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

Year: 2026
Type: article
Abstract: 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.
Source: Sensors
DOI: 10.3390/s26227108
Language: English
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225728435
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Cites: 33
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