Business-Cycle-Aware Preference-Adaptive Dueling DQN for Library Computing Resource Scheduling

Smart library services increasingly depend on heterogeneous IoT terminals and edge nodes, whose computing resources must be scheduled under three conflicting objectives—latency, energy, and cost—with a trade-off that shifts with the business cycle. Existing deep-reinforcement-learning (DRL) schedulers mostly scalarize these objectives with fixed weights and therefore cannot re-balance the trade-off as the business context changes. We propose BC-PA-DDQN, a business-cycle-aware preference-adaptive dueling double DQN that conditions the Q-value on a task-level preference vector: each task’s weights combine a task-type prior, a business-period modulation, and a per-task log-normal perturbation, replacing the fixed uniform preference of the baseline PA-DDQN. Because no public library-computing dataset with per-task telemetry exists, we calibrate the business-cycle arrival multipliers and task-type composition from 18 months of real library operational data and model the task-size and SLA distributions from domain knowledge. Over 10 seeds with per-seed paired sign-permutation tests, BC-PA-DDQN significantly outperforms the fixed-preference PA-DDQN (+0.984 reward, Holm-corrected p=0.0234, 10/10 seeds) and the DDQN/D3QN/greedy/random baselines, and it shows no significant difference from a maximum-entropy SAC baseline (p=0.56). A factor test and a leave-one-phase-out test show that the numerical preference carries information beyond the phase label and transfers to unseen phases. The gain is an explicit energy-for-cost trade-off, and the 5316-parameter network is about 3× smaller than SAC.