Cloud Hardware Accelerator Selection via Multi-Cloud Deployment
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Solution Overview
Problem
Current computer systems face inefficiencies in performance due to traditional system architectures, where compute power is not optimally positioned relative to data, leading to bottlenecks and suboptimal execution times and costs in processing requests.
Innovation Solution
The implementation of a cloud-based hardware accelerator system using OpenCAPI, which deploys accelerator images to multiple clouds, monitors execution times and costs, and selects the most efficient cloud-based accelerator for subsequent requests based on defined criteria, dynamically generating and deploying hardware accelerators to optimize compute-intensive tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional system architecture is used, then system simplicity is maintained, but compute power is not optimally positioned relative to data, leading to performance bottlenecks
Solution Approach 1:
The system segments compute functions into separate hardware accelerators that can be independently deployed and positioned closer to data sources. The accelerator manager divides the task of selecting and managing accelerators across multiple clouds, allowing each component to operate independently while contributing to overall system performance.
Solution Approach 2:
The patent introduces a new dimension of spatial distribution by deploying hardware accelerators across multiple cloud environments rather than concentrating them in a single location. This multi-cloud architecture allows compute power to be positioned optimally relative to data sources in different geographical and logical locations, resolving the contradiction between performance and complexity.
2Loss of time
If hardware accelerators are deployed to multiple clouds, then execution time and cost optimization is achieved, but system complexity increases
Solution Approach 1:
The accelerator manager performs preliminary actions by pre-deploying accelerator images to multiple clouds and pre-establishing monitoring mechanisms before actual compute tasks are executed. This allows the system to have accelerators ready and positioned in advance, reducing execution time while the preliminary setup manages the complexity of multi-cloud deployment.
Solution Approach 2:
The system implements continuous feedback loops where the accelerator manager monitors execution characteristics of hardware accelerators in real-time and uses this feedback to dynamically select optimal accelerators for subsequent requests. This feedback mechanism optimizes execution time while automating the complexity of managing multiple cloud deployments.
3Productivity
If cloud-based hardware accelerators are selected based on monitored characteristics, then processing efficiency improves, but monitoring and selection complexity increases
Solution Approach 1:
The accelerator manager serves multiple functions simultaneously: it deploys accelerator images, monitors execution characteristics, selects optimal accelerators, and routes subsequent requests. This universal component consolidates the complexity of monitoring and selection into a single multi-functional system that improves overall processing efficiency.
Solution Approach 2:
The system implements self-service mechanisms where the accelerator manager automatically monitors accelerator performance, selects optimal accelerators based on monitored characteristics, and routes requests without external intervention. This automation improves processing efficiency while the self-service nature manages the complexity internally rather than requiring external coordination.
Data Source
AI summary
A cloud-based hardware accelerator is selected by deploying an accelerator image to first and second clouds to generate first and second cloud-based hardware accelerators, executing a first request on the first and second cloud-based hardware accelerators, monitoring characteristics of the first and second cloud-based hardware accelerators executing the first request, which may include execution time and monetary cost, and selecting one of the first and second hardware accelerators according to defined selection criteria. Subsequent requests are then routed to the selected cloud-based accelerator.