Genomics as a service: A joint computing and networking perspective

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2018-11, Vol.145, p.27-51
• Main Authors: Reali, G., Femminella, M., Nunzi, E., Valocchi, D.
• Format: Article
• Language: English
• Subjects: Big Data; Cloud computing; Computational grids; Computer networks; Genomic; Genomics; Medical research; Network virtualization; Pipeline; Pipelines; Taxonomy; Visualization
• ISSN: 1389-1286; 1872-7069
• DOI: 10.1016/j.comnet.2018.08.005

This paper shows a global picture of the deployment of networked processing services for genomic data sets. Many current research and medical activities make an extensive use of genomic data, which are massive and rapidly increasing over time. They are typically stored in remote databases, accessible by using Internet connections. For this reason, the quality of the available network services could be a significant issue for effectively handling genomic data through networks. A first contribution of this paper consists in identifying the still unexploited features of genomic data that could allow optimizing their networked management. The second and main contribution is a methodological classification of computing and networking alternatives, which can be used to deploy what we call the Genomics-as-a-Service (GaaS) paradigm. In more detail, we analyze the main genomic processing applications, and classify both the computing alternatives to run genomics workflows, in either a local machine or a distributed cloud environment, and the main software technologies available to develop genomic processing services. Since an analysis encompassing only the computing aspects would provide only a partial view of the issues for deploying GaaS systems, we present also the main networking technologies that are available to efficiently support a GaaS solution. We first focus on existing service platforms, and analyze them in terms of service features, such as scalability, flexibility, and efficiency. Then, we present a taxonomy for both wide area and datacenter network technologies that may fit the GaaS requirements. It emerges that virtualization, both in computing and networking, is the key for a successful large-scale exploitation of genomic data, by pushing ahead the adoption of the GaaS paradigm. Finally, the paper illustrates a short and long-term vision on future research challenges in the field.