Optimized Batching Strategy

• Optimized batching strategy is a method for grouping jobs to improve resource utilization and reduce costs in complex systems.
• It dynamically adjusts batch sizes based on workload variability, latency requirements, and system constraints to balance efficiency with responsiveness.
• Applications span machine learning inference, distributed optimization, and logistics, where tailored batching policies yield significant throughput and energy improvements.

An optimized batching strategy refers to the design, analysis, and implementation of batching policies that maximize performance, efficiency, or other application-specific objectives in computational and operational systems—often under complex constraints such as latency, resource usage, variability in workload, or quality-of-service requirements. Across domains—from high-throughput inference in machine learning, distributed optimization, and online transaction processing, to warehousing and business process orchestration—batching strategies determine how individual jobs, samples, or operations are grouped and scheduled for joint execution, directly impacting resource utilization, energy consumption, delay, and cost.

1. Foundational Principles of Batching Optimization

Batching converts a stream or set of jobs into grouped execution units (batches) to exploit parallelism, amortize overheads, improve compute or energy efficiency, or reduce unit costs. The optimal batch size or policy typically results from intricate trade-offs:

• Efficiency vs. Responsiveness: Larger batch sizes often improve computation and energy efficiency (e.g., saturating GPU throughput or reducing per-request overhead), but induce longer waiting times or increased response latency for individual jobs (Xu et al., 4 Jan 2025).
• Stochasticity and System Constraints: Variability in request arrival rates, job characteristics, or hardware conditions demands dynamic or adaptive batching policies instead of static, one-size-fits-all hyperparameters (Pang et al., 7 Mar 2025, Choi et al., 2020, Mamageishvili et al., 2022).
• Resource and Cost Constraints: Practical deployments must respect hardware limits (e.g., memory, buffer size), operate under tight SLAs, or optimize cost-performance trade-offs (e.g., on serverless platforms or business process batching) (Chen et al., 2024, López-Pintado et al., 21 Jul 2025).
• Specialization to System Architecture: Batching strategies reflect the structure of the underlying computation (e.g., layer-level in DNNs, module-level in Mixture-of-Experts, prefix sharing in LLM inference) and may exploit model or workflow regularities (Xu et al., 12 Mar 2025, Zheng et al., 2024).

Optimized batching is fundamentally an instance of applied operations research or stochastic systems control, with objective function and constraint definitions customized to the domain.

2. Batching in Machine Learning Inference and Training

Dynamic Batching for Latency–Efficiency Trade-off

On servers with parallel compute (e.g., GPUs), batching increases computational and energy efficiency but can elevate response time. An SMDP-based framework models the inference queue as a batch service process with batch-size-dependent service time and explicitly incorporates power and latency into the objective. The batching policy is the solution to a continuous-time average-cost SMDP, typically solved after truncating the state space and aggregating "tail" states for computational tractability (Xu et al., 4 Jan 2025, Xu et al., 2023).

The main structure:

• State: Number of jobs in queue.
• Action: Select batch size (or wait if threshold criteria aren't met).
• Transition: Determined by arrivals (usually Poisson) and stochastic, batch-size-dependent service.
• Cost: Weighted sum of latency (holding cost per job) and power (per-batch energy).

The optimal batching strategy displays a control-limit structure: for each queue length, there is a batch size that minimizes weighted long-term cost. Introducing abstract costs for aggregated tail states yields substantial computational savings (up to 98% reduction in time complexity and 63.5% in space) while preserving performance (Xu et al., 4 Jan 2025).

