Intent-Driven Scheduling Paradigm

• Intent-driven scheduling is defined by converting high-level user and application intents into explicit scheduling constraints and goals.
• It employs formal models, machine learning extraction, and control-theoretic optimization to adapt scheduling decisions to dynamic system states.
• Empirical results show improvements such as up to 167× speedup and enhanced resource utilization, demonstrating its efficiency across domains.

Intent-driven scheduling is a paradigm that formalizes, interprets, and enforces application- or user-level high-level goals—termed “intents”—within the resource allocation and scheduling process. Unlike classical scheduling, which optimizes generic metrics like throughput, fairness, or worst-case completion times, intent-driven scheduling explicitly models stakeholder requirements, semantic priorities, and interdependencies, and translates them into actionable system behavior across domains such as operating systems, cloud/edge management, RAN, industrial IoT, UAM, and programmable networks. This approach employs formal models of intent, interpretable logic or ML-driven intent extraction, and optimization or control-theoretic techniques to dynamically adapt scheduling decisions based on both current system state and evolving high-level requirements.

1. Core Principles and Conceptual Framework

Intent-driven scheduling contrasts with traditional approaches by elevating the semantics of applications, user requests, or operational requirements (collectively, “intents”) to first-class scheduling drivers. Key features are:

• Explicit representation of intents as structured objects: tuples or vectors encapsulating metrics, goals, constraints, time horizons, and weights (Narendra et al., 2024, Elkael et al., 23 May 2025).
• Decoupling scheduling objectives from system-level surrogates (e.g., CPU share) toward minimization of explicitly quantified “unhappiness,” intent violation, or delay debts (Nikseresht et al., 2010, Akbari et al., 6 Apr 2025).
• Dynamic adaptation to changing system demands, heterogeneous task types, and ambiguous or multi-granular user/operator requirements (Kim et al., 17 Dec 2025, Dutta et al., 2024).
• Injection of explanations, interpretability, and explainable decision-making cycles in the face of vague or underspecified inputs through human-in-the-loop reasoning or symbolic dialogue (Kim et al., 17 Dec 2025).

The principle is to systematically map high-level requirements or semantic signals into operational constraints, weights, and optimization targets for a scheduler, integrating any relevant dependencies, priorities, and the wider context of resource contention and application heterogeneity.

2. Formal Models of Intent and Scheduling Objectives

Models of intent typically treat intents as formal specifications involving metrics (K), goals (G), constraints (C), and time validity (T), e.g.,

$\mathcal{I} = (K, G, C, T)$

where $K$ is the set of KPIs (latency, reliability, throughput), $G$ the desired goals (quantified targets), $C$ resource constraints, and $T$ the time window of applicability (Narendra et al., 2024). Scheduling is formulated as one or more constrained optimization problems, examples include:

• Minimizing total system “unhappiness,” a scalar that accumulates as wall-clock delay or as delay debt for each externally requested service:

$$U = \sum_{i} W(C_i) \sum_j \sum_k w^k_i \cdot u^k_{i \to j}$$

with “unhappiness” $u^k_{i \to j}(t)$ representing the outstanding delay for each request (Nikseresht et al., 2010).

• Enforcing explicit user-level SLOs as hard or soft constraints, e.g., “keep $85\%$ of handovers within 50 ms over a 30 min window,” or “maintain EMA(response time) ∈ [L, U]$$” (<a href="/papers/2412.04232" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Narendra et al., 2024</a>, <a href="/papers/2504.04429" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Akbari et al., 6 Apr 2025</a>).</li> <li>For LLM inference and time-critical workloads, minimizing average waiting time subject to semantic priority constraints:</li> </ul> <p>$$\min \frac{1}{T} \sum_{i=1}^T (f_i - a_i) \quad \mathrm{s.t.}\; \forall i,j: f_i < f_j \implies (f_i < a_j) \vee (f_e(p_i) \leq f_e(p_j))$</p> <p>where$f_e(p)$$encodes emergency level or other semantic priority (<a href="/papers/2506.12204" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Hua et al., 13 Jun 2025</a>).</p> <ul> <li>Multi-objective canonical forms, e.g., knapsack optimization in RAN:</li> </ul> <p>$$\max_{x \in \mathbb{Z}^{+}} \sum_{i \in U} v_i(x_i; K_i) \;\;\mathrm{s.t.}\; \sum_i x_i \leq P,\; l_i(K_i) \leq x_i \leq r_i(K_i)$</p> <p>with$v_i$$mapping KPIs to value in alignment with intent (<a href="/papers/2505.18389" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Elkael et al., 23 May 2025</a>).</p> <p>Intent satisfaction indicators$$\Phi(\mathcal I, x(\cdot))$ may be introduced for constraint satisfaction, utility, and intent-aware selection (Narendra et al., 2024).

