Decentralized Computation Frameworks for Data Zone Coordination
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Solution Overview
Problem
Existing distributed data processing systems face challenges in efficiently performing computations across multiple data zones without a centralized controller, particularly in dynamically selecting computation locations and types to minimize resource usage and ensure data privacy and security.
Innovation Solution
The system recursively instantiates computations across data zones, allowing clients to send global computation requests that trigger downstream computations in other data zones, with each data zone dynamically selecting computation frameworks and managers to process locked data batches, enabling decentralized and efficient computation without a centralized orchestrator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a centralized controller is used to orchestrate computations across data zones, then computation coordination is simplified, but system complexity and single point of failure risk increase
Solution Approach 1:
Each data zone autonomously instantiates computation frameworks and manages its own locked data batches without external coordination. The system enables self-service computation where data zones independently select and execute appropriate computation types based on their local data characteristics and requirements.
Solution Approach 2:
The computation orchestration function is segmented and distributed across multiple data zones rather than centralized. Each data zone becomes an independent decision-making unit that can autonomously instantiate computation frameworks, thereby eliminating the single point of failure while maintaining coordination through standardized interfaces.
2Productivity
If data is centralized for processing, then computation efficiency improves, but data privacy and security risks increase
Solution Approach 1:
Computation is performed locally at each data zone on its own locked data batches, preserving data privacy while enabling efficient processing. Each data zone maintains control over its data with computations executed in-place, eliminating the need to transfer sensitive data to centralized locations.
Solution Approach 2:
Computation frameworks act as intermediaries that operate on locked data batches without requiring data to leave its home data zone. The framework manager coordinates computation instantiation while the actual processing occurs locally, mediating between the need for efficient computation and data privacy requirements.
3Use of energy by moving object
If computation frameworks are instantiated dynamically across multiple data zones, then resource usage is optimized, but system complexity increases
Solution Approach 1:
The system dynamically instantiates computation frameworks based on real-time computation requests and data zone capabilities. Frameworks are created on-demand and can be configured with specific computation types, allowing the system to adapt resource allocation to actual needs rather than maintaining static infrastructure.
Solution Approach 2:
A universal framework manager interface is established that can instantiate multiple types of computation frameworks across different data zones. This universal interface simplifies management complexity by providing standardized control mechanisms while enabling diverse computation types to be executed throughout the distributed system.
4Reliability
If data batches are locked to specific data zones, then data security is improved, but computation flexibility across zones is reduced
Solution Approach 1:
The computation framework serves as an intermediary that enables secure cross-zone computations without requiring data to leave its locked data zone. Frameworks can instantiate downstream computations in other data zones while maintaining data security boundaries, thereby preserving both data security and computation flexibility.
Solution Approach 2:
The system adds a temporal and logical dimension to data access by using computation frameworks that can reference and process locked data batches across zones through standardized interfaces. This dimensional approach allows data to remain physically locked in specific zones while enabling logical access and processing flexibility across the distributed system.
Data Source
AI summary
A computing device of a data zone includes a persistent storage and a processor. The persistent storage includes a locked data batch of the data zone. The processor obtains an upstream computation request; instantiates a computation framework to process the locked data batch based on a global data batch specified in the upstream computation request; instantiates a downstream computation manager to manage a downstream computation; and instantiates, by the downstream computation manager, a second computation framework in a second computing device of a second data zone to process a second locked data batch of the second data zone.