  This paper considers a Markov decision model for profit maximization of a
cloud computing service provider catering to customers submitting jobs with
firm real-time random deadlines. Customers are charged on a per-job basis,
receiving a full refund if deadlines are missed. The service provider leases
computing resources from an infrastructure provider in a two-tier scheme:
long-term leasing of basic infrastructure, consisting of heterogeneous parallel
service nodes, each modeled as a multi-server queue, and short-term leasing of
external servers. Given the intractability of computing an optimal dynamic
resource allocation and job routing policy, maximizing the long-run average
profit rate, the paper addresses the design, implementation and testing of
low-complexity heuristics. The policies considered are a static policy given by
an optimal Bernoulli splitting, and three dynamic index policies based on
different index definitions: individually optimal (IO), policy improvement (PI)
and restless bandit (RB) indices. The paper shows how to implement efficiently
each such policy, and presents a comprehensive empirical comparison, drawing
qualitative insights on their strengths and weaknesses, and benchmarking their
performance in an extensive study.