  3. Architecture and Algorithms across Domains

  Approaches differ per domain but share core features:

  Domain/Problem	Intent Modeling	Algorithmic Core
  OS Scheduling	Pending external request → “unhappiness”; weighted by customer/process (Nikseresht et al., 2010)	Request-Based Priority Elevation (RBPE), boosting nice values based on socket/IPC activity, dynamically decayed
  LLM Inference	Semantic label (urgency), est. job length (Hua et al., 13 Jun 2025)	Lex-priority min-heap, dual-heap batching, strictly prioritized dequeuing
  RAN Scheduling	Operator NL intent mapped to KPI targets, groupings, value/limit functions (Elkael et al., 23 May 2025)	LLM-generated dApp code: knapsack optimization, performance profile DB, multi-round group-based scheduling
  UAM Rescheduling	Human requests parsed into multi-valued logic flags (Kim et al., 17 Dec 2025)	ASP decision tree + MILP, dynamic constraint locking, explanation generation
  Cloud/Edge/IoT	Response time target, placement, scaling (Akbari et al., 6 Apr 2025)	LLM-based RCA and remediation selection, continuous monitoring, API-driven orchestration
  IIoT NOMA Uplink	Intent vector: per-user ($\epsilon_n$, $L_n$) (Mostafa et al., 2024)	DRL (PPO/DQN) with graph action space, hypergraph matching

  Algorithmic patterns across these systems include:

  • Extraction and formalization of intent, often by ML/LLM or logic programming—DistilBERT for urgency (Hua et al., 13 Jun 2025), LLM for NL parsing (Dutta et al., 2024, Elkael et al., 23 May 2025), 3-valued logic and ASP (Kim et al., 17 Dec 2025), or intent API (Narendra et al., 2024).
  • Construction of intent- or semantic-aware optimization, e.g., request-based boosting, weighted knapsack, MILP with selective constraint binding, RL scheduling with intent-parameterized state spaces.
  • Closed-loop enforcement with monitoring and intent-violation detection, and root-cause analysis potentially delegated to explainable ML (Akbari et al., 6 Apr 2025).

  4. Performance, Robustness, and Empirical Results

  Across a range of systems and workloads, empirical results show robust, intent-aligned improvements:

  • OS RBPE scheduling holds Apache/MySQL/Mplayer performance nearly constant under heavy load, yielding 1.5×–3× speedups for interactive workloads at $N=30$ background jobs (Nikseresht et al., 2010).
  • “IntentContinuum” achieves an 85% intent satisfaction rate and reduces violation time by over 3× versus threshold-based Kubernetes autoscaling, with moderate resource use and low LLM overhead ($≈$13 s prompt round-trip up to 600 nodes) (Akbari et al., 6 Apr 2025).
  • In LLM inference, semantic scheduling cuts normalized wait times by 1–2 orders of magnitude compared to FCFS, with up to $167\times$ speedup in critical EMS scenarios (Hua et al., 13 Jun 2025).
  • Intent-driven backup job scheduling on Netbackup datasets yields a $35\%$ reduction in failure rate versus static approaches, precisely managing concurrency, overlap, and self-affinity based on user preferences (Dutta et al., 2024).
  • ALLSTaR’s intent-based RAN scheduling framework realizes per-UE delay control unattainable by classical slicing, achieving sub-50ms HOL delay at the 99th percentile and fine-grained throughput capping (Elkael et al., 23 May 2025).

  These results substantiate the claim that intent-driven schedulers are able to robustly enforce a diversity of performance, fairness, and reliability targets even under non-stationary, heterogeneous workloads.

