CXL Memory Pooling for Cross-Host DRAM Reallocation
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Solution Overview
Problem
Efficient utilization of underutilized DRAM across multiple hosts in distributed computing environments is challenging, leading to wasted memory resources.
Innovation Solution
A system utilizing Compute Express Link (CXL) to interconnect hosts and a resource composer that manages memory resources, enabling efficient allocation and mapping of unused DRAM across hosts by analyzing memory usage patterns and translating commands to ensure seamless access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If DRAM is allocated to processes on a single host, then memory access speed is improved, but memory resource utilization deteriorates due to underutilized DRAM
Solution Approach 1:
The patent combines DRAM resources from multiple hosts into a unified memory pool that can be dynamically shared across hosts. The memory management unit monitors usage patterns and reallocates unused DRAM from one host to another, merging previously isolated memory resources into a collaborative pool that improves overall utilization while maintaining fast access speeds through localized caching strategies.
Solution Approach 2:
The patent introduces a memory management unit as an intermediary between hosts and DRAM resources. This mediator monitors memory usage patterns, identifies underutilized DRAM, and performs intelligent reallocation across hosts. The intermediary layer enables dynamic optimization of both access speed and resource utilization without requiring changes to the underlying hardware architecture.
2Stability of the object's composition
If DRAM is dedicated to specific hosts, then system stability is improved, but adaptability deteriorates due to inability to reallocate unused memory
Solution Approach 1:
The patent implements dynamic memory allocation where DRAM assignments are not fixed but continuously adjusted based on real-time usage patterns. The system transitions from static dedicated allocation to dynamic shared allocation, allowing memory resources to adapt to changing workload requirements while maintaining system stability through controlled reallocation mechanisms and monitoring.
Solution Approach 2:
The patent makes DRAM resources universal by enabling them to serve multiple hosts and different workloads sequentially. Instead of dedicating memory to specific hosts, the system creates multi-functional memory pools that can be allocated to any host that needs resources, increasing adaptability while maintaining stability through standardized allocation protocols and monitoring.
3Device complexity
If memory resources are distributed across hosts, then system complexity is reduced, but productivity deteriorates due to wasted memory capacity
Solution Approach 1:
The patent implements feedback mechanisms where the memory management unit continuously monitors DRAM usage patterns across hosts and uses this information to make reallocation decisions. The system collects performance data, analyzes utilization patterns, and adjusts memory assignments dynamically, creating a closed-loop control system that optimizes productivity while managing complexity through automated decision-making algorithms.
Data Source
AI summary
A Memory as a Service (MaaS) system enabling on-demand memory provisioning across distributed computing infrastructure comprising CPUs, GPUs, and accelerators. The system comprises a first host, a second host, and a computer interconnected via Compute Express Link (CXL). Each host runs packaged computing environments (PCEs) comprising containers or virtual machines. The computer monitors page table values from processes running in PCEs, identifies underutilized DRAM regions that remain unused for predetermined durations, and dynamically reallocates these memory resources as a service to memory-demanding processes on different hosts. This CXL-enabled MaaS architecture transforms static memory allocation into a flexible, consumption-based model, reducing data center memory waste while enabling real-time memory elasticity for cloud-native applications, AI/ML workloads, and multi-tenant environments.