  Modern Infrastructure-as-a-Service Clouds operate in a competitive
environment that caters to any user's requirements for computing resources. The
sharing of the various types of resources by diverse applications poses a
series of challenges in order to optimize resource utilization while avoiding
performance degradation caused by application interference. In this paper, we
present two scheduling methodologies enforcing consolidation techniques on
multicore physical machines. Our resource-aware and interference-aware
scheduling schemes aim at improving physical host efficiency while preserving
the application performance by taking into account host oversubscription and
the resulting workload interference. We validate our fully operational
framework through a set of real-life workloads representing a wide class of
modern cloud applications. The experimental results prove the efficiency of our
system in optimizing resource utilization and thus energy consumption even in
the presence of oversubscription. Both methodologies achieve significant
reductions of the CPU time consumed, reaching up to 50%, while at the same time
maintaining workload performance compared to widely used scheduling schemes
under a variety of representative cloud platform scenarios.
