arXiv subjects

Chloe Alverti

Publications and source records attributed to Chloe Alverti.

3 recordsLinked to original sources

arXiv2026

Towards CXL Resilience to CPU Failures

Compute Express Link (CXL) 3.0 and beyond allows the compute nodes of a cluster to share data with hardware cache coherence and at the granularity of a cache line. This enables shared-memory semantics for distributed computing, but introduces new resilience challenges: a node failure leads to the loss of the dirty data in its caches, corrupting application state. Unfortunately, the CXL specification does not consider processor failures. Moreover, when a component fails, the specification tries to isolate it and continue application execution; there is no attempt to bring the application to a consistent state. To address these limitations, this paper extends the CXL specification to be resilient to node failures, and to correctly recover the application after node failures. We call the system ReCXL. To handle the failure of nodes, ReCXL augments the coherence transaction of a write with messages that propagate the update to a small set of other nodes (i.e., Replicas). Replicas save the update in a hardware Logging Unit. Such replication ensures resilience to node failures. Then, at regular intervals, the Logging Units dump the updates to memory. Recovery involves using the logs in the Logging Units to bring the directory and memory to a correct state. Our evaluation shows that ReCXL enables fault-tolerant execution with only a 30% slowdown over the same platform with no fault-tolerance support.

cs.DC↗

arXiv2024

Chameleon: Adaptive Caching and Scheduling for Many-Adapter LLM Inference Environments

The widespread adoption of LLMs has driven an exponential rise in their deployment, imposing substantial demands on inference clusters. These clusters must handle numerous concurrent queries for different LLM downstream tasks. To handle multi-task settings with vast LLM parameter counts, methods like Low-Rank Adaptation (LoRA) enable task-specific fine-tuning while sharing most of the base LLM model across tasks. Hence, they allow concurrent task serving with minimal memory requirements. However, existing LLM serving systems face inefficiencies: they overlook workload heterogeneity, impose high link bandwidth from frequent adapter loading, and suffer from head-of-line blocking in their schedulers. To address these challenges, we present Chameleon, a novel LLM serving system optimized for many adapter environments, that relies on two core ideas: adapter caching and adapter-aware scheduling. First, Chameleon caches popular adapters in GPU memory, minimizing the adapter loading times. Importantly, it uses the otherwise idle GPU memory, avoiding extra memory costs. Second, Chameleon uses a non-preemptive multi-queue scheduling to efficiently account for workload heterogeneity. In this way, Chameleon simultaneously prevents head of line blocking and starvation. We implement Chameleon on top of a state-of-the-art LLM serving platform and evaluate it with real-world production traces and open-source LLMs. Under high loads, Chameleon reduces P99 and P50 TTFT latency by 80.7% and 48.1%, respectively, while improving throughput by 1.5x compared to state-of-the-art baselines.

cs.DC↗

arXiv2024

Squeezy: Rapid VM Memory Reclamation for Serverless Functions

Resource elasticity is one of the key defining characteristics of the Function-as-a-Service (FaaS) serverless computing paradigm. While compute resources assigned to VM-sandboxed functions can be seamlessly adjusted on the fly, memory elasticity remains challenging. Hot(un)plugging memory resources suffers from long reclamation latencies and occupies valuable CPU resources. We identify the obliviousness of the OS memory manager to the hotplugged memory as the key issue hindering hot-unplug performance, and design Squeezy, a novel approach for fast and efficient VM memory hot(un)plug, targeting VM-sandboxed serverless functions. Our key insight is that by segregating hotplugged memory regions from regular VM memory, we are able to bound the lifetime of allocations within these regions thus enabling their fast and efficient reclamation. We implement Squeezy in Linux v6.6 as an extension to the OS memory manager. Our evaluation reveals that Squeezy is an order-of-magnitude faster than state-of-the-art, keeping tail latency bounded, when reclaiming VM memory, achieving sub-second reclamation of multiple GiBs of memory while serving realistic FaaS load.

cs.OS↗