High-Level Scheduling Module

• High-level scheduling modules are components that translate abstract task graphs into resource-aware execution plans using diverse algorithmic techniques.
• They integrate profiling, prediction, and optimization to map tasks to heterogeneous resources, ensuring efficient performance in dynamic environments.
• They employ methods ranging from heuristic scheduling to hierarchical reinforcement learning to balance multi-objective criteria like efficiency, latency, and energy use.

A high-level scheduling module is a system component responsible for translating user-, application-, or architect-level workloads and abstract task graphs into resource-aware execution plans, mappings, and dispatch orders. These modules orchestrate resource allocation, parallelism, dependency management, and adaptability in highly complex, heterogeneous, or dynamic computing environments. They employ diverse algorithmic and formal methods adapted to their domain: e.g., list or heuristic schedulers for SoCs, entropy-aware selection for compound LLM pipelines, constrained optimization for accelerator-level parallelism, staged RL hierarchies for human-in-the-loop automation, or integer-programming with SMT for mobile robot fleets. Their design is central to the efficient, robust, and scalable operation of modern parallel, distributed, and AI-driven systems.

1. Conceptual Foundations and System Role

High-level scheduling modules operate above resource-execution backends, providing a bridge from user/application intent (DAGs, multi-model graphs, job sets, task sequences) to platform-specific dispatch actions (kernel launches, device allocations, roadmaps for mobile robots, pipeline decompositions for compound models). These modules abstract or encapsulate:

• Workload structure: Application DAGs, multi-staged jobs, or compound modular graphs;
• Resource model: Accelerators, compute nodes, chiplet topologies, communication fabrics, robot fleets, or inference engine workers;
• Policy/Objective: Makespan, latency, energy-delay product (EDP), average JCT (job completion time), throughput, fairness, sequential dependency, or specialized utility metrics.

The scheduling module thus serves as the control plane for resource utilization and parallelism, integrating profiling, prediction, optimization, and adaptation in heterogeneous, data-, and event-driven settings. Its interface is characterized by taking declarative workload specifications and producing concrete binding plans, mappings, or executable schedules.

2. Algorithmic Architectures and Formalisms

A wide spectrum of formalizations governs high-level scheduling modules:

• List Scheduling/Heuristic DAG Scheduling: Algorithms like HEFT, PEFT, and their dynamic variants, which process (possibly merged) outstanding DAGs, compute upward ranks, and greedily assign ready tasks to processors or PEs based on earliest finish or energy×delay objectives (x_{v,p}), as in (Mack et al., 2021).
• Constrained Optimization / MILP/SMT Hybridization: Modules such as ComSat for robot fleets decompose the assignment and timing into subproblems: E-routing (MILP for task assignment under time windows and battery), capacity verification (SMT to enforce mutual exclusion over shared zones or paths), and path-changing (MILP detours) (Roselli et al., 27 Oct 2025). Variables x_{R,n}, x_{R,e} model time-parameterized arrivals and edge usages.
• Hierarchical/Feudal RL for Multi-Agent Systems: High-level PPO or other policy-gradient agents select macro-schedules (task buffers for workers), while decentralized agent policies execute on local state (Carvalho et al., 2022). State is structured by occupancy and task buffers; reward penalizes (average) job slowdown.
• Probabilistic and Information-Theoretic Models: Entropy-based or Bayesian scheduling, such as LLMSched’s BN-based profiler and uncertainty-quantifier, chooses execution order in compound LLM apps maximizing uncertainty reduction and JCT minimization (Zhu et al., 4 Apr 2025).
• Resource-Constrained DP and Offloading: Slice-Level Scheduling for LLMs partitions requests into bounded-time/memory slices, performing DP-based adaptive batch construction and max–min offloading to GPUs to both guarantee OOM-safety and balance load (Cheng et al., 19 Jun 2024).
• Multi-Level, Multi-Stage Partitioning and Pipelined Mapping: SCAR’s module for multi-model, multi-chiplet workloads partitions workloads in time (windows), segments by model, and packs segments to heterogeneous chiplets via a combination of greedy layer-packing, segmentation heuristics, and inter-chiplet schedule tree search, targeting optimal EDP under strict hardware constraints (Odema et al., 1 May 2024).