  5. Interpretability, Adaptivity, and Human-in-the-Loop Features

  Intent-driven scheduling paradigms place interpretability and adaptivity at the core of control loops:

  • Human users or operators are supported by procedural dialog (decision trees, symbolic logic, LLM feedback) to resolve ambiguous or underspecified requirements before schedule synthesis (Kim et al., 17 Dec 2025).
  • Systems produce detailed explanations, reporting both interpreted intent and post-hoc rationale for resulting schedules, e.g., showing which MILP variables and constraints were affected (Kim et al., 17 Dec 2025).
  • Adaptive mechanisms continually monitor KPI streams, trigger intent violation detection, root-cause analysis (potentially by LLM), and re-optimization, ensuring consistent alignment with evolving requirements (Akbari et al., 6 Apr 2025, Dutta et al., 2024).
  • Hierarchical architectures (e.g., O-RAN meta-scheduling) support scalable decomposition and adaptation across distributed domains, with active inference cycles for continual belief updating and planning (Narendra et al., 2024).

  Adaptivity not only manifests as online re-planning and constraint relaxation, but also as the ability to generalize across intent spaces—redefining user-level intents, adding/removing KPIs, and scaling from per-task to multi-tenant enforcement.

  6. Methodological Extensions and Open Research Challenges

  Recent research identifies several outstanding directions:

  • Automated, intent-to-optimization decomposition at scale, leveraging causal inference, active inference, DRL, or ML-based translation of heterogeneous NL or DSL inputs across administrative domains (Narendra et al., 2024, Elkael et al., 23 May 2025).
  • Compositional, multi-layer scheduling via meta-schedulers and local controllers—e.g., in O-RAN, with formal guarantees for cross-DU consistency (Narendra et al., 2024).
  • Integration of forecasting (for exogenous load, non-backup data, maintenance) and non-stationary behaviors into the intent state and optimization pipeline (Dutta et al., 2024).
  • Standardization and interface evolution for interoperable reporting, real-time intent exchange, and capability discovery (Narendra et al., 2024).
  • Systematic, interpretable performance profiling and ML-driven scheduler selection for massive-scale, dynamic environments (Elkael et al., 23 May 2025).
  • Robustness to intent misclassification (critical in preemptive, high-stakes settings), and synthesis of explainable remedies for violation or infeasibility (Hua et al., 13 Jun 2025, Kim et al., 17 Dec 2025).

  Future work is expected to generalize the paradigm to energy-aware, multi-objective, federated, and cross-layer scenarios, with stronger theoretical and empirical guarantees.

  7. Representative Systems and Comparative Table

  System / Domain	Intent Modeled	Scheduling Method	Key Outcome
  “Customer Appeasement Scheduling” (Nikseresht et al., 2010)	External request delay, unhappiness	RBPE (priority elevation)	1.5–3× improved responsiveness
  “Intent-driven backup jobs” (Dutta et al., 2024)	Anti-/co-overlap $\alpha$, spacing $\epsilon$	KDE, distribution sampling	35% reduction in job failure
  “Semantic Scheduling for LLM” (Hua et al., 13 Jun 2025)	Urgency + length	Lex-priority, dual heap	$10$–$100\times$ lower wait time
  “ALLSTaR for RAN” (Elkael et al., 23 May 2025)	Per-UE delay, slice, mobility	Multi-round knapsack, LLM code	Sub-50ms HOL_99% for RT UEs
  “IntentContinuum” (Akbari et al., 6 Apr 2025)	Response time band, RCA	LLM decision + orchestrator	85% intent satisfaction
  “UAM Rescheduling” (Kim et al., 17 Dec 2025)	Ambiguous changes, partial reopt	ASP, MILP, dialog	Human-aligned, explainable schedules
  “IIoT DRL NOMA” (Mostafa et al., 2024)	Per-user QoS (latency, PER)	RL with hypergraph action space	+25pp task success over Round-Robin

  Each system operationalizes intent-driven scheduling via hybrid logic, ML, optimization, or RL, directly targeting satisfaction of explicit, structured, and context-sensitive requirements.

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  Intent-driven scheduling establishes a rigorous nexus between high-level user/operator purposes and system control, facilitating explainable, adaptive, and efficient scheduling behavior across OS, cloud, wireless, and real-time domains. Ongoing research targets deeper generalization, automation, and theoretical understanding of these paradigms.