Predictive EQ Model for Dynamic HPC Resource Allocation
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Solution Overview
Problem
High-performance computing (HPC) systems in multi-tenant environments face challenges in predicting workload impact and resource allocation, leading to poor system utilization, high costs, and unacceptable queue times due to the difficulty in correlating Quality of Service (QoS) with efficiency, especially in dynamic and shared resource environments.
Innovation Solution
The development of a predictive Efficiency-Quality of Service (EQ) model that dynamically assigns and reassigns resources based on predicted workload needs, ensuring sustainable QoS and maximizing resource efficiency by calculating and monitoring QoS and efficiency in real-time, allowing for adaptive resource allocation and billing adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If computing resources are allocated statically in multi-tenant HPC environments, then system complexity is reduced, but Quality of Service prediction accuracy deteriorates and resource utilization efficiency worsens
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring workload characteristics and adjusting resource assignment in real-time. The system transitions from static to dynamic allocation, where resource assignments are updated based on changing workload demands, thereby maintaining accurate QoS predictions without excessive complexity.
Solution Approach 2:
The patent employs feedback mechanisms by monitoring actual workload performance and QoS metrics, then using this information to adjust future resource allocations. This closed-loop approach enables the system to learn from past allocations and improve prediction accuracy while managing complexity through data-driven decisions.
2Productivity
If more computing resources are allocated to HPC workloads, then processing speed and QoS are improved, but resource efficiency and cost-effectiveness worsen
Solution Approach 1:
The patent applies partial action by allocating resources dynamically based on actual workload needs rather than providing full resources continuously. The system allocates computing resources proportionally to the actual processing requirements, avoiding over-provisioning and improving resource efficiency while maintaining adequate processing speed.
Solution Approach 2:
The patent changes the allocation parameters dynamically by adjusting resource assignment based on workload characteristics, priority levels, and QoS requirements. This allows the system to optimize the balance between processing speed and resource efficiency by modifying allocation parameters in response to changing conditions.
3Loss of energy
If resource allocation is optimized for efficiency, then cost and energy usage are reduced, but QoS consistency and reliability deteriorate
Solution Approach 1:
The patent applies local quality by providing different levels of resource allocation to different workloads based on their specific QoS requirements and priority levels. High-priority workloads receive preferential resource allocation to maintain QoS consistency, while lower-priority workloads receive optimized allocation for cost efficiency, thereby achieving both goals simultaneously.
Solution Approach 2:
The patent uses dynamic resource allocation to adjust resource assignment in real-time based on workload demands and QoS requirements. This dynamic approach allows the system to maintain QoS consistency for critical workloads while optimizing resource efficiency for less critical tasks, balancing reliability and cost efficiency.
4Measurement precision
If predictive EQ modeling is implemented, then resource allocation accuracy and QoS sustainability are improved, but system complexity and computational overhead increase
Solution Approach 1:
The patent implements preliminary action by pre-computing workload characteristics and EQ ratings before actual resource allocation. The system analyzes workload patterns and predicts resource requirements in advance, enabling more accurate allocation decisions without adding significant complexity during runtime execution.
Data Source
AI summary
Systems and methods are provided for maintaining a desired efficiency of use of resources in a computing system, such as a high performance computing (HPC) system in conjunction with a desired quality of service (QoS) associated with performance of an application executed by the resources. Efficiency and QoS may be considered together, and the provided systems and methods optimize both during application runtime.