Pond: CXL-Based Memory Pooling Systems for Cloud Platforms (2203.00241v4)

Abstract: Public cloud providers seek to meet stringent performance requirements and low hardware cost. A key driver of performance and cost is main memory. Memory pooling promises to improve DRAM utilization and thereby reduce costs. However, pooling is challenging under cloud performance requirements. This paper proposes Pond, the first memory pooling system that both meets cloud performance goals and significantly reduces DRAM cost. Pond builds on the Compute Express Link (CXL) standard for load/store access to pool memory and two key insights. First, our analysis of cloud production traces shows that pooling across 8-16 sockets is enough to achieve most of the benefits. This enables a small-pool design with low access latency. Second, it is possible to create machine learning models that can accurately predict how much local and pool memory to allocate to a virtual machine (VM) to resemble same-NUMA-node memory performance. Our evaluation with 158 workloads shows that Pond reduces DRAM costs by 7% with performance within 1-5% of same-NUMA-node VM allocations.

• The paper introduces Pond, a system that leverages CXL hardware pooling and zNUMA nodes to reallocate up to 25% stranded DRAM.
• It employs machine learning models for dynamic memory management, keeping performance losses within a narrow 1-5% range.
• The evaluation demonstrates that Pond can reduce DRAM requirements by 7-9%, translating to significant cost savings for cloud providers.

Overview of "Pond: A Full-Stack Memory Pooling System for Cloud Providers"

The paper presents "Pond," a comprehensive system for memory pooling, leveraging emerging Compute Express Link (CXL) technology to address challenges faced by cloud providers in improving memory utilization. The central premise is to mitigate memory stranding, where a notable percentage of DRAM remains unused due to no corresponding CPU cores available to use it. This situation results in substantial costs and inefficiencies, which Pond aims to address through a combination of hardware and software solutions.

In empirical analysis, the authors reveal that up to 25% of memory can remain stranded in highly utilized server environments within cloud platforms like Azure. To combat this, the paper proposes a CXL-based design that reallocates unused memory across servers to better align memory resources with demand.

Key Components and Techniques

Pond encompasses hardware innovations and system-level software enhancements:

1. CXL-based Hardware Pooling: The paper outlines a hardware configuration utilizing CXL technology, which significantly reduces the latency of accessing pooled memory, compared to previous disaggregated memory systems. The design focuses on efficiency by proposing small pools of 8 to 16 sockets, achieving a balance between access latency and DRAM utilization gains.
2. System Software Innovations: A core component of Pond is the introduction of "zNUMA nodes," designated as memory-only NUMA nodes, which allow operating systems within VMs to seamlessly allocate memory. This setup aims to extend the illusion of a single coherent memory space while managing the higher latencies inherently associated with pooled memory.
3. Predictive Models and Dynamic Management: Utilizing machine learning models, Pond dynamically predicts the memory usage patterns and latency sensitivity of workloads. This prediction allows the system to allocate resources efficiently, potentially limiting performance losses to a narrow configurable bandwidth of 1-5%.

Performance and Economic Implications

The evaluation demonstrates Pond's capability to yield a reduction of DRAM needs by 7% to 9% with a negligible performance sacrifice, translating to significant cost savings potentially amounting to hundreds of millions of dollars annually for large cloud providers.

The authors emphasize the significance of making memory pooling practical for real-world deployments, particularly given the stringent performance requirements typical to public cloud workloads. Through simulations and prototype implementations, Pond has been shown to maintain performance margins within acceptable bounds, even under increased memory latencies posed by the use of pooled DRAM.

Future Prospects

The paper opens up numerous avenues for further exploration, especially in fine-tuning CXL-based architectures, expanding deployment scales beyond small memory pools, and enhancing ML-based workload predictions. Moreover, the work invites further research into broader applications of CXL, potentially leading to innovations in resource allocation strategies that could include other types of disaggregated resources.

In summary, this work provides a viable blueprint for enhancing memory utilization in cloud computing environments, leveraging modern interconnect standards and predictive analytics to produce a flexible and economic full-stack solution. Future iterations of Pond, based on growing CXL adoption, could see more sophisticated implementations, further enhancing resource efficiency and cost-effectiveness in the cloud infrastructure paradigm.