Accelerator Queue Rate Limiting for SLA-Aware Offloading
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Solution Overview
Problem
Processor overload and failure to meet Service Level Agreements (SLAs) due to unmanaged resource allocation and request handling by accelerator devices in cryptographic and compression tasks.
Innovation Solution
Implementing rate limiting and dynamic resource allocation using an AI model to manage queue allocation and device utilization, adjusting bandwidth limits, and prioritizing requests based on device load and SLA requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If requests are allocated to accelerator device without rate limiting, then processor can offload cryptographic and compression tasks to reduce computational loads, but accelerator device becomes overloaded and operations slow down
Solution Approach 1:
The patent implements dynamic rate limiting that adjusts request allocation to the accelerator device based on real-time utilization metrics. The system monitors accelerator device performance and dynamically modifies request rates, queue allocations, and bandwidth limits to prevent overload while maximizing offloading benefits. This dynamic adjustment resolves the contradiction by adapting the system behavior to current load conditions.
Solution Approach 2:
The system employs feedback mechanisms where utilization information from the accelerator device is continuously monitored and fed back to the rate limiting logic. This feedback loop enables the system to detect when the accelerator is approaching overload thresholds and automatically adjust request allocation accordingly, maintaining reliable operation while preserving productivity benefits.
2Productivity
If requests are allocated to accelerator device without rate limiting, then computational tasks are offloaded to accelerator, but Service Level Agreements (SLAs) are not met
Solution Approach 1:
The rate limiting system dynamically adjusts request allocation based on SLA requirements and accelerator utilization. When SLA compliance is at risk, the system modifies request rates and prioritization schemes to ensure critical tasks meet their service level agreements while still allowing non-critical tasks to utilize accelerator resources, thus maintaining both productivity and reliability.
3Reliability
If rate limiting is implemented to prevent accelerator overload, then accelerator device operations are maintained, but resource utilization efficiency decreases
Solution Approach 1:
The system uses dynamic rate limiting that increases request allocation to the accelerator device when utilization is low and decreases it when utilization approaches thresholds. This dynamic behavior maximizes resource utilization efficiency during low-load periods while maintaining accelerator stability during high-load periods, resolving the contradiction between these two objectives.
Solution Approach 2:
The patent changes key parameters such as request rates, queue allocations, and bandwidth limits based on accelerator utilization metrics. By adjusting these parameters dynamically, the system optimizes resource utilization efficiency while preventing overload, achieving both high productivity and reliable operation.
4Device complexity
If unmanaged resource allocation is used, then simple request handling is maintained, but processor overload and SLA failure occur
Solution Approach 1:
The patent introduces a rate limiting intermediary layer between the processor and accelerator device that manages resource allocation. This intermediary handles the complexity of utilization monitoring, dynamic parameter adjustment, and request prioritization, allowing the processor to maintain simple request handling while the intermediary ensures reliable operation and SLA compliance through sophisticated resource management.
Data Source
AI summary
Examples described herein relate to adjusting a queue size based on utilization of a device and an artificial intelligence (AI) model trained on at least one or more of: data size, request priority, device congestion, device latency, device interface throughput, network throughput, queue length, queue priority, request receipt rate, number of queues allocated to receive the requests, device memory usage, and/or whether address translation prefetch mode is enabled or not enabled. In some examples, the device includes an accelerator to perform cryptographic and/or compression operations in response to the requests.