Multi-Level Memory Resource Allocation in Networked Nodes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional datacenters face inefficiencies in managing memory resources across a network of computing nodes due to varying topologies, memory speed, and capacity differences, leading to suboptimal memory allocation and high access latency.
Innovation Solution
Implementing a runtime system that dynamically discovers and allocates memory resources across multiple levels of memory modules (MLMs) interconnected in various topologies, using a memory hierarchy that accounts for performance characteristics and workload demands, and adjusts based on availability changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If remote memory resources are used to augment memory capacity, then memory capacity is improved, but access latency increases
Solution Approach 1:
The patent segments memory resources into multiple levels (L1, L2, L3, L4) based on proximity to the computing node. L1 is local memory, L2 is memory in directly connected nodes, L3 is memory in nodes one hop away, and L4 is memory in nodes two or more hops away. This segmentation allows the system to allocate memory requests to appropriate levels, balancing capacity and latency by preferring closer memory resources when possible.
Solution Approach 2:
The patent introduces a spatial dimension to memory management by considering network topology and physical distance between nodes. Instead of treating all remote memory equally, the system evaluates memory resources based on their topological distance from the computing node, creating a multi-dimensional memory hierarchy that optimizes for both capacity and access time.
2Productivity
If memory resources are dynamically apportioned across network nodes, then memory utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-discovering and cataloging memory resources across the network before actual memory allocation occurs. The system builds a topology map and identifies available memory resources in advance, so when memory allocation is needed, the system can quickly query pre-collected information rather than performing complex real-time discovery, thus reducing operational complexity while maintaining high utilization.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors memory usage, allocation patterns, and access performance. Based on this feedback, the memory management system dynamically adjusts allocation decisions, optimizing utilization efficiency while adapting to changing workloads without requiring manual reconfiguration or increasing system complexity.
3Adaptability or versatility
If multi-level memory modules are implemented across network nodes, then memory scalability is improved, but management complexity increases
Solution Approach 1:
The patent creates a universal memory management framework that can handle multiple memory levels (L1-L4) and various network topologies through a single standardized interface and set of protocols. The system uses unified discovery mechanisms, allocation algorithms, and monitoring approaches that work across different node configurations and memory hierarchies, enabling scalability without proportionally increasing management complexity.
Data Source
AI summary
A method of managing memory in a network of nodes includes identifying memory resources for each of the plurality of nodes connected to the network, storing memory resource information describing the memory resources, and based on the stored memory resource information, allocating a portion of the memory resources for execution of instructions in a workload, where at least a first node of the plurality of nodes is configured to execute the workload using the allocated portion of the memory resources.