Module-Based and Memory-Aware Strategies

Emergent architectures (e.g., MoE models or LLM serving on memory-constrained GPUs) benefit from module- or layer-specific batching:

• Module-Based Batching: Distinct batching policies for attention and expert modules, accumulating sufficient tokens per module (e.g., be≥2¹¹ for experts) to maximize GPU Flop utilization and hide PCIe transfer costs. An explicit memory and concurrency management system coordinates overlapping kernel launches and host/device buffers (Xu et al., 12 Mar 2025).
• Memory-Aware and SLA-Constrained Batching: Dynamic adaptation of batch size in real time, based on (i) live memory utilization forecasts and (ii) per-step decode latency relative to SLA, regulates throughput and capacity while bounding OOM and response violations (Pang et al., 7 Mar 2025).

Batch size is elevated to a runtime control variable instead of a static hyperparameter, and batch admission is regulated through explicit probabilistic (CLT-based) memory overflow control and binary search feedback for latency constraints.

SLA-Aware, Fine-Grained, and Adaptive Batching

For cloud ML inference, SLA violations are abated—and both throughput and latencies improved—by node-level, SLA-slack-aware batching. Here, a per-layer scheduler merges requests at DAG boundaries, preempts or merges sub-batches when latency slack (computed conservatively from per-layer latency lookups) allows, and adapts batch sizes at every layer (Choi et al., 2020). Key results include up to 15× lower response times, 1.5× throughput gains, and >5× reduction in SLA violations over coarse-grained graph batching.

In distributed training, adaptive batch size strategies—employing per-worker gradient variance tests—ensure that local batch sizes grow as stochastic gradient noise diminishes, attaining a trade-off between gradient variance, generalization, and communication overhead (Lau et al., 2024).

3. Application in Large-Scale and Specialized Computational Workloads

Large-Scale Batch Processing and Transactional Systems

In OLTP and large batch-processing systems, throughput is a function of batch size and a sub-additive speedup function reflecting diminishing returns (e.g., the service time per batch follows $T(k) = ak + b$). While exact Markovian models for throughput optimization are computationally infeasible for large n, mean-field approximation yields a closed-form throughput:

$$\Theta(k) = \min\{m k \mu(k), \frac{n \lambda \mu(k)}{\lambda + \mu(k)}\}$$

The optimal batch size is analytically derived (e.g., via solving a quadratic for linear speedup), and in real-world systems the mean-field prediction matches or is within 1–2 units of the true optimum for n≥50 (Kar et al., 2020).

GPU Kernel Launch Optimization via Batching

For fine-grained GPU workloads, the per-launch overhead of kernels can dominate at scale. Grouping iterative kernel launches into fixed-size batches, unrolling each batch into a CUDA Graph, and launching entire graphs instead of individual kernels yields substantial speedup. The optimal batch size is derived from the intersection of linear growth in graph-creation cost and amortized execution savings:

$n^* = \sqrt{a / k_c}$

where $k_c$ is the slope of graph-creation cost and $a$ parameterizes execution amortization (Ekelund et al., 16 Jan 2025).

Combining Hierarchical/Structural Batching with Memory Planning

For dynamic DNNs and irregular structures (e.g. trees, lattices), the batching frontier varies dynamically per instance. Strategies such as FSM-learned batching policies (with reinforcement learning to select "frontier" batch types), combined with PQ-tree-based memory planning for coalesced data movement, can yield 1.15–2.45× inference speedup on CPUs and GPUs (Chen et al., 2023).

Similarly, compile-time hybrid static+dynamic auto-batching (e.g., ACRoBat) fuses static analysis and runtime scheduling to maximize horizontal fusion, minimize kernel launches, and amortize per-batch costs—producing up to 8.5× acceleration versus dynamic-only systems (Fegade et al., 2023, Neubig et al., 2017).

