NUMA-Based Client Placement in Distributed Systems
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Solution Overview
Problem
Current client placement strategies in distributed computer systems do not effectively utilize multi-processor memory architectures, particularly Non-Uniform Memory Access (NUMA) architectures, leading to suboptimal resource management and inefficient placement of resource-consuming clients like virtual machines.
Innovation Solution
A management server and method that consider multi-processor memory architectures, including NUMA architectures, to recommend optimal host computers for client placement and migration by calculating NUMA scores based on virtual and physical processor ratios, enabling better resource allocation and load balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current client placement strategies are used, then placement decisions can be made, but resource utilization efficiency deteriorates due to ignoring NUMA architecture characteristics
Solution Approach 1:
The patent applies local quality by considering the specific NUMA architecture characteristics of each host computer when making client placement decisions. Instead of using a uniform placement strategy across all hosts, the system evaluates memory access patterns and NUMA node configurations specific to each host, placing clients on hosts where they will have optimal memory access performance. This resolves the contradiction by adapting placement strategies to local architectural qualities rather than applying a one-size-fits-all approach.
Solution Approach 2:
The patent changes the parameters considered in client placement decisions by incorporating NUMA-specific metrics such as memory access times, NUMA node configurations, and memory bandwidth characteristics. These parameter changes enable the placement strategy to account for multi-processor memory architecture differences, improving resource utilization efficiency without being overwhelmed by the complexity, as the system focuses on key NUMA parameters rather than all possible architectural details.
2Productivity
If NUMA architecture information is considered in client placement, then resource allocation efficiency improves, but decision-making complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-evaluating and storing NUMA architecture characteristics of host computers before client placement decisions are made. The system prepares NUMA topology information, memory access patterns, and host compatibility metrics in advance, so that when a client needs to be placed, the management server can quickly match clients to suitable hosts using pre-computed data. This reduces the complexity of real-time decision-making while maintaining high resource allocation efficiency.
Solution Approach 2:
The patent introduces an intermediary layer (the management server) that mediates between client placement requirements and host NUMA architecture characteristics. This intermediary collects, processes, and analyzes NUMA architecture information from multiple hosts, then presents simplified placement recommendations to the placement decision process. By acting as an intermediary, the system handles the complexity of NUMA architecture analysis centrally, allowing individual placement decisions to remain relatively simple while still achieving high resource allocation efficiency.
3Ease of operation
If raw processor capacity is used for client placement, then placement decisions are simple to make, but placement optimality deteriorates
Solution Approach 1:
The patent applies partial action by selectively incorporating NUMA architecture considerations into client placement decisions rather than requiring complete analysis of all host characteristics. The system focuses on the most critical NUMA parameters (such as memory access patterns and NUMA node configurations) that have the greatest impact on client performance, ignoring less significant architectural details. This partial approach maintains relative simplicity in decision-making while achieving improved placement optimality compared to using only raw processor capacity.
Data Source
AI summary
A management server and method for performing resource management operations in a distributed computer system takes into account information regarding multi-processor memory architectures of host computers of the distributed computer system, including information regarding Non-Uniform Memory Access (NUMA) architectures of at least some of the host computers, to make a placement recommendation to place a client in one of the host computers.