An Online Scalable Algorithm for Minimizing `k-norms of Weighted Flow Time on Unrelated Machines

We consider the problem of scheduling jobs that arrive online in the unrelated machine model to minimize `k norms of weighted flowtime. In the unrelated setting, the processing time and weight of a job depends on the machine it is assigned to, and it is perhaps the most general machine model considered in scheduling literature. Chadha et al. [10] obtained a recent breakthrough result in obtaining the first non-trivial algorithm for minimizing weighted flowtime (that is, the `1 norm) in this very general setting via a novel potential function based analysis. They described a simple algorithm and showed that for any > 0 it is (1 + )-speed O(1/ )-competitive (a scalable algorithm). In this paper we give the first non-trivial and scalable algorithm for minimizing `k norms of weighted flowtime in the unrelated machine model; for any > 0, the algorithm is O(k/ )-competitive. The algorithm is immediatedispatch and non-migratory. Our result is of both practical and theoretical interest. Scheduling to minimize `k norms of flowtime for some small k > 1 has been shown to balance total response time and fairness, which is desirable in practice. On the theoretical side, `k norms for k > 1 pose substantial technical hurdles when compared to when k = 1 even for the single machine case. Our work develops a novel potential function as well as several tools that can be used to lower bound the optimal solution.