  Most cloud computing optimizers explore and improve one workload at a time.
When optimizing many workloads, the single-optimizer approach can be
prohibitively expensive. Accordingly, we examine "collective optimizer" that
concurrently explore and improve a set of workloads significantly reducing the
measurement costs. Our large-scale empirical study shows that there is often a
single cloud configuration which is surprisingly near-optimal for most
workloads. Consequently, we create a collective-optimizer, MICKY, that
reformulates the task of finding the near-optimal cloud configuration as a
multi-armed bandit problem. MICKY efficiently balances exploration (of new
cloud configurations) and exploitation (of known good cloud configuration). Our
experiments show that MICKY can achieve on average 8.6 times reduction in
measurement cost as compared to the state-of-the-art method while finding
near-optimal solutions.
  Hence we propose MICKY as the basis of a practical collective optimization
method for finding good cloud configurations (based on various constraints such
as budget and tolerance to near-optimal configurations).
