Online Scheduling Fair of Spark Workloads with Mesos using Different Fair Allocation Algorithms

In the following, we present example illustrative and experimental results comparing fair schedulers allocating resources (indexed r) from multiple servers (indexed i, with resource capacities ci,r) to distributed application frameworks (indexed r, with resource demands per task di,r). Resources are allocated so that at least one resource r is exhausted in every server. Schedulers considered include DRF (DRFH) and Best-Fit DRF (BF-DRF) [1, 11], TSF [10], and PS-DSF [2]. We also consider server selection under Randomized Round Robin (RRR) and based on their residual (unreserved) resources. In the following, we consider cases with frameworks of equal priority and without server-preference constraints. We first give typical results of an illustrative numerical study and then give typical results of a study involving Spark workloads on Mesos, which we have modified and open-sourced to prototype different schedulers.