Dynamic Quota Management for Cloud Compute Saturation
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Solution Overview
Problem
Deploying transformer-based machine learning models as cloud-based services is costly due to high GPU resource utilization and inefficient resource management, with existing quota management systems failing to dynamically ensure saturation of compute resources and fair distribution of workloads.
Innovation Solution
A dynamic quota management system that adjusts task parallelism in real-time based on feedback signals from a quota service, scaling up or down to maintain resource utilization near the quota limit and ensure fair sharing across workloads, thereby maximizing GPU utilization and minimizing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If transformer models are deployed as cloud-based services with high GPU utilization, then model performance and capability are improved, but operational costs increase
Solution Approach 1:
The system dynamically adjusts the number of parallel task streams based on real-time resource availability and quota utilization. The concurrency manager continuously monitors feedback from the quota service and adapts the parallelism level, transitioning from static to dynamic resource allocation to optimize both performance and cost efficiency.
Solution Approach 2:
The system changes the parameter of task parallelism (number of concurrent streams) based on feedback signals indicating quota utilization levels. By adjusting this parameter dynamically, the system maximizes resource utilization when capacity is available while reducing consumption when quotas are approaching limits, thereby optimizing operational costs.
2Productivity
If the number of parallel task streams is increased to maximize resource utilization, then throughput is improved, but resource quota limits are exceeded causing congestion
Solution Approach 1:
The system implements a feedback loop where the quota service monitors resource utilization and provides feedback signals to the concurrency manager. Based on this feedback indicating whether quota limits are approaching, the system adjusts the number of parallel streams accordingly, ensuring throughput maximization while maintaining reliable quota compliance.
Solution Approach 2:
The concurrency manager autonomously adjusts task parallelism based on feedback signals without requiring external intervention. The system self-regulates by interpreting quota status and automatically modifying the number of concurrent streams, enabling self-service resource management that maintains both high throughput and quota compliance.
3Productivity
If dynamic adjustment of task parallelism is implemented to maintain quota saturation, then resource efficiency is improved, but system complexity increases
Solution Approach 1:
The system introduces a quota service as an intermediary that handles the complex task of monitoring resource utilization and generating feedback signals. This intermediary component absorbs the complexity of quota management, allowing the concurrency manager to focus on adjusting parallelism based on simple feedback indicators, thereby improving resource efficiency while managing system complexity through modular design.
Data Source
AI summary
A disclosed method facilitates an increase in utilization with respect to a resource quota allocated to a tenant from a shared resource pool. The method includes transmitting a lease request to a quota service on behalf of the tenant, where the lease request identifies a processing task and specifies quantity of cloud-based resources requested from the shared resource pool for execution of the processing task. The method further provides for determining, based on a feedback signal received from the quota service, whether grant of the lease request would cause the tenant to exceed a resource quota allocated to the tenant and dynamically decreasing parallelism of active tasks being processed by the cloud-based resources on behalf of the tenant in response to determining that grant of the lease request would cause the tenant to exceed the resource quota limit.