3. Scheduling Algorithms, Inputs, and Outputs

The high-level scheduling process generally entails:

1. Input Acquisition: Ingest application DAGs, model graphs, resource profiles (performance, constraints), real-time metrics, and (optionally) job arrival rates or device-specific limits. For instance, POAS requires FLOPs/s, memory bandwidth, tile size constraints per accelerator (Martínez et al., 2022).
2. Prediction/Profiling: Use empirical models, regressions, or Bayesian estimates to predict per-task, per-device cost (latency, throughput, memory).
3. Core Scheduling Loop:
   • Formulate constraints: Dependencies (DAG edges), resource capacities, time windows, allocation bounds, or fairness indices.
   • Decision logic: Static (compute once) vs dynamic (periodic reoptimization, adaptation triggers); exploit exploration–exploitation or load-balancing.
   • Mapping/dispatch: Assign sub-tasks to resources (static schedule, real-time rebinding).
   • Algorithmic pattern: Greedy (list-based) policies, DP for batch formation, RL/planning for compositional tasks, hybrid MILP/SMT for constraint-rich or geometric domains.
4. Output Artifacts: Mappings of tasks to devices, workers, nodes, routes, parameterized time windows, or segmentation, together with execution orders, prefetches, adaptation triggers, and dispatch plans.
5. Integration/Monitoring: API layers for connecting to lower-level runtimes (CUDA/OpenCL/oneAPI for compute, ROS/MQTT for robots, inference APIs for LLMs), progress feedback loops, instrumentation for performance counters, and trigger policies.

4. Multi-Objective Optimization and Adaptation

High-level scheduling modules are frequently tasked with balancing, trading off, or satisfying multiple conflicting objectives, often in the presence of unpredictable workloads and resource dynamics:

• Resource Efficiency: Maximize utilization (active_compute_time/total_time), minimize idle or wasted compute (padding, buffer over-allocation), or optimize cost/throughput (Cheng et al., 19 Jun 2024, Martínez et al., 2022).
• Latency and JCT Minimization: Minimize average/maximum JCT, response time, or makespan across jobs or tasks (Zhu et al., 4 Apr 2025, Jadhav et al., 29 May 2025, Mack et al., 2021).
• Energy and EDP Optimization: Explicit EDP models (E·Lat) under hardware and pipeline-level constraints, supporting selection between high-throughput or low-energy modes (Odema et al., 1 May 2024).
• Fairness and Load Balancing: Achieve uniform tail latencies, load standard deviation minimization, or Jain's index maximization, as observed in both LLM scheduling and dynamic SoC workflows (Cheng et al., 19 Jun 2024, Jadhav et al., 29 May 2025).
• Information Gain or Uncertainty Reduction: In compound AI applications with stochastic or branching graphs, scheduling stages achieving maximal mutual information or entropy reduction drives better JCT and stability (Zhu et al., 4 Apr 2025).
• Adaptation and Dynamic Triggers: Repartition, reoptimize, or reload mappings/statistics on significant prediction deviation, resource state change, or performance spike, using thresholds or statistical triggers (Martínez et al., 2022).

