Automated Hardware Provisioning via Double-Blinded ILP Optimization
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Solution Overview
Problem
In distributed computing, the challenge lies in providing optimal hardware recommendations for workload deployment without compromising confidential information, as users and hardware providers remain 'blind' to each other's proprietary details, making it difficult to evaluate and recommend hardware effectively, especially in edge-to-cloud deployments.
Innovation Solution
The implementation of automated end-to-end (E2E) resource provisioning using double-blinded hardware recommendations, which employs integer linear programming (ILP) to ensure optimal hardware selections are made without human intervention, utilizing a client-server architecture that keeps confidential information secure and proprietary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users share workload information with hardware providers, then hardware recommendations can be optimized, but confidential and proprietary information is exposed
Solution Approach 1:
The patent introduces a service provider as an intermediary that receives workload information from users and hardware capability information from hardware providers. The service provider performs the hardware recommendation process without either party directly sharing confidential information with the other, thus maintaining security while enabling optimization.
Solution Approach 2:
The patent segments the hardware recommendation process into separate stages: workload analysis by the service provider, hardware capability evaluation by the hardware provider, and recommendation generation based on matched information. This segmentation allows each party to work with their own confidential data separately while achieving integrated optimization.
2Productivity
If hardware providers optimize every workload beforehand, then hardware performance is maximized, but the process becomes unrealistic and overly complex
Solution Approach 1:
The patent applies preliminary action by having the service provider analyze workload characteristics and create workload profiles before actual deployment. This preliminary analysis enables targeted hardware recommendations without requiring full optimization of every possible workload scenario in advance.
Solution Approach 2:
The patent changes the approach from optimizing hardware for every possible workload to optimizing hardware selections based on workload parameters and characteristics. The service provider evaluates workload parameters and matches them to appropriate hardware configurations, reducing complexity while maintaining performance.
3Productivity
If automated hardware recommendation systems are implemented, then resource provisioning efficiency is improved, but the system requires access to both workload and hardware performance information
Solution Approach 1:
The service provider acts as an intermediary that collects and processes both workload information and hardware capability information without requiring direct sharing between users and hardware providers. This enables automated recommendation systems to function while preserving information security through the mediating role.
Data Source
AI summary
In one embodiment, an apparatus comprises a communication interface to communicate over a network, and a processor. The processor is to: receive a workload provisioning request from a user, wherein the workload provisioning request comprises information associated with a workload, a network topology, and a plurality of potential hardware choices for deploying the workload over the network topology; receive hardware performance information for the plurality of potential hardware choices from one or more hardware providers; generate a task dependency graph associated with the workload; generate a device connectivity graph associated with the network topology; select, based on the task dependency graph and the device connectivity graph, one or more hardware choices from the plurality of potential hardware choices; and provision a plurality of resources for deploying the workload over the network topology, wherein the plurality of resources are provisioned based on the one or more hardware choices.