Research map / Bandits and online learning / Online convex optimization and games
Scheduling jobs with stochastic holding costs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine an initial preemptive phase with later nonpreemptive scheduling (ablation) | Empirical cμ rule · Varying gaps between mean holding costs | regret | Helped |
| Use preemptive scheduling exclusively instead of switching to nonpreemptive scheduling (ablation) | Empirical cμ rule · Close mean holding costs | regret | Hurt |
| Use nonpreemptive scheduling exclusively instead of an initial preemption phase (ablation) | Empirical cμ rule · Large gaps between mean holding costs | regret | Hurt |
| Prioritize the largest job class unless another class is confidently higher-cost (ablation) | PN cμ rule · Unbalanced job classes | regret: PN cμ rule: \Omega(N{S}^{2/3}); refined PN cμ rule: O\left(\max\left\{{S}^{2/3}(\log N{S})^{1/3}, N^{1/2}{S}^{1/2}(\log | Helped |
| Use largest-class prioritization in the refined rule (ablation) | PN cμ rule · Unbalanced job classes with one class containing all but one job | regret growth with N | Helped |
| Use largest-class prioritization in the refined rule (ablation) | PN cμ rule · Balanced versus unbalanced job classes | regret growth with N | Mixed |
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