PIDNN Controller for Dynamic Memory Bandwidth Allocation
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Solution Overview
Problem
In datacenter environments, workload performance is sensitive to shared hardware resources, leading to fluctuations and increased total cost of ownership due to overprovisioning to ensure acceptable performance, as shared resources are often underutilized when multiple workloads compete for resources.
Innovation Solution
A PID neural network (PIDNN) controller dynamically manages memory bandwidth allocation by integrating a neural network with a PID controller to adjust weights automatically, allowing for dynamic resource control and reducing the need for manual tuning, thereby optimizing resource allocation and reducing overprovisioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If shared hardware resources are overprovisioned to ensure acceptable performance of priority applications, then workload performance stability is improved, but datacenter total cost of ownership increases due to underutilized resources
Solution Approach 1:
The patent implements dynamic resource allocation by using a PID neural network controller that continuously adjusts memory bandwidth allocation based on real-time workload conditions. Instead of static overprovisioning, the system dynamically scales resource allocation to match actual demand, ensuring priority workloads receive sufficient resources while preventing waste during low-utilization periods. This dynamic control mechanism resolves the contradiction by making resource allocation adaptive rather than fixed.
Solution Approach 2:
The system employs a closed-loop feedback mechanism where the PID neural network controller monitors workload performance metrics (such as throughput and latency) and uses this feedback to continuously adjust memory bandwidth allocation. The controller receives feedback about actual resource utilization and performance outcomes, then modifies allocation decisions accordingly. This feedback loop enables the system to maintain workload performance stability while optimizing resource utilization, eliminating the need for conservative overprovisioning.
2Measurement precision
If manual tuning is used to allocate shared resources, then control precision is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The patent implements self-service through the PID neural network controller that automatically tunes and adjusts resource allocation parameters without requiring manual intervention. The controller self-adapts to changing workload conditions by processing performance feedback and autonomously modifying memory bandwidth allocation. This self-service capability eliminates the need for manual tuning while maintaining high control precision, resolving the contradiction between precise control and operational simplicity.
Solution Approach 2:
The system replaces manual mechanical tuning processes with an automated neural network-based control system. Instead of requiring operators to manually adjust parameters based on experience and observation, the PID neural network uses machine learning algorithms to automatically optimize resource allocation. This substitution of manual mechanical adjustment with automated intelligent control achieves precise resource allocation while dramatically reducing operational complexity.
Data Source
AI summary
Examples described herein relate to circuitry to utilize a proportional, derivative, integral neural network (PIDNN) controller to adjust one or more parameters allocated to a first group of one or more workloads based on one or more target parameters for a second group of one or more workloads. In some examples, the second group of one or more workloads are a same, lower, or higher priority level than that of the first group of one or more workloads.