Aug 2026· IEEE International Conference on Embedded and Real-Time Computing Systems and Applications· pp. 230-231· 0 citations· 6 references
Abstract
Log-structured merge-tree (LSM-tree) key-value stores rely on caching to mitigate multi-component lookups and long tails, yet block and KP caches are prone to compactioninduced expiry, and all three cache types suffer from scan pollution under LRU eviction. We present AutoThermKV, a two-level in-memory architecture that decouples fast admission from hot-item protection. The front tier (HotFilter) uses a 64 Baligned bitmap/key/value page layout with Quick Space Reservation for near-constant admission cost; the back tier (HotCommitted) retains proven hot items via a lightweight LRU and a secondtouch promotion rule. Implemented atop RocksDB and evaluated on YCSB-like workloads with controllable temporal locality, AutoThermKV achieves up to 2.58 × throughput speedup, 2.60 × average-latency reduction, and tail-latency improvements of up to 20 × (p99) and 36 (p999) under modest memory budgets.
Modern serverless platforms create function instances based on user-specified memory limits, which often result in memory over-provisioning even when the average runtime footprint is small. This over-provisioning reduces warm container density under a fixed memory budget, leading to early pool saturation, frequent evictions, and increased cold-start latency. To address this fundamental limitation, we propose Ditto, a system that integrates online memory rightsizing with value-aware container management. Ditto dynamically adjusts container memory to match actual usage, reclaiming unused memory while safely avoiding out-of-memory (OOM) conditions. In addition, it prioritizes containers based on their benefit per unit memory using a score-based eviction and keep-alive policy. We implement Ditto in Apache OpenWhisk and evaluate it across diverse workloads. Ditto reduces cold-start rate by up to 25.7% and latency by up to 33.3% compared to vanilla OpenWhisk, and by up to 14.8% and 19.4%, respectively, compared to RainbowCake.
Dongjae Lee, Kyuli Park, Yeonwoo Jeong et al.· IEEE International Conferenc...· 0 citations
Results show that object-value signals rank what to retain, while persistent responsibility determines which group bears reclamation pressure, which shows that object-value signals rank what to retain, while persistent responsibility determines which group bears reclamation pressure.
Read latency is a critical bottleneck for NAND-based SSDs in AI-driven datacenter workloads, where model parameters and key-value data are frequently swapped between main memory and storage. Existing prefetching schemes operate on block-level address sequences that have been stripped of application context by the filesystem layer, limiting their prediction accuracy and coverage. We propose the BASIC Prefetcher, which recovers application-level I/O patterns by classifying requests into size-based bins and modeling temporal bin transitions with a lightweight Markov-chain framework. By confining address prediction to frequently accessed bins and their primary data regions, BASIC Prefetcher achieves up to 89% prediction accuracy on sequential workloads and 70% on highly random workloads, including AI large language model inference traces, without requiring pre-training or per-page state tracking. Across six diverse workloads, our approach improves SSD throughput by 1.41× to 2.39× over existing prefetching schemes.
Han Jang, Dongjun Lee, Youngbin Jin et al.· Proceedings of the 19th ACM...· 0 citations
Large language model serving faces a critical memory bottleneck: the KV cache grows with sequence length and batch size. PagedAttention uses fixed-size memory blocks to reduce allocator-level fragmentation, but recent KV eviction algorithms operate at a token granularity finer than block-level management. This mismatch causes intra-block fragmentation, leaving a large fraction of allocated KV memory unreclaimable. We present vToken, a lightweight token-level virtualization layer that decouples logical token liveness from physical block placement. vToken maintains a stable logical token view through token-table indirection and realizes physical reclamation by repacking live tokens asynchronously. The design preserves PagedAttention kernels and CUDA Graph compatibility. We implement vToken in vLLM and evaluate it with H2O, Random, and Scissorhands across models. Compared with a paired Naive-Evict baseline, vToken reduces retained KV blocks per request by 27.2\%--72.3\% and improves SLA-constrained throughput by up to 1.37$\times$. Under a constrained active-KV budget, it extends the maximum feasible concurrency by up to 2$\times$, while reducing the per-policy integration footprint from 500+ lines to under 50.
Yuanhang Gao, Xiangrui Yang, Yuanfeng Chen et al.· 0 citations
KV-cache offload is widely used to stretch GPU memory for LLM serving, but its storage behavior has not been characterized at the block-device level. In this paper, we study LMCache through realworld multi-session workloads that span same/different context $\times$ same/different prompt, using over 100 stateless requests per workload. Our comparison is observational rather than factorial: it contrasts two realizable deployment snapshots—LMCache 0.3.0 with buffered I/O and the kernel page cache, and LMCache 0.3.16 with prefix-aware deduplication and O_DIRECT. Key findings define the paper. First, the legacy stack appears almost read-free at the SSD layer in all four workloads, but a flush_ram experiment shows that this is conditional on page-cache warmth: once the cache is evicted, the same path issues about 1 GB of sequential reads in 683 ms. Second, deduplication delivers a dramatic write reduction only for the exact-repeat workload: one set collapses from 1.62 GB to 11.75 MB of writes, while the other three sets remain in the 2–5 GB range. Third, O_DIRECT converts a hidden and bimodal read cost into an explicit and stable one: on one set, warm TTFT rises from 27 ms to 93 ms because each of the 99 warm requests re-reads the same ~12 MB partial chunk from SSD. The main conclusion is therefore not “newer is better,” but a system trade-off: predictability versus average-case latency.
Ying He, Dingsen Shi, Yanbo Dai et al.· 2026 International Conferenc...· 0 citations
The key-value (KV) cache is a primary capacity and bandwidth bottleneck in long-context LLM serving. We present Minima-KV, a retention-preserving hierarchy for mixed-format paged attention. Recent and protected Anchor pages remain in FP8, while older non-anchor pages move to packed TQ3; every live-request page remains addressable. Format-specific kernels compute partial attention states and combine them through a globally normalized online-softmax merge, enabling direct heterogeneous decode without a cache-sized dense shadow. Across separate, configuration-bound Qwen3.6-27B profiles on a single 96-GB NVIDIA RTX PRO 6000 Blackwell GPU, deployment accounting reports 18.3 KiB of attention KV per live token, corresponding to 3.50x compression relative to BF16 and 1.75x relative to FP8. A materializing quality profile matches its dense control on 16K RULER needle-in-a-haystack tasks. On the same 503-question LongBench v2 set, measured deltas are -0.80, -0.60, and -0.40 percentage points at 16K, 32K, and 64K. A separate single-pair direct-decode canary with two 59,008-token requests measures 3.625x active-KV compression and 0.9821x throughput relative to its control, routes all 16 full-attention layers without fallback, and retains no dense shadow. These results establish a practical mixed-format path for compressing long-context state without evicting live-request KV pages.
S. Kozyrev, AI DavydMaiborodaMinima, Inc.· 0 citations
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