Virtual machine-based serverless computing suffers from high memory overhead due to data duplication. This duplication occurs not only across function (or VM) images but also within in-memory caches shared between VMs. Such redundancy limits the scalability and efficiency of serverless computing infrastructure. While existing memory deduplication techniques can help, they are ill-suited to serverless computing due to their high CPU overhead and delayed deduplication, which often comes too late to provide meaningful memory savings given the short lifetime of a function. However, serverless platforms inherently know which functions (or VMs) will be executed in advance, presenting a unique opportunity for proactive memory optimization. In this paper, we propose CPC, a proactive deduplication scheme that enables efficient page sharing across microVMs, lightweight variants of traditional VMs, in serverless environments. Upon function submission, CPC deduplicates identical files across function images using existing techniques. During function execution, CPC leverages this deduplication information to further deduplicate redundant cache data across VMs and the hypervisor. Comprehensive experiments using serverless function benchmarks show that CPC reduces memory consumption by up to 32.0% compared to AWS Lambda when hosting heterogeneous functions on a shared worker node. Furthermore, with Azure Functions traces, CPC achieves a 41.2% reduction in 99th percentile tail latency by reducing unnecessary instance evictions through more efficient memory utilization.

Modern log-structured merge (LSM) tree-based key-value stores are widely used to process update-heavy workloads effectively as the LSM tree sequentializes write requests to a storage device to maximize storage performance. However, this append-only approach leaves many outdated copies of frequently updated key-value pairs, which need to be routinely cleaned up through the operation called compaction . When the system load is modest, compaction happens in background. However, at a high system load, it can quickly become the major performance bottleneck. To address this compaction bottleneck and further improve the write throughput of LSM tree-based key-value stores, we propose LAB-DB, which augments the existing LSM tree with a pair of B + trees on byte-addressable nonvolatile memory (NVM). The auxiliary B + trees on NVM reduce both compaction frequency and compaction time, hence leading to lower compaction overhead for writes and fewer storage accesses for reads. According to our evaluation of LAB-DB on RocksDB with YCSB benchmarks, LAB-DB achieves 94% and 67% speedups on two write-intensive workloads (Workload A and F), and also a 43% geomean speedup on read-intensive YCSB Workload B, C, D, and E. This performance gain comes with a low cost of NVM whose size is just 0.6% of the entire dataset to demonstrate the scalability of LAB-DB with an ever increasing volume of future datasets.