  A cloud service provider strives to provide a high Quality of Service (QoS)
to client jobs. Such jobs vary in computational and Service-Level-Agreement
(SLA) obligations, as well as differ with respect to tolerating delays and SLA
violations. The job scheduling plays a critical role in servicing cloud demands
by allocating appropriate resources to execute client jobs. The response to
such jobs is optimized by the cloud provider on a multi-tier cloud computing
environment. Typically, the complex and dynamic nature of multi-tier
environments incurs difficulties in meeting such demands, because tiers are
dependent on each other which in turn makes bottlenecks of a tier shift to
escalate in subsequent tiers. However, the optimization process of existing
approaches produces single-tier-driven schedules that do not employ the
differential impact of SLA violations in executing client jobs. Furthermore,
the impact of schedules optimized at the tier level on the performance of
schedules formulated in subsequent tiers tends to be ignored, resulting in a
less than optimal performance when measured at the multi-tier level. Thus,
failing in committing job obligations incurs SLA penalties that often take the
form of either financial compensations, or losing future interests and
motivations of unsatisfied clients in the service provided. In this paper, a
scheduling and allocation approach is proposed to formulate schedules that
account for differential impacts of SLA violation penalties and, thus, produce
schedules that are optimal in financial performance. A queue virtualization
scheme is designed to facilitate the formulation of optimal schedules at the
tier and multi-tier levels of the cloud environment. Because the scheduling
problem is NP-hard, a biologically inspired approach is proposed to mitigate
the complexity of finding optimal schedules.
