A new arXiv paper addresses how mobile inference can be split across a GPU and multiple CPU core clusters on heterogeneous chips. Rather than assigning whole operators to one processor, the approach partitions work in a way that accounts for the characteristics of each partition. The authors argue this improves co-execution efficiency on mobile hardware.
A new arXiv paper introduces HiGFRL, a method that fuses hierarchical graph representations with reinforcement learning to schedule tasks with dependencies across heterogeneous cloud clusters. The authors target the difficulty of jointly handling DAG structure and multi-dimensional resource limits in online settings, which they say existing deep RL schedulers address only partially. The work is a preprint and has not yet been peer reviewed.
A new arXiv preprint introduces HQARRF, a scheduling approach that combines hierarchical Q-learning with force-aware routing to coordinate multiple chargers in wireless rechargeable sensor networks. The method aims to balance sensor death risk, charger energy limits, travel costs, return-to-base feasibility and inter-charger coordination, which urgency-driven schedulers handle poorly. The work is categorized under machine learning research.
A new arXiv paper tackles the stability-plasticity dilemma in continual reinforcement learning by progressively expanding a DQN agent guided by a curriculum. The approach is applied to adaptive train scheduling, where conditions shift over time and earlier knowledge must be retained. It aims to let the agent grow more complex behaviors without overwriting what it already learned.
Researchers propose MAPS, a scheduling framework designed to handle bursty large language model workloads on cloud infrastructure. The work targets memory-bound decode instances in prefill-decode disaggregated serving setups, where memory pressure limits throughput. It aims to improve scheduling decisions by predicting memory needs ahead of time.
A new arXiv paper applies genetic programming to dynamic multi-mode resource-constrained project scheduling, where tasks face precedence rules, limited resources, several execution modes, and uncertain durations. The authors add surrogate assistance and phenotypic characterisation to guide the evolutionary search toward promising schedules. The work sits at the intersection of evolutionary computation and operations research rather than commercial AI products.
A new arXiv paper proposes a hierarchical reinforcement learning method that uses graph attention to schedule workflows in cloud environments. The approach targets three competing goals at once: meeting deadlines, improving container utilization, and lowering energy use. It also accounts for unpredictable task runtimes, communication costs that depend on where tasks are placed, and the need to decide task assignment and container selection together.
A new arXiv preprint examines how machine learning models for predicting job runtimes in grid and distributed computing should be validated without data leakage. The authors argue that evaluation must reflect real deployment constraints, since overly optimistic results can mislead scheduling-aware resource management. The work revisits CPU burst time prediction with these safeguards in mind.
A new arXiv paper argues that existing LLM scheduling designs were built around human users and may not suit agentic workloads, where software agents issue the requests. The authors note that agents consume far more tokens per request and exhibit different traffic patterns than people. They propose SMetric, a session-centric scheduling approach intended to balance serving performance under these agent-driven conditions.
A new arXiv paper tackles how to schedule quantum circuits across systems that combine high-performance computing with several quantum processing units, where the QPUs differ in size, connectivity, native gate sets, and noise. The proposed approach factors in these hardware differences when assigning circuits, aiming to maximize fidelity on today's noisy devices where errors compound during compilation.
A research paper presents a method for timing scheduled batch jobs on large asynchronous computing platforms, where tasks such as promotional notifications and recommender pre-computation must run at the right moment. The approach personalizes execution times across very large job volumes, aiming to improve how promptly information is delivered or refreshed.
A new arXiv paper tackles how to plan and schedule activities in business processes when the exact order of steps is not known in advance, since choices often depend on data that only appears during execution. The work aims to improve efficiency measures such as makespan despite this uncertainty. This is an extended version of the paper.
A new arXiv preprint examines how agentic LLM workflows, which alternate model turns with tool calls, are affected by the timing of when completed turns are dispatched. The authors argue that separating a turn's readiness from its release lets runtimes optimize for tail latency rather than simply pushing each turn out immediately. The work targets scheduling policies that reduce worst-case end-to-end completion times for these multi-step pipelines.