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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 SLAviolations. The job scheduling plays a critical role in servicing cloud demandsby allocating appropriate resources to execute client jobs. The response tosuch jobs is optimized by the cloud provider on a multi-tier cloud computingenvironment. Typically, the complex and dynamic nature of multi-tierenvironments incurs difficulties in meeting such demands, because tiers aredependent on each other which in turn makes bottlenecks of a tier shift toescalate in subsequent tiers. However, the optimization process of existingapproaches produces single-tier-driven schedules that do not employ thedifferential impact of SLA violations in executing client jobs. Furthermore,the impact of schedules optimized at the tier level on the performance ofschedules formulated in subsequent tiers tends to be ignored, resulting in aless than optimal performance when measured at the multi-tier level. Thus,failing in committing job obligations incurs SLA penalties that often take theform of either financial compensations, or losing future interests andmotivations of unsatisfied clients in the service provided. In this paper, ascheduling and allocation approach is proposed to formulate schedules thataccount for differential impacts of SLA violation penalties and, thus, produceschedules that are optimal in financial performance. A queue virtualizationscheme is designed to facilitate the formulation of optimal schedules at thetier and multi-tier levels of the cloud environment. Because the schedulingproblem is NP-hard, a biologically inspired approach is proposed to mitigatethe complexity of finding optimal schedules.

SLA-Driven Load Scheduling in Multi-Tier Cloud Computing: Financial Impact Considerations