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Creators/Authors contains: "Arpaci-Dusseau, Remzi H"

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  1. ; ; ; (, USENIX NSDI)
    USENIX (Ed.)
    Serverless computing has gained traction due to its event-driven architecture and “pay for use” (PFU) billing model. However, our analysis reveals that current billing practices do not align with true resource consumption. This paper challenges the prevailing SLIM (static, linear, interactive-only model) assumptions that underpin existing billing models, demonstrating that current billing does not realize PFU for realistic workloads. We introduce the Nearly Pay-for-Use (NPFU) billing model, which accommodates varying CPU and memory demands, spot cores, and preemptible memory. We also introduce Leopard, an NPFU-based serverless platform that integrates billing awareness into several major subsystems: CPU scheduler, OOM killer, admission controller, and cluster scheduler. Experimental results indicate that Leopard benefits both providers and users, increasing throughput by more than 2x and enabling cost reductions. 
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    Free, publicly-accessible full text available April 28, 2026
  2. ; ; ; ; ; ; ; ; (, USENIX FAST)
    USENIX (Ed.)
    We present Cloudscape, a dataset of nearly 400 cloud archi- tectures deployed on AWS. We perform an in-depth analysis of the usage of storage services in cloud systems. Our findings include: S3 is the most prevalent storage service (68%), while file system services are rare (4%); heterogeneity is common in the storage layer; storage services primarily interface with Lambda and EC2, while also serving as the foundation for more specialized ML and analytics services. Our findings provide a concrete understanding of how storage services are deployed in real-world cloud architectures, and our analysis of the popularity of different services grounds existing research. 
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    Free, publicly-accessible full text available February 25, 2026
  3. ; ; ; ; ; ; ; ; (, USENIX FAST)
    null (Ed.)
    We introduce non-hierarchical caching (NHC), a novel approach to caching in modern storage hierarchies. NHC improves performance as compared to classic caching by redirecting excess load to devices lower in the hierarchy when it is advantageous to do so. NHC dynamically adjusts allocation and access decisions, thus maximizing performance (e.g., high throughput, low 99%-ile latency). We implement NHC in Orthus-CAS (a block-layer caching kernel module) and Orthus-KV (a user-level caching layer for a key-value store). We show the efficacy of NHC via a thorough empirical study: Orthus-KV and Orthus-CAS offer significantly better performance (by up to 2x) than classic caching on various modern hierarchies, under a range of realistic workloads. 
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    Accepted Manuscript Full Text Available