating policy information objects.