Statistical Model Checking-Based Evaluation and Optimization for Cloud Workflow Resource Allocation

Abstract

Due to the existence of resource variations, it is very challenging for Cloud workflow resource allocation strategies to guarantee a reliable Quality of Service (QoS). Although dozens of resource allocation heuristics have been developed to improve the QoS of Cloud workflow, it is hard to predict their performance under variations because of the lack of accurate modeling and evaluation methods. So far, there is no comprehensive approach that can quantitatively reason the capability of resource allocation strategies or enable the tuning of parameters to optimize resource allocation solutions under variations. To address the above problems, this paper proposes a novel framework that can evaluate and optimize resource allocation strategies effectively and quantitatively. By using the statistical model checker UPPAAL-SMC and supervised learning approaches, our framework can: i) conduct complex QoS queries on resource allocation instances considering resource variations; ii) make quantitative and qualitative comparisons among resource allocation strategies; iii) enable the tuning of parameters to improve the overall QoS; and iv) support the quick optimization of overall workflow QoS under customer requirements and resource variations. The experimental results demonstrate that our automated framework can support both the Service Level Agreement (SLA) negotiation and workflow resource allocation optimization efficiently.