KVCache-Centric Scheduler Overview

• KVCache-centric scheduling is a cache and memory management approach that optimizes storage, access, eviction, and allocation for large-scale LLM inference.
• It employs predictive models and workload characterization to dynamically adjust cache retention, prefetching, and eviction policies based on reuse and access patterns.
• Empirical evaluations show that such strategies can boost cache hit ratios, reduce latency up to 10×, and increase throughput by over 75% in distributed systems.

A KVCache-centric scheduler is a cache and memory management system whose policies, decisions, and scheduling logic are explicitly designed around the efficient storage, access, eviction, and utilization of Key-Value Caches (KVCache) in large-scale parallel and distributed systems, most notably for LLM inference and related high-throughput environments. Fundamentally, such schedulers use predictive models, workload characterization, and resource-awareness to orchestrate cache retention, prefetching, eviction, and even compute resource allocation to maximize cache hit rates, minimize latency, and optimize hardware utilization under strict memory, bandwidth, and latency constraints.

1. Conceptual Foundations and Motivation

KVCache-centric scheduling is rooted in the recognition that key-value caches—such as the intermediate token representations in LLMs—constitute a principal computational and memory bottleneck in modern AI and cloud serving stacks. Traditional scheduling is often compute- or throughput-oriented, with cache management relegated to auxiliary heuristics such as LRU or uniform allocation. KVCache-centric approaches reorder this priority: cache management becomes a first-class scheduling constraint, and orchestration is driven by reuse likelihood, cache access patterns, and predictions about future query behavior. This design enables systems to dynamically control cache allocation, optimize reuse, and adapt to workload-specific memory pressure.

The motivation for such approaches is particularly acute for LLM inference, where KVCache memory often limits the number of concurrent requests, and cache miss penalties (computation, I/O, and recomputation) can dominate latency and energy costs. As LLM applications scale to multi-agent settings, @@@@1@@@@ (MoE) architectures, or long context windows, KVCache-centric scheduling becomes necessary to achieve practical throughput and service-level objectives (SLOs) (Qin et al., 24 Jun 2024, Liu et al., 2 Aug 2025).

2. Predictive Modeling and Workload Characterization

Accurate prediction of cache stress and workload behavior is foundational to KVCache-centric scheduling. Early work employed probabilistic cache stress characterization: by estimating the distribution of stack distances (distance between repeated accesses to a memory line), one can, in constant time, predict cache miss rates across different cache configurations. The core idea is to abstract observed memory tracks into a stack distance distribution, with the miss probability (for a cache of size $c_s$ and line size $l_s$) given by

$P(\text{cache miss}) = 1 - \text{cdf}(c_s / l_s)$

where cdf is the cumulative distribution function of observed stack distances. This enables near-instantaneous evaluation of cache stress, which is directly useful for on-the-fly task schedule assignment on heterogeneous clusters (0902.4822).

In production LLM and cloud settings, more nuanced characterizations are required. Real-world traces have shown that KVCache reuse patterns are highly skewed (hot/cold blocks), temporally and spatially non-uniform, and workload-specific. For instance, the probability of reuse for a given KV block often follows an exponential distribution per request category, and spatial locality dictates that "head" tokens are preferentially reused (Wang et al., 3 Jun 2025). These empirical insights drive the creation of scheduling heuristics and feature-based utility scoring in modern schedulers.

3. Scheduling Algorithms and Policies

3.1 Resource-Aware and Batching Schedulers

LLM inference systems face unique constraints due to the sequential dependency of token generation and the linear (or superlinear) growth of cache memory per request. Theoretically, one models the scheduler as selecting a set $S^{(t)}$ of in-progress requests and a set $R^{(t)}$ of waiting requests, under the constraint

$\sum_{i \in S^{(t)}} (s_i + o_i^{(t)}) \leq M$

where $s_i$ is the starting prompt length, $o_i^{(t)}$ is the number of generated tokens, and $M$ is the total GPU memory. The Memory Constrained Shortest First (MC-SF) algorithm packs requests by greedily sorting candidates by smallest output length, checking feasibility at every potential future completion epoch, and selecting maximal sets while never exceeding memory (Jaillet et al., 10 Feb 2025). Such online algorithms are proven to achieve competitive ratios close to hindsight-optimal integer programming benchmarks, offering both theoretical robustness and empirical efficiency.

3.2 Workload-Aware and Priority-Based Eviction

Rather than standard LRU or LFU eviction, advanced schedulers use workload-derived priority functions. The eviction priority for a cache block is

$$\textrm{Priority} = (\textrm{ReuseProb}_w(t, \textrm{life}), -\textrm{Offset})$$

where $\textrm{ReuseProb}_w(t, \textrm{life})$ is the predicted reuse probability for workload $w$, $t$ is time since last access, $\textrm{life}$ is the expected block lifespan, and Offset quantifies spatial locality within a request (Wang et al., 3 Jun 2025). This yields finer-grained and context-sensitive eviction behavior, permitting higher cache hit rates and lower queuing latency.

3.3 Layer- and Head-Aware Allocation

Schedulers such as BaKlaVa and CAKE further refine allocation by distributing cache budgets non-uniformly across attention heads or transformer layers based on their empirically determined contribution to output quality. BaKlaVa profiles attention heads using the cosine similarity between input and output vectors in self-attention (interpreted as importance) and dynamically reallocates memory budgets (Gulhan et al., 18 Feb 2025). CAKE frames cache allocation as a "cake-slicing problem," computing each layer's preference metric from spatial attention dispersion (entropy) and temporal attention shift (variance), and allocates the fraction

$$B_l = \frac{\mathcal{P}_l}{\sum_{k=0}^{L-1} \mathcal{P}_k}\, B_{\text{total}}$$

for each layer $l$ (Qin et al., 16 Mar 2025). This adaptive, global allocation ensures optimal use of constrained cache under highly non-uniform attention dynamics.

