Data Processing Arbitration for Sensitive HPC Batch Workloads
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Solution Overview
Problem
The inefficient storage, transfer, and processing of large amounts of data in bioinformatics systems result in network resource wastage and performance lag, particularly due to manual data operation selection and varying availability and cost of remote computing resources, which complicates the management of sensitive data.
Innovation Solution
A data processing controller that automatically arbitrates between local and remote computing clusters to execute data processing operations, determining the suitability based on data sensitivity and resource availability, thereby optimizing data processing efficiency and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual data operation selection is used, then user control over data processing is maintained, but network resource efficiency deteriorates and performance lag increases
Solution Approach 1:
The system automatically selects and executes optimal data processing operations without requiring manual user intervention. The controller autonomously determines whether to process data locally or remotely based on predefined criteria, eliminating the need for users to manually select operations while optimizing network resource utilization.
Solution Approach 2:
The system changes the operational parameters by automatically determining processing location (local vs. remote) based on data characteristics and system state. This dynamic parameter selection optimizes network resource efficiency while maintaining ease of use through automated decision-making.
2Power
If remote computing resources are used, then processing capacity is increased, but system complexity increases due to varying availability and cost
Solution Approach 1:
The controller acts as an intermediary between the user application and the computing clusters. It manages the complexity of selecting between local and remote resources, handling availability and cost considerations automatically, while presenting a simplified interface to the user.
Solution Approach 2:
The system provides universal access to both local and remote computing resources through a unified interface. The controller manages multiple resource types (local clusters, remote clusters, cloud resources) with different availability and cost characteristics, abstracting their differences from the user.
3Power
If data is transferred to remote locations for processing, then computational power is accessed, but network resources are wasted due to inefficient transfer
Solution Approach 1:
The system dynamically changes the data processing location parameter based on the specific computational task requirements and current system state. This determines whether data should be transferred to remote locations or processed locally, optimizing the balance between accessing computational power and minimizing network resource waste.
Solution Approach 2:
The controller continuously monitors network conditions, resource availability, and task requirements to make real-time decisions about data processing location. This feedback mechanism ensures efficient network resource utilization while providing access to necessary computational power.
Data Source
AI summary
A data processing architecture controls data processing arbitration between a service provider, a local computing cluster, and a remote computing cluster. The architecture receives, by a data processing controller, a request to perform one or more data processing operations for a batch of data and determines, by the data processing controller, whether the batch of data includes private information. The data processing controller selects a computing cluster from a plurality of computing clusters based on a result of determining whether the batch of data includes private information, the plurality of computing clusters comprising a local cluster and a cloud cluster. The data processing controller instructs the selected computing cluster to access the batch of data and perform the one or more data processing operations.