  We study the online busy time scheduling model on heterogeneous machines. In
our setting, unit-length jobs arrive online with a deadline that is known to
the algorithm at the job's arrival time. An algorithm has access to machines,
each with different associated capacities and costs. The goal is to schedule
jobs on machines before their deadline, so that the total cost incurred by the
scheduling algorithm is minimized. Relatively little is known about online busy
time scheduling when machines are heterogeneous (i.e., have different costs and
capacities), despite this being the most practical model for clients using
cloud computing services. We make significant progress in understanding this
model by designing an 8-competitive algorithm for the problem on unit-length
jobs, and providing a lower bound on the competitive ratio of 2. We further
prove that our lower bound is tight in the natural setting when jobs have
agreeable deadlines.
