Apache Mesos, a cluster-wide resource manager, is widely deployed in massive
scale at several Clouds and Data Centers. Mesos aims to provide high cluster
utilization via fine grained resource co-scheduling and resource fairness among
multiple users through Dominant Resource Fairness (DRF) based allocation. DRF
takes into account different resource types (CPU, Memory, Disk I/O) requested
by each application and determines the share of each cluster resource that
could be allocated to the applications. Mesos has adopted a two-level
scheduling policy: (1) DRF to allocate resources to competing frameworks and
(2) task level scheduling by each framework for the resources allocated during
the previous step. We have conducted experiments in a local Mesos cluster when
used with frameworks such as Apache Aurora, Marathon, and our own framework
Scylla, to study resource fairness and cluster utilization. Experimental
results show how informed decision regarding second level scheduling policy of
frameworks and attributes like offer holding period, offer refusal cycle and
task arrival rate can reduce unfair resource distribution. Bin-Packing
scheduling policy on Scylla with Marathon can reduce unfair allocation from
38\% to 3\%. By reducing unused free resources in offers we bring down the
unfairness from to 90\% to 28\%. We also show the effect of task arrival rate
to reduce the unfairness from 23\% to 7\%