4. Optimized Batching in Operations, Logistics, and Business Processes

In warehousing and order fulfillment, batching strategies affect total picker travel through assignment of orders to trolleys or batches. Optimized batching must consider:

• Capacity and Routing Constraints: Each order assigned exactly once, trolleys have basket capacities, and batches should minimize approximated walking distance (with distance-approximation MIP models) (Valle et al., 2018, Abelli et al., 2024).
• Matheuristics and Scalability: Partial-integer optimization matheuristics provide nearly optimal batching with drastically reduced computation time (solving instances up to 75 orders in minutes with sub-2% deviation from joint batch-routing MIP), outperforming time-savings heuristics (Valle et al., 2018).
• Order Selection for Cost Efficiency: Including the order selection decision (not batching all available orders) can yield 40% reduction in per-item travel cost at scale (Abelli et al., 2024).

Business processes require multi-objective optimal batching policies to balance cycle time (waiting) and cost:

• Meta-Heuristic Optimization: Guided hill-climbing, simulated annealing, and reinforcement learning approaches, with 19 domain-specific heuristics, are applied to iteratively adapt batch size thresholds and activation rules. RL-guided policies show best performance on diverse benchmark logs (López-Pintado et al., 21 Jul 2025).
• Composite Batch Activation Rules: Policies combine size, timeout, inactivity, and timed triggers (e.g., “batch after θ_v jobs, θ_f time passed, or at scheduled hour”) for Pareto-efficient trade-offs.
• Empirical Evaluation: Multi-objective heuristics reduce average case cycle times and achieve better Pareto coverage compared to baselines.

5. Domain-Specific and Context-Aware Batching Strategies

Cloud and Serverless Inference

In serverless DNN inference for heterogeneous workloads and multi-SLO applications, batching must analytically unify CPU and GPU platform-specific constraints (e.g., time-sliced GPU mechanisms), Poissonian arrivals, and diverse SLOs. HarmonyBatch's two-stage merging and heuristically computed batch groupings minimize cost subject to latency bounds, outperforming previous per-application or single-platform strategies by 30%–80% (Chen et al., 2024).

Real-Time Clinical and Urgent Operations

In clinical laboratory sample processing, especially for high-priority (vital) samples with stochastic arrival and transport times, batching policies must account for both presently available and in-transit samples:

• Stochastic MIQP Formulation: At every decision epoch, a mixed-integer quadratic program determines the split between immediate and deferred batch assignments, explicitly minimizing expected patient turnaround time, using CDFs over anticipated arrival times (Novak et al., 7 Dec 2025).
• Discrete-Event Simulation Embedding: Online policies (event-driven solver) nearly match the offline (oracle) upper bound, achieving significant reductions in 0.95 quantile and median patient turnaround without harming low-priority traffic.

Throughput and Posting Cost Control in Blockchain and Rollups

In batch posting for rollup chains, cost-minimizing strategies use queue- (delay-) and price-based thresholds, set according to the observed fee distribution. Practically all cost benefits of full dynamic programming are captured by two-parameter threshold rules, yielding up to 8%–29% savings versus naive always-post, with tunable queue and delay bounds (Mamageishvili et al., 2022).

6. Implementation Guidelines, Trade-offs, and Limitations

• Profiling and Parameter Selection: Always empirically profile core timing, memory, or latency as a function of batch size (and, if applicable, module or layer), extracting fitted model parameters to drive analytical or search-based optimization (Xu et al., 12 Mar 2025, Ekelund et al., 16 Jan 2025).
• Adaptivity and Runtime Control: Where latency or stochasticity precludes a static policy, deploy runtime or event-driven adaptivity (SLA-aware latency feedback, per-worker variance adaptation, or event-driven MIQP decision-making).
• Scalability and Complexity: For high-dimensional or combinatorial cases, finite truncation, abstract tail-state aggregation, and matheuristics offer scalable near-optimality (e.g., SMDP tail-state aggregation reduces MDP size by 63.5% and time complexity by 98% (Xu et al., 4 Jan 2025)).
• Generalizability and Portability: Underlying principles (trade-offs between efficiency and responsiveness, memory- and arrival-rate-aware batching) apply across domains, though technical realization depends on system constraints and workload regularity.
• Limitations: Strategies premised on distributional assumptions (e.g., CLT-based batch size bounds (Pang et al., 7 Mar 2025)) may require conservative tuning under heavy-tailed workloads. Overfitting of learned scheduling policies to static topologies mandates retraining under substantial model changes (Chen et al., 2023).

