OnTimeURB: Multi-Cloud Resource Brokering for Bioinformatics Workflows

Scientific workflows due to their data and memory intensive requirements are among the prime applications which benefit by leveraging cloud computing. However, Cloud service providers (CSPs) have distinct policies and service dynamics that present a problem of excess choice for users. Performance an...

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Vydáno v:2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) s. 466 - 473
Hlavní autoři: Pandey, Ashish, Lyu, Zhen, Joshi, Trupti, Calyam, Prasad
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.11.2019
Témata:
Bioinformatics
Bioinformatics workflows
Cloud computing
Cost
Costs
Custom templates
Interoperability
Logic gates
Monitoring
Multi-Cloud interoperability
Nearest neighbor methods
Optimization
Optimization models
Scalability
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Shrnutí:Scientific workflows due to their data and memory intensive requirements are among the prime applications which benefit by leveraging cloud computing. However, Cloud service providers (CSPs) have distinct policies and service dynamics that present a problem of excess choice for users. Performance and cost of the cloud services are among the principal factors in CSP selection for scientific bioinformatics workflows. The workflows typically are based on private data, and require diverse cloud resources, thus often requiring synergistic services from multiple CSPs. In this paper, we address this challenge of multi-cloud resource selection using cloud template solutions based on user specifications. We propose an optimizer that incorporates a combinatorial optimization model built on performance, cost and CSPs interoperability factors. The optimizer is integrated within a novel resource broker (i.e., OnTimeURB) for prescriptive recommendations of template solutions with intuitive choices for users. We implement and evaluate the OnTimeURB recommendations framework with a catalog of bioinformatics workflow applications integrated within a KBCommons science gateway. The evaluation considered four CSP resources featuring more than 300 different machine configuration instances. Our evaluation results show that our OnTimeURB creates consistently more economical, performance optimized and practical cloud solutions compared to a k-nearest neighbors (k-NN) approach.
DOI:10.1109/BIBM47256.2019.8983386