5. Architectural Variants and Integration Patterns

Diversity in architecture reflects the application and resource domain:

• Centralized/Decoupled Designs: Many modules (e.g., Celerity's IDAG scheduler (Knorr et al., 13 Mar 2025), POAS (Martínez et al., 2022), task-graph orchestrators (Mack et al., 2021)) use central decision points decoupled from execution, overlapping analysis with execution and keeping orchestrator off the critical path.
• Hybrid Hierarchies: RL and feudal scheduling architectures layer strategic (high-level) and local (low-level) agents for complex, partially observable domains (Carvalho et al., 2022, Deng et al., 27 Nov 2025).
• Plug-and-Play Modular Integration: Schedulers that accept pluggable executors or agents enable reuse across low-level backends (e.g., the CES framework, which slots in any Executor for GUI automation (Deng et al., 27 Nov 2025)).
• APIs and Service Interfaces: Scheduler modules are commonly exposed as API endpoints or microservices, facilitating their reuse and orchestration with existing batch systems, resource managers (Slurm, PBS), or distributed runtimes (Jadhav et al., 29 May 2025, Martínez et al., 2022).
• Online/Batch Mode: Scheduling may be designed for offline batch preplanning (large HPC runs), online incremental dispatch (LLM serving, mobile robots), or periodic hybrid rescheduling (dynamic resource pool scenarios).

6. Performance Characterization and Empirical Outcomes

Published systems consistently quantify the gains and trade-offs from high-level schedulers:

• Speedup: POAS achieves up to 45% speedup with concurrent accelerator execution vs. best single-device, using task partitioning derived from profiling and cost modeling (Martínez et al., 2022).
• Throughput: Slice-level scheduling yields up to 315.8% throughput improvement versus naive or iteration-level methods in LLM serving, and >90% reduction in compute waste from padding (Cheng et al., 19 Jun 2024).
• Energy and EDP: SCAR's co-scheduling in multi-chiplet systems achieves 27.6–29.6% EDP improvement over homogeneous baselines (Odema et al., 1 May 2024); HEFT_EDP variants yield 30% energy savings over makespan-first heuristics (Mack et al., 2021).
• Schedule Overhead: LLMSched reduces scheduler invocation overhead to 0.16–2.32 ms (vs. prior up to 28 ms) while delivering 14–79% JCT reduction (Zhu et al., 4 Apr 2025).
• Robustness and Adaptivity: MUSS-TI reduces shuttle operations in quantum modules by up to 73.4%, improving throughput and fidelity under modular scaling (Wu et al., 30 Sep 2025). Hierarchical RL schedulers outperform random baselines and naive dispatch for dynamic arrivals and buffer scaling (Carvalho et al., 2022).
• System Resilience: Integrated rescheduling and time-parameterized route updates in robot fleets maintain full task completion with rapid re-planning (<30 s for large instances), even under environment and agent failures (Roselli et al., 27 Oct 2025).

7. Practical Guidelines and Lessons

Empirically validated practices for high-level scheduling include:

• Profiling and Modeling: Initial and ongoing profiling of device performance, memory, and transfer characteristics is foundational to effective partitioning and mapping (Martínez et al., 2022, Odema et al., 1 May 2024).
• Explicit Constraint Enforcement: Tight integration of constraint checking (resource, time, precedence, application logic) within or immediately following scheduling decisions ensures correctness and allows zero-shot adaptivity even with non-domain-specific schedulers (e.g., LLM-based (Jadhav et al., 29 May 2025)).
• Low-Overhead, Modular Design: Optimally, the scheduler imposes negligible runtime cost; best-in-class designs pipeline scheduling logic apart from execution (instruction-graph or task-graph separation, microservice APIs) (Knorr et al., 13 Mar 2025, Mack et al., 2021).
• Variant Switching: Exposing different scheduling modes (energy-first, makespan-first, load-balanced, exploration–exploitation) allows tuning and runtime switching depending on operational regime—burst, adversarial, or steady-state (Mack et al., 2021, Jadhav et al., 29 May 2025).
• End-to-End Integration: High-level modules should accept declarative inputs, produce explicit mappings, and expose event or logging hooks for integration with continuous monitoring and adaptive feedback (Martínez et al., 2022, Cheng et al., 19 Jun 2024).

These patterns collectively enable high-level scheduling modules to scale, adapt, and generalize across a diversity of architectures, workloads, and performance objectives, underpinning the performance and flexibility of modern distributed, accelerator-rich, and multi-agent computational systems.