7. Comparative Summary Table

Domain / Application	Key Method	Objective / Constraint	Reported Improvement	Reference
ML inference (GPU)	SMDP dynamic batching	Latency, energy, queue size	Pareto-dominates static, ≥63% faster	(Xu et al., 4 Jan 2025)
LLM serving (GPU)	Mem+SLA-aware adaptation	OOM, latency, compatibility	+8–28% throughput, +22% cap.	(Pang et al., 7 Mar 2025)
MoE inference (GPU)	Module-based batching	Module RAM/compute profiles	8–31× throughput, 15× E2E faster	(Xu et al., 12 Mar 2025)
Cloud inference (SLA)	Node-level, slack batching	Throughput, SLA violation	1.1–1.5× throughput, 5.5× fewer SLA	(Choi et al., 2020)
Distributed training	Adaptive batch size	Comm–var. tradeoff	Matches/exceeds large-batch optima	(Lau et al., 2024)
OLTP, batch-processing	Mean-field batch opt.	Throughput, server count	Closed form optimum, <1% error	(Kar et al., 2020)
GPU kernel batching	CUDA-Graph batching	Build + launch overhead	1.2–1.5× speedup, analytic n*	(Ekelund et al., 16 Jan 2025)
Warehousing	Matheuristic/MIP	Picker routing, cap.	2–40% cost reduction, fast	(Valle et al., 2018)
Business process orchestration	RL/meta-heuristics	Cycle time, cost, utilization	55–65% of cases, better Pareto front	(López-Pintado et al., 21 Jul 2025)
Clinical lab batching	Stoch. MIQP; DES	Turnaround, uncertain arr.	−4.9 min median, −9.7 min 0.95q TAT	(Novak et al., 7 Dec 2025)
Rollup posting (blockchain)	Queue/price-threshold	Fees, delay	8–29% cost savings	(Mamageishvili et al., 2022)

• SMDP-Based Dynamic Batching for Improving Responsiveness and Energy Efficiency of Batch Services (Xu et al., 4 Jan 2025)
• MoE-Gen: High-Throughput MoE Inference on a Single GPU with Module-Based Batching (Xu et al., 12 Mar 2025)
• Optimizing LLM Inference Throughput via Memory-aware and SLA-constrained Dynamic Batching (Pang et al., 7 Mar 2025)
• LazyBatching: An SLA-aware Batching System for Cloud Machine Learning Inference (Choi et al., 2020)
• Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods (Lau et al., 2024)
• On the Throughput Optimization in Large-Scale Batch-Processing Systems (Kar et al., 2020)
• Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs (Ekelund et al., 16 Jan 2025)
• Order batching using an approximation for the distance travelled by pickers (Valle et al., 2018)
• Joint Order Selection, Allocation, Batching and Picking for Large Scale Warehouses (Abelli et al., 2024)
• Optimization of Activity Batching Policies in Business Processes (López-Pintado et al., 21 Jul 2025)
• Urgent Samples in Clinical Laboratories: Stochastic Batching to Minimize Patient Turnaround Time (Novak et al., 7 Dec 2025)
• Efficient Rollup Batch Posting Strategy on Base Layer (Mamageishvili et al., 2022)
• BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batching (Zheng et al., 2024)
• ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines (Chen et al., 2023)
• ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time (Fegade et al., 2023)
• On-the-fly Operation Batching in Dynamic Computation Graphs (Neubig et al., 2017)
• HarmonyBatch: Batching multi-SLO DNN Inference with Heterogeneous Serverless Functions (Chen et al., 2024)
• Improved Batching Strategy For Irregular Time-Series ODE (Lam et al., 2022)