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

TL;DR

This paper proposes a biologically inspired scheduling approach for multi-tier cloud computing that considers SLA violation penalties to optimize financial outcomes, addressing the limitations of existing tier-focused methods.

Contribution

It introduces a novel queue virtualization scheme and a multi-tier scheduling method that accounts for SLA violation impacts, improving financial performance in cloud service provisioning.

Findings

Schedules considering SLA penalties improve financial outcomes.

The biologically inspired approach mitigates NP-hard scheduling complexity.

Multi-tier scheduling enhances overall cloud service efficiency.

Abstract

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.