KVCache-centric Scheduler

• KVCache-centric scheduling is a mechanism for managing key-value caches generated during LLM inference, reducing redundant computations and latency.
• It employs workload-aware eviction, predictive prefetching, and dynamic resource allocation to optimize cache retention and streamline distributed processing.
• Its design enhances throughput and memory efficiency by aligning cache management with service-level demands and real-time access patterns.

A KVCache-centric scheduler is an advanced cache-aware scheduling mechanism that optimizes the allocation, retention, and access of Key-Value (KV) caches—intermediate representations crucial for reducing computation in LLM inference and related workloads. Such schedulers have become pivotal in the design of high-throughput, memory-efficient AI serving systems and general distributed computation frameworks as the size and heterogeneity of serving infrastructures and model contexts have grown. The following sections review the fundamental concepts, methodologies, design patterns, performance considerations, and recent innovations central to KVCache-centric scheduling, as established in the peer-reviewed and preprint literature.

1. Conceptual Foundations and Motivating Principles

At its core, a KVCache-centric scheduler is defined by its explicit focus on managing the lifecycle and locality of key-value pairs computed during model inference tasks—especially those involving multi-turn or long-context processing. These KV caches, typically storing output from self-attention operations at each transformer layer and for every token, enable LLMs and other workloads to generate output with linear rather than quadratic time complexity with respect to sequence length.

The scheduler’s objective is twofold:

• Optimize cache retention: Decide which KV cache entries should be kept in fast-access memory (often limited GPU VRAM or CPU DRAM), minimizing redundant computations and data transfers.
• Minimize latency and maximize throughput: Support task batching and dynamic resource allocation in a manner that respects cache constraints while maintaining quality-of-service requirements such as Time-To-First-Token (TTFT) and time-between-tokens (TBT).

A unifying feature of KVCache-centric scheduling is that it elevates cache management from a reactive or local policy into a system-level, prediction-driven scheduling decision that is often aware of workload patterns, workflow semantics, and hardware characteristics.

2. Workload Characterization and Access Patterns

Workload analysis is foundational in motivating specialized caching and eviction strategies for KVCache-centric scheduling. Empirical characterization of real-world serving traces, such as those conducted on large cloud provider workloads and in LLM agentic workflow systems, consistently reveals:

• Skewed reuse distributions: A small proportion of KV blocks account for the majority of reuse both in single-turn (API-driven) and multi-turn (chat) workloads (Wang et al., 3 Jun 2025).
• Temporal and spatial locality: Prefix tokens (the "head") and recently generated tokens exhibit high probabilities of immediate reuse.
• Predictability by request type: Stratifying by request category (single-turn vs. multi-turn) and conversation turn number generates reuse patterns that fit predictable (often exponential) distributions (Wang et al., 3 Jun 2025).
• Prefix prefill and range access: In scenarios such as retrieval-augmented generation (RAG) and multi-agent workflows, shared prefixes lead to repeated sequential accesses, enabling bulk prefetching and reducing redundant computation (Zhu et al., 28 May 2025, Pan et al., 10 Jul 2025).

These insights motivate cache partitioning, prioritization, and scheduling decisions that are sensitive to type-specific locality, cache lifespan (life expectancy of a block), and expected access sequences—forming the empirical basis for workload-aware and future-predictive scheduling strategies.

3. Scheduling and Eviction Methodologies

KVCache-centric schedulers integrate advanced policy modules for cache allocation, eviction, and prefetching by leveraging both general and workflow-specific predictions of cache utility.

3.1 Workload-Aware and Predictive Eviction

Rather than applying uniform policies, modern schedulers utilize:

• Workload-aware eviction: Each KV block’s retention priority is determined by an ordered tuple comprising the predicted probability of reuse (often fitted as an exponential decay based on recent trace statistics) and its spatial offset within the token sequence (favoring prefix tokens) (Wang et al., 3 Jun 2025). The eviction decision is made lexicographically.

$$\text{Priority} = \big(\text{ReuseProb}_w(t, \mathrm{life}),\ -\text{Offset}\big)$$

• Agent step-graph-driven policies: In multi-agent workflows, the scheduler computes a "steps-to-execution" value for each agent or workflow node based on the future execution plan (Agent Step Graph). This value is propagated through a prefix-tree cache structure, directly guiding fine-grained node-level eviction (Pan et al., 10 Jul 2025).
• Cascading and adaptive allocation: Cache budgets are allocated proportionally across model layers according to computed "preference scores" from attention dynamics, with eviction indicators combining both sustained and dynamic importance metrics (Qin et al., 16 Mar 2025).

3.2 Prefetching and Latency Hiding

Advanced scheduling mechanisms use predictive prefetching to overlap expensive KVCache transfers or loading operations with ongoing computation:

• Fully overlapped prefetching: The scheduler proactively loads required KV tensors from slower (CPU or remote) memory into the GPU ahead of predicted usage, monitored and orchestrated by status-aware background threads. This mitigates cache miss stalls during LLM generation (Pan et al., 10 Jul 2025).
• Chunked and pipeline prefill: For very long-context scenarios, input tokens are processed in chunks, with KVCache storage and transfer overlapping computation to avoid holding large, contiguous caches in GPU memory (Qin et al., 24 Jun 2024).
• Workload-aware range queries: Recognizing high fractions of sequential block accesses, the scheduler groups contiguous requests for bulk (range) retrieval, while maintaining fast point lookups for random accesses (Zhu et al., 28 May 2025).

