Brief Announcement: Load Balancing with Duration Predictions

We study the classic fully dynamic load balancing problem on unrelated machines where jobs arrive and depart over time and the goal is minimizing the maximum load, or more generally the lp-norm of the load vector. Previous work either studied the clairvoyant setting in which exact durations are known to the algorithm, or the unknown duration setting in which no information on the duration is given to the algorithm. For the clairvoyant setting algorithms with polylogarithmic competitive ratios were designed, while for the unknown duration setting strong lower bounds exist and only polynomial competitive factors are possible. We bridge this gap by studying a more realistic model in which some estimate/prediction of the duration is available to the algorithm. We observe that directly incorporating predictions into classical load balancing algorithms designed for the clairvoyant setting can lead to a notable decline in performance. We design better algorithms whose performance depends smoothly on the accuracy of the available prediction. We also prove lower bounds on the competitiveness of algorithms that use such inaccurate predictions.