Computation Orchestration via Local Data Caching
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Solution Overview
Problem
Current computer network systems face challenges in efficiently managing and orchestrating computations across multiple hosts, particularly in terms of scalability, network bandwidth usage, and data immutability, while ensuring reproducibility and auditing capabilities.
Innovation Solution
A client-side filesystem and computation orchestrator system that utilizes a repository manager to store and manage data and program code, allowing clients to access and update data locally, cache credentials for efficient network usage, and orchestrate computations across hosts based on capabilities, ensuring immutability and reproducibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a centralized repository manager is used to manage computations across multiple hosts, then scalability and centralization are improved, but network bandwidth usage increases and system complexity increases
Solution Approach 1:
The repository manager performs preliminary actions by pushing data and program code to host filesystems before computations are needed. This advance provisioning allows hosts to cache and reuse data locally during computation execution, significantly reducing real-time network bandwidth requirements while maintaining centralized control.
2Adaptability or versatility
If a centralized repository manager is used to manage computations across multiple hosts, then scalability and centralization are improved, but device complexity increases
Solution Approach 1:
The system creates local copies of data and program code on host filesystems from the centralized repository. This copying mechanism simplifies the architecture by allowing hosts to operate independently with cached data, eliminating the need for complex real-time centralized coordination during computation execution while maintaining scalability.
3Reliability
If data is stored and managed centrally, then data immutability and auditing capabilities are improved, but network bandwidth usage increases
Solution Approach 1:
The repository manager performs preliminary actions by pushing data and program code to host filesystems before computations are needed. This advance provisioning allows hosts to cache and reuse data locally during computation execution, significantly reducing real-time network bandwidth requirements while maintaining centralized control.
4Productivity
If computations are orchestrated across multiple hosts, then productivity and resource efficiency are improved, but network bandwidth usage and system complexity increase
Solution Approach 1:
The repository manager performs preliminary actions by pushing data and program code to host filesystems before computations are needed. This advance provisioning allows hosts to cache and reuse data locally during computation execution, significantly reducing real-time network bandwidth requirements while maintaining centralized control.
Solution Approach 2:
The system creates local copies of data and program code on host filesystems from the centralized repository. This copying mechanism simplifies the architecture by allowing hosts to operate independently with cached data, eliminating the need for complex real-time centralized coordination during computation execution while maintaining scalability.
Data Source
AI summary
Methods, systems, and computer-readable media for orchestration of computations using a remote repository are disclosed. A representation of one or more inputs to a computation is stored in a repository. The computation is assigned to one or more hosts of a plurality of hosts. A representation of program code executable to perform the computation is stored in the repository. A local copy of the one or more inputs is stored on the one or more hosts. The computation is initiated on the one or more hosts using the program code and the local copy of the one or more inputs. The computation is initiated for a plurality of keys. The computation succeeds for one or more keys after the computation has failed for one or more other keys. A representation of one or more outputs of the computation is stored in the repository.