  Datacenters are the main infrastructure on top of which cloud computing
services are offered. Such infrastructure may be shared by a large number of
tenants and applications generating a spectrum of datacenter traffic. Delay
sensitive applications and applications with specific Service Level Agreements
(SLAs), generate deadline constrained flows, while other applications initiate
flows that are desired to be delivered as early as possible. As a result,
datacenter traffic is a mix of two types of flows: deadline and regular. There
are several scheduling policies for either traffic type with focus on
minimizing completion times or deadline miss rate. In this report, we apply
several scheduling policies to mix traffic scenario while varying the ratio of
regular to deadline traffic. We consider FCFS (First Come First Serve), SRPT
(Shortest Remaining Processing Time) and Fair Sharing as deadline agnostic
approaches and a combination of Earliest Deadline First (EDF) with either FCFS
or SRPT as deadline-aware schemes. In addition, for the latter, we consider
both cases of prioritizing deadline traffic (Deadline First) and prioritizing
regular traffic (Deadline Last). We study both light-tailed and heavy-tailed
flow size distributions and measure mean, median and tail flow completion times
(FCT) for regular flows along with Deadline Miss Rate (DMR) and average
lateness for deadline flows. We also consider two operation regimes of
lightly-loaded (low utilization) and heavily-loaded (high utilization). We find
that performance of deadline-aware schemes is highly dependent on fraction of
deadline traffic. With light-tailed flow sizes, we find that FCFS performs
better in terms of tail times and average lateness while SRPT performs better
in average times and deadline miss rate. For heavy-tailed flow sizes, except
for tail times, SRPT performs better in all other metrics.
