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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. 

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. 

Persistent memory (PM) can be accessed directly from userspace without kernel involvement, but most PM filesystems still perform metadata operations in the kernel for secuity and rely on the kernel for cross-process synchronization. We present per-file virtualization, where a virtualization layer implements a complete set of file functionalities, including metadata management, crash consistency, and concurrency control, in userspace. We observe that not all file metadata need to be maintained by the kernel and propose embedding insensitive metadata into the file for userspace management. For crash consistency, copy-on-write (CoW) benefits from the embedding of the block mapping since the mapping can be efficiently updated without kernel involvement. For cross-process synchronization, we introduce lockfree optimistic concurrency control (OCC) at user level, which tolerates process crashes and provides better scalability. Based on per-file virtualization, we implement MadFS, a library PM filesystem that maintains the embedded metadata as a compact log. Experimental results show that on concurrent workloads, MadFS achieves up to 3.6× the throughput of ext4-DAX. For real-world applications, MadFS provides up to 48% speedup for YCSB on LevelDB and 85% for TPC-C on SQLite compared to NOVA. 

Do some storage interfaces enable higher performance than others? Can one identify and exploit such interfaces to realize high performance in storage systems? This paper answers these questions in the affirmative by identifying nilexternality, a property of storage interfaces. A nil-externalizing (nilext) interface may modify state within a storage system but does not externalize its effects or system state immediately to the outside world. As a result, a storage system can apply nilext operations lazily, improving performance. In this paper, we take advantage of nilext interfaces to build high-performance replicated storage. We implement SKYROS, a nilext-aware replication protocol that offers high performance by deferring ordering and executing operations until their effects are externalized. We show that exploiting nil-externality offers significant benefit: for many workloads, SKYROS provides higher performance than standard consensus-based replication. For example, SKYROS offers 3x lower latency while providing the same high throughput offered by throughput-optimized Paxos. 

We present uFS, a user-level filesystem semi-microkernel. uFS takes advantage of a high-performance storage development kit to realize a fully-functional, crash-consistent, highly-scalable filesystem,with relative developer ease. uFS delivers scalable high performance with a number of novel techniques: careful partitioning of in-memory and on-disk data structures to enable concurrent access without locking, inode migration for balancing load across filesystem threads, and a dynamic scaling algorithm for determining the number of filesystem threads to serve the current workload. Through measurements, we show that uFS has good base performance and excellent scalability; for example, uFS delivers nearly twice the throughput of ext4 for LevelDB on YCSB workloads. 

We introduce consistency-aware durability or Cad, a new approach to durability in distributed storage that enables strong consistency while delivering high performance. We demonstrate the efficacy of this approach by designing cross-client monotonic reads, a novel and strong consistency property that provides monotonic reads across failures and sessions in leader-based systems; such a property can be particularly beneficial in geo-distributed and edge-computing scenarios. We build Orca, a modified version of ZooKeeper that implements Cad and cross-client monotonic reads. We experimentally show that Orca provides strong consistency while closely matching the performance of weakly consistent ZooKeeper. Compared to strongly consistent ZooKeeper, Orca provides significantly higher throughput (1.8--3.3×) and notably reduces latency, sometimes by an order of magnitude in geo-distributed settings. We also implement Cad in Redis and show that the performance benefits are similar to that of Cad’s implementation in ZooKeeper. 

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. 

We introduce consistency-aware durability or CAD, a new approach to durability in distributed storage that enables strong consistency while delivering high performance. We demonstrate the efficacy of this approach by designing cross-client monotonic reads, a novel and strong consistency property that provides monotonic reads across failures and sessions in leader-based systems. We build ORCA, a modified version of ZooKeeper that implements CAD and cross-client monotonic reads. We experimentally show that ORCA provides strong consistency while closely matching the performance of weakly consistent ZooKeeper. Compared to strongly consistent ZooKeeper, ORCA provides significantly higher throughput (1.8 – 3.3×), and notably reduces latency, sometimes by an order of magnitude in geo-distributed settings. 

We introduce the scheduler subversion problem, where lock usage patterns determine which thread runs, thereby subverting CPU scheduling goals. To mitigate this problem, we introduce Scheduler-Cooperative Locks (SCLs), a new family of locking primitives that controls lock usage and thus aligns with system-wide scheduling goals; our initial work focuses on proportional share schedulers. Unlike existing locks, SCLs provide an equal (or proportional) time window called lock opportunity within which each thread can acquire the lock. We design and implement three different scheduler-cooperative locks that work well with proportional-share schedulers: a user-level mutex lock (u-SCL), a reader-writer lock (RWSCL), and a simplified kernel implementation (k-SCL). We demonstrate the effectiveness of SCLs in two user-space applications (UpScaleDB and KyotoCabinet) and the Linux kernel. In all three cases, regardless of lock usage patterns, SCLs ensure that each thread receives proportional lock allocations that match those of the CPU scheduler. Using microbenchmarks, we show that SCLs are efficient and achieve high performance with minimal overhead under extreme workloads.