AI Pipeline Provider Switching for Resource-Aware Invoicing
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Solution Overview
Problem
Enterprises face challenges in managing AI pipelines due to lack of tools for adjusting resource utilization, leading to inefficiencies and increased costs, especially when using multiple datasets and models across different hyperscalers, with no real-time options for switching service providers.
Innovation Solution
A platform that allows tenants to create, manage, and dynamically select AI pipelines based on resource availability and cost, using a server to poll service providers for transaction information and forecast costs, enabling dynamic selection of service providers to minimize consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If enterprises use multiple hyperscalers for AI services, then service availability and compute capacity increase, but resource consumption and operational costs increase
Solution Approach 1:
The system dynamically selects service providers based on real-time conditions. The pipeline executor monitors resource consumption metrics and automatically switches between hyperscalers to optimize performance and cost, making the system adaptable rather than static in its provider selection
Solution Approach 2:
The system changes operational parameters by adjusting which service provider is used based on varying conditions such as resource availability, cost metrics, and performance requirements. This allows optimization of resource consumption while maintaining service availability
2Ease of manufacture
If enterprises fix service provider selection in advance, then pipeline setup is simpler, but real-time optimization of resource utilization is impossible
Solution Approach 1:
The system performs preliminary setup by pre-configuring multiple service provider options and establishing selection criteria in advance. This maintains ease of pipeline setup while enabling real-time optimization through automated execution logic that selects the best provider based on current conditions
3Loss of energy
If enterprises manually switch service providers, then control over resource consumption is achieved, but time and complexity increase
Solution Approach 1:
The pipeline executor automatically monitors resource consumption and switches between service providers without human intervention. The system self-manages the optimization process by evaluating metrics and making switching decisions autonomously, saving time and reducing operational complexity
Solution Approach 2:
The system implements continuous feedback loops where resource consumption metrics are monitored and fed back to the selection logic. This automated feedback mechanism enables real-time optimization without manual input, reducing both time and complexity compared to manual switching
4Reliability
If AI pipelines execute at fixed service providers, then execution stability is maintained, but cost optimization opportunities are lost
Solution Approach 1:
The system maintains execution stability through automated provider selection rather than fixed assignment. By dynamically choosing the most appropriate service provider based on real-time conditions, the system ensures both stability in execution outcomes and optimization of operational costs
Data Source
AI summary
Systems and methods are described for executing tenant artificial intelligence pipelines by dynamically selecting service providers based on resource consumption. A server can poll a group of service providers and receive resource information that indicates compute, network, storage, and token requirements to perform an action. When the tenant AI pipeline executes, a pipeline engine can select a service provider to execute the action based on stored resource information. The service providers available in the group can also dynamically change based on terms of service.