Distributed AI Workload Optimizer for Multi-Vendor Resource Allocation
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Solution Overview
Problem
Efficiently optimizing workload distribution across computing resources in distributed AI systems is challenging due to difficulties in maximizing the use of cloud/GPU computing resources across multiple vendors at scale, particularly in meeting constraints like time, budget, and regulatory requirements.
Innovation Solution
A distributed AI workload optimizer that uses an AI algorithm to build an optimization model based on processing constraints, contextual parameters, and availability data to identify optimal resource distribution options across multiple computing resources, including CPUs and GPUs, and adjusts resource allocation dynamically to meet specific task requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workload is distributed across multiple computing resources, then resource utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces an optimization service as an intermediary layer between the computing platform and multiple computing resources. This service receives workload requests, queries availability data from various resources, builds optimization models, and determines optimal resource distribution. By centralizing the decision-making logic in this intermediary service, the system achieves efficient multi-resource utilization without requiring complex distributed coordination across all resources, thus resolving the contradiction between resource utilization efficiency and system complexity.
2Reliability
If optimization model considers multiple constraints, then task completion reliability is improved, but computational overhead increases
Solution Approach 1:
The patent implements preliminary action by pre-building and maintaining an optimization model that incorporates multiple constraints (time, budget, regulatory requirements) before actual workload execution. The service queries availability data in advance and pre-determines optimal resource distribution strategies. When workloads arrive, the system can quickly apply pre-established optimization rules rather than performing complex real-time optimization, thus ensuring reliable task completion while minimizing computational overhead during execution.
3Measurement precision
If AI algorithm is used for workload optimization, then resource distribution accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by implementing a hierarchical optimization approach where the AI algorithm focuses on optimizing key decision parameters rather than evaluating all possible resource distributions. The system uses the AI model to determine optimal allocation strategies for critical resources while applying simpler rules for less critical decisions. This selective application of complex AI optimization maintains high resource distribution accuracy for最重要的 decisions while reducing overall processing time by avoiding exhaustive analysis of all parameters.
Data Source
AI summary
Arrangements for a distributed artificial intelligence workload optimizer are provided. In some aspects, a workload that identifies a number of computer processing cycles required to complete a task may be received. Processing constraints may be received from a user computing device. Contextual parameters associated with the workload may be received. Availability data for a plurality of resources in a distributed computing environment, each capable of performing at least part of the workload, may be acquired. Using an artificial intelligence algorithm, an optimization model for distributing the workload may be built based on the processing constraints, the contextual parameters, and the availability data. The optimization model may optimize the distribution of available resources allocated to executing the workload. Based on the optimization model, resource distribution options including an optimal distribution of the available resources for executing the workload may be identified, and the workload may be executed accordingly.