Learning Adaptive SED for heterogeneous load balancing
This work proposes an online learning algorithm that converges to SED while learning the service rates and proves that the algorithm achieves finite regret; this differs from classical Multi-Armed Bandit settings where regret typically grows logarithmically in time.
Sanne van Kempen, Jaron Sanders, Fiona Sloothaak et al.
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