NUMA-Aware Container Scheduling for Latency-Sensitive Workloads
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Solution Overview
Problem
Conventional container-orchestration systems lack the ability to select appropriate hardware partitions for executing latency-sensitive workloads due to a lack of knowledge about resource availability at a granular level, which affects performance in computer architectures like 5G Telco systems.
Innovation Solution
The scheduler in the container-orchestration system queries host systems for available resources at each hardware partition, generates scheduling hints to identify optimal partitions, and assigns workloads accordingly, enabling intelligent selection based on resource availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the scheduler uses conventional resource monitoring, then the system can track aggregate resource availability, but it cannot identify specific hardware partitions with sufficient resources for latency-sensitive workloads
Solution Approach 1:
The patent segments the host system into multiple hardware partitions (e.g., NUMA nodes) and tracks resource availability at each partition level independently. The scheduler queries and maintains resource state for each partition separately, enabling precise identification of suitable partitions for latency-sensitive workloads without requiring complex global optimization algorithms.
2Productivity
If the scheduler queries resource availability at each hardware partition, then it can make informed scheduling decisions, but it increases the complexity of the scheduling process
Solution Approach 1:
The system performs preliminary actions by having host systems proactively report their resource availability states to the scheduler before workloads need scheduling. The scheduler maintains an updated view of partition resource states and uses this pre-collected information to quickly match workloads with suitable partitions, avoiding complex real-time analysis during the scheduling decision process.
3Reliability
If workloads are assigned without hardware partition awareness, then the scheduling process is simple, but latency-sensitive workloads experience performance degradation
Solution Approach 1:
The patent applies local quality by tailoring the scheduling approach to the specific requirements of latency-sensitive workloads. The scheduler identifies workloads requiring low latency and directs them to hardware partitions with sufficient resources, while other workloads can be scheduled using conventional methods. This selective application of partition-aware scheduling optimizes performance for critical workloads without unnecessarily complicating the overall system.
Data Source
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AI summary
A request to execute a workload that utilizes an amount of resources to be executed is received from a client device. Corresponding resources that are available at multiple nonuniform memory access (NUMA) nodes are received from one or more host systems. A particular NUMA node of the multiple NUMA nodes is identified in view of the particular NUMA node having available resources that are greater than the amount of resources to execute the workload. A scheduling hint is assigned to the workload that indicates that the particular NUMA node is to be used to execute the workload.