3.3 Dynamic and Heterogeneous Resource Allocation

Schedulers allocate memory budgets non-uniformly:

• Importance-based allocation: Allocation of cache resources is guided by profiling or real-time heuristics that quantify the "importance" or representational change induced by each attention head or layer, enabling differentiated compression and retention (Gulhan et al., 18 Feb 2025, He et al., 25 Jan 2025, Jha et al., 24 Feb 2025).
• Semantic and task-aware partitioning: Cache budgets are dynamically distributed based on a layer or attention head’s expected semantic relevance to the downstream task, as revealed by semantic vector deviations or observed attention patterns (He et al., 25 Jan 2025).

4. System Architectures and Integration

KVCache-centric schedulers are now integral in several advanced serving architectures, each embedding their scheduling and cache management logic into the broader system:

• Disaggregated architectures: Platforms like Mooncake separate prefill and decoding clusters, leveraging underutilized CPU, DRAM, and SSD resources in the GPU cluster to maintain a disaggregated cache pool. The scheduler (e.g., "Conductor") orchestrates KVCache transfers, block reuse, and resource pairing, balancing throughput with latency constraints and enabling early rejection under overload (Qin et al., 24 Jun 2024).
• Hybrid storage and transfer engines: KVCache is partitioned and moved between storage tiers (GPU, CPU, SSD), and transfer engines using SmartNICs and optimized RDMA-based stacks (e.g., FlexiNS) achieve near line-rate KVCache movement via header-only offloads, in-cache RX processing, and DMA-only notification channels, minimizing host CPU overhead and providing RDMA verbs compatibility (Chen et al., 25 Apr 2025).
• Multi-agent and tree-structured cache organizers: For agentic or workflow-driven scenarios, the cache is managed as a tree with nodes corresponding to shared or unique agent step prompts, and scheduling evicts or preloads prefixes according to the schedule graph (Pan et al., 10 Jul 2025).

5. Performance Metrics and Empirical Results

Performance evaluation of KVCache-centric schedulers typically considers:

• Throughput improvements: Mooncake achieves up to 525% higher throughput in long-context simulations and 75% more requests handled in real-world workloads compared to LRU-based and baseline systems (Qin et al., 24 Jun 2024). KVFlow achieves up to 2.2× speedup in multi-agent workflows against reactive LRU-based caching (Pan et al., 10 Jul 2025).
• Latency and SLO adherence: Schedulers explicitly report metrics such as P90 TTFT and TBT, with near-total SLO compliance due to early rejection and dynamic load prediction (Qin et al., 24 Jun 2024). Queue times to first token (QTTFT) reductions of 28–41% are reported when adopting workload-aware cache management policies (Wang et al., 3 Jun 2025).
• Cache hit rates: Adaptive eviction and allocation policies yield hit rate improvements of 1.5–23.9% over classical LRU/LFU baselines (Wang et al., 3 Jun 2025).
• Compression and memory reduction: Differentiated cache allocation and dynamic partitioning consistently reduce memory requirements. CAKE maintains model performance with only 3.2% of the full KV cache (Qin et al., 16 Mar 2025); WindowKV achieves comparable performance with 12% of baseline cache use (Zuo et al., 23 Mar 2025).
• Inference accuracy and computational overhead: KVCrush and PQCache enable up to 4×–70% KV cache reduction at less than 1% model accuracy drop with sub-1% additional inference latency (Jha et al., 24 Feb 2025, Zhang et al., 1 Jul 2024, Gulhan et al., 18 Feb 2025).

6. Limitations, Challenges, and Future Directions

Emerging challenges and research opportunities in KVCache-centric scheduling include:

• Prediction robustness: Accurate estimation of reuse probabilities, scheduling future agent execution, and semantic importance under heterogeneous and time-varying workloads require continually improving predictive models, fast profiling, and lightweight monitoring.
• Integration with system heterogeneity: Scheduling decisions must adapt to evolving hardware architectures, such as SmartNIC offloads, hybrid storage tiers, and the presence of parameter-centric memory management (e.g., KunServe) (Cheng et al., 24 Dec 2024).
• Scalability in concurrent, multi-tenant environments: Systems must efficiently partition and coordinate cache usage among competing tasks and tenants, especially as agent-based workflows and fine-grained caching (e.g., at the sub-agent or block level) become central (Pan et al., 10 Jul 2025).
• Balancing complexity and overhead: Fine-grained policies (e.g., steps-to-execution computation, semantic head profiling) introduce nontrivial overhead. Schedulers need to ensure these costs do not outweigh the throughput and latency benefits, particularly at high concurrency or scale.

7. Comparative Perspective and Synthesis

KVCache-centric schedulers represent an intersection of memory management, task scheduling, and application semantics, distinguishing themselves from prior cache-oblivious or uniformly-managed systems by:

• Making resource allocation and eviction decisions informed by application- and workload-level predictions rather than reactive recency or frequency statistics.
• Integrating hierarchical and layered cache allocation, semantic profiling, and future-aware batch and prefetch decisions.
• Achieving substantial gains in memory compression, throughput, latency, and efficiency on real-world LLM serving and agentic workflow tasks.

The field has seen rapid development, with production systems and peer-reviewed research increasingly converging on the need for tailored, predictive, and semantically-informed cache scheduling. These advances provide a robust foundation for continued innovation at the intersection of AI systems, distributed cache management, and high-performance inference serving.