Workload Distribution Node Orchestrating Analytics Across Clusters
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Solution Overview
Problem
Existing distributed data processing frameworks face challenges in handling geographically dispersed data across multiple sites with different ownership and formats, requiring a shared distributed file system which is difficult to configure and maintain, and raises privacy concerns when data is copied to a centralized site for analysis.
Innovation Solution
A multi-cluster distributed data processing platform that orchestrates analytics workloads across multiple clusters without a shared file system, using a WWH catalog to direct computations to local data resources, ensuring privacy and efficiency by processing data locally within each cluster and transferring only results between clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a shared distributed file system is deployed across all processing nodes, then data accessibility and processing capability are improved, but system complexity and difficulty of configuration and maintenance increase
Solution Approach 1:
The patent introduces a distributed file system interface layer that acts as an intermediary between processing nodes and data sources. This interface provides unified data access semantics without requiring actual shared storage infrastructure, thereby maintaining data accessibility while avoiding the complexity of deploying and maintaining shared distributed file systems across geographically dispersed nodes.
Solution Approach 2:
The system creates virtual copies of data access interfaces at each processing node, allowing nodes to access data locally through simulated file system operations. This copying approach enables data accessibility without physical data replication or shared storage infrastructure, reducing system complexity while maintaining functional equivalence to shared file system access.
2Productivity
If data is copied from local sites to a centralized site for analytics, then data processing capability is improved, but data transfer time and privacy concerns worsen
Solution Approach 1:
Instead of copying data from distributed local sites to a centralized processing location, the system inverts the approach by bringing processing capability to the distributed data locations. Analytics workloads are distributed across multiple processing nodes that access data locally, eliminating bulk data transfer while maintaining processing capability through parallel distributed computation.
Solution Approach 2:
The patent segments the centralized analytics workload into distributed micro-tasks that can be executed independently at local processing nodes. This segmentation allows data processing to occur in-place without requiring data movement, as each node processes its local data segment independently and results are aggregated to form the complete analytics output.
3Productivity
If data is copied to a centralized site for analytics, then processing capability is improved, but data privacy and security worsen
Solution Approach 1:
The system inverts the traditional centralized analytics model by distributing processing capabilities to multiple nodes that remain at their original geographic locations. This inversion allows analytics to be performed on local data without movement, preserving data privacy and security while maintaining processing capability through coordinated distributed computation.
Solution Approach 2:
The patent implements local quality by allowing each processing node to maintain its own data access and processing environment with locally-appropriate security and privacy policies. Each node processes data according to its local governance requirements while contributing to the overall distributed analytics computation, enabling processing capability without compromising data privacy through centralized aggregation.
4Adaptability or versatility
If a shared distributed file system is deployed across multiple geographic locations, then data accessibility is improved, but configuration and maintenance difficulty increase
Solution Approach 1:
The system introduces a virtual file system interface as an intermediary layer that provides unified data access semantics without requiring actual shared storage infrastructure across geographic locations. This interface abstracts away the complexity of distributed file system configuration and maintenance while maintaining data accessibility through local data access at each processing node.
Solution Approach 2:
The patent copies the file system interface functionality to each processing node rather than deploying a shared file system infrastructure across locations. This copying approach enables data accessibility through local interface implementations while avoiding the operational complexity of configuring and maintaining distributed file system infrastructure across multiple geographic sites.
Data Source
AI summary
An apparatus in one embodiment comprises at least one processing device having a processor coupled to a memory. The processing device implements a first workload distribution node configured to communicate with multiple distributed data processing clusters over at least one network. The workload distribution node is further configured to receive a data processing request, to identify particular ones of the distributed data processing clusters that are suitable for handling at least a portion of the data processing request, and to assign the data tasks to one or more of the distributed data processing clusters. Results of performance of the data tasks from the one or more assigned distributed data processing clusters are received by the first workload distribution node and aggregated into a response that is returned to a source of the data processing request. The source of the data processing request in some embodiments is another workload distribution node.