Monetary Cost Optimizations for Hosting Workflow-as-a-Service in IaaS Clouds
Amelie Chi Zhou, Bingsheng He, Cheng Liu

TL;DR
This paper introduces Dyna, a probabilistic scheduling framework for WaaS in IaaS clouds that minimizes costs while providing probabilistic deadline guarantees, accounting for cloud performance and price variability.
Contribution
It presents a novel probabilistic scheduling approach with A*-based instance configuration and spot instance refinement, addressing dynamic cloud environments.
Findings
Accurately predicts probabilistic deadline satisfaction.
Reduces monetary costs compared to existing methods.
Effective in real-world scientific workflows.
Abstract
Recently, we have witnessed workflows from science and other data-intensive applications emerging on Infrastructure-asa-Service (IaaS) clouds, and many workflow service providers offering workflow as a service (WaaS). The major concern of WaaS providers is to minimize the monetary cost of executing workflows in the IaaS cloud. While there have been previous studies on this concern, most of them assume static task execution time and static pricing scheme, and have the QoS notion of satisfying a deterministic deadline. However, cloud environment is dynamic, with performance dynamics caused by the interference from concurrent executions and price dynamics like spot prices offered by Amazon EC2. Therefore, we argue that WaaS providers should have the notion of offering probabilistic performance guarantees for individual workflows on IaaS clouds. We develop a probabilistic scheduling…
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Taxonomy
TopicsCloud Computing and Resource Management · Distributed and Parallel Computing Systems · Scientific Computing and Data Management
