  The rapid proliferation of shared edge computing platforms has enabled
application service providers to deploy a wide variety of services with
stringent latency and high bandwidth requirements. A key advantage of these
platforms is that they provide pay-as-you-go flexibility by charging clients in
proportion to their resource usage through short-term contracts. This affords
the client significant cost-saving opportunities, by dynamically deciding when
to host its service on the platform, depending on the changing intensity of
requests.
  A natural policy for our setting is the Time-To-Live (TTL) policy. We show
that TTL performs poorly both in the adversarial arrival setting, i.e., in
terms of the competitive ratio, and for i.i.d. stochastic arrivals with low
arrival rates, irrespective of the value of the TTL timer.
  We propose an online policy called RetroRenting (RR) and show that in the
class of deterministic online policies, RR is order-optimal with respect to the
competitive ratio.
  In addition, we provide performance guarantees for RR for i.i.d. stochastic
arrival processes coupled with negatively associated rent cost sequences and
prove that it compares well with the optimal online policy. Further, we conduct
simulations using both synthetic and real world traces to compare the
performance of RR with the optimal offline and online policies. The simulations
show that the performance of RR is near optimal for all settings considered.
Our results illustrate the universality of RR.