4. Advanced Mechanisms: Prefetching, Compression, and Semantic Cache Sharing

Modern KVCache-centric schedulers often integrate additional mechanisms to further enhance efficiency:

• Proactive Prefetching: Workflow-aware systems like KVFlow model multi-agent task graphs, attaching a "steps-to-execution" metric to each cache node. They proactively prefetch KV tensors for agents scheduled for near-future execution—using background threads—thus ensuring necessary cache is present in GPU at dispatch time (Pan et al., 10 Jul 2025).
• Compression and Selective Retrieval: PQCache applies product quantization to the token keys during prefilling, enabling approximate nearest neighbor search to retrieve only the most relevant KV entries during decoding. This approach both compresses the cache and transforms it into a queryable embedding database, allowing selective, MIPS-based cache population and retrieval (Zhang et al., 1 Jul 2024).
• Semantic Cache Sharing: SemShareKV goes beyond prefix or exact match reuse by matching tokens between prompts using locality-sensitive hashing (LSH) on RoPE-injected embeddings. Fuzzy alignment allows the scheduler to reuse and prioritize cache segments even when serving semantically similar but lexically divergent prompts, leading to step-function accelerations under certain workload regimes (Zhao et al., 29 Sep 2025).

5. System Architecture and Real-World Application

State-of-the-art serving platforms implement KVCache-centric scheduling within broader distributed and disaggregated designs:

• Disaggregated KVCache and Multi-Stage Scheduling: Mooncake separates prefill and decoding clusters, leveraging underutilized host CPU and DRAM for a distributed cache. Its conductor scheduler dynamically selects nodes based on KVCache reuse, timing predictions, and SLO adherence, employing early rejection for requests predicted to exceed supported latency (Qin et al., 24 Jun 2024).
• Expert-Sharded and Adaptive Retention (MoE): In MoE setups, PiKV partitions the KVCache across experts and nodes, and its scheduler adaptively scores token groups (pages) based on features such as attention intensity, recency, query-token similarity, and predicted reuse. Scheduling is thus aware of both the data distribution and the expert routing, aligning KV storage with query demand (Liu et al., 2 Aug 2025).
• SmartNIC-Centric Transfer Scheduling: FlexiNS addresses the network-level dimension, providing programmable offloading engines, header-only transfer, and DMA-optimized notification for high-throughput, KVCache-aware network scheduling—up to 1.3$\times$ improvement in transfer rates (Chen et al., 25 Apr 2025).
• Parameter-Centric Throttling: Although not KVCache-centric by design, KunServe demonstrates the interplay between cache and parameter management; by selectively dropping parameter replicas to instantly release GPU memory, the approach allows KVCache expansion during load spikes, achieving 27.3$\times$ reduction in tail latency compared to classical KVCache-centric schemes (Cheng et al., 24 Dec 2024).

6. Performance Metrics and Impact

KVCache-centric schedulers are typically evaluated along the following axes:

Metric	Relevance	Typical Gains
Cache Hit Ratio	Primary measure of cache efficiency	+8% to +24% via workload-aware (Wang et al., 3 Jun 2025)
Latency (TTFT, TBT)	Time-to-first-token and time-between-tokens	Up to 10$\times$ speedup (Qin et al., 16 Mar 2025)
Throughput	Requests served per second	75%+ more requests (Qin et al., 24 Jun 2024), 1.7$\times$ faster (Liu et al., 2 Aug 2025)
Memory Usage	Effective reduction in peak GPU or cluster memory	3.9$\times$ less (Liu et al., 2 Aug 2025), 42% lower (Zhao et al., 29 Sep 2025)
Computational Cost	CPU and GPU cycles saved or spent	Minimal overhead for advanced eviction strategies

Empirical evaluations consistently show that switching from uniform to adaptive, KVCache-centric scheduling frameworks leads to substantial improvements in efficiency and scalability, with minimal compromise in output quality—even at aggressive cache compression ratios (Gulhan et al., 18 Feb 2025, Zhang et al., 1 Jul 2024, Qin et al., 16 Mar 2025).

7. Implications, Extensions, and Limitations

KVCache-centric scheduling is a rapidly evolving paradigm with notable implications:

• Sustainability and Cost-Efficiency: By maximizing cache reusability and minimizing costly recomputation, such schedulers reduce energy, water, and operational expenses in large-scale deployments (Jaillet et al., 10 Feb 2025).
• Generalization and Adaptivity: The most effective systems integrate workload profiling, per-layer/head allocation, dynamic eviction policies, and semantic reuse, as seen in CAKE and SemShareKV (Qin et al., 16 Mar 2025, Zhao et al., 29 Sep 2025).
• Complexity and Overhead: Adaptive methods introduce computational overhead (entropy/variance computations for CAKE, clustering for PQCache, LSH for SemShareKV), but empirical evidence shows this is negligible relative to gains in efficiency.
• Limitations: Strategies often depend on workload predictability, accurate profiling, and may need tuning for heterogeneous, bursty, or previously unseen tasks. Some approaches are best suited to particular hardware (e.g., SmartNICs for FlexiNS), or may have limited portability.
• Integration with Other Resource Managers: The strongest performance arises when KVCache-centric schedulers are embedded into holistic orchestration frameworks that also consider compute, network, and storage resource constraints.

In summary, KVCache-centric scheduling harnesses cache-aware, adaptive, and predictive techniques to orchestrate resource usage in computationally demanding inference and data-processing systems. It offers demonstrable gains in throughput, latency, and memory efficiency, and forms the architectural backbone of state-of-the-art serving infrastructure for large-scale LLMs and related applications.