AuthorsF. Zahid
TitleEfficient and cost-effective data-intensive computing on multi-clouds: An introduction to the MELODIC project
AfilliationCommunication Systems
Project(s)MELODIC: Multi-cloud Execution-ware for Large-scale Optimised Data-Intensive Computing
Publication TypeTalks, invited
Year of Publication2017
Location of TalkBioInformatics in Torun (BIT), Toruń, Poland

Data-intensive computing, often simply referred to as big data, is one of the major current trends in ICT. In areas as diverse as social media, business intelligence, information security, Internet-of-Things, and scientific research, a tremendous amount of data is created or collected at a speed surpassing what we can handle using traditional data management techniques. Life sciences are not different. With the vast amount of biological information available, such as Omics data, unprecedented opportunities for modern research and scientific breakthroughs arise, all depending on the efficient and cost-effective data analysis. Cloud computing, characterized by the paradigm of on-demand network access to computational resources and pay-as-you-go economic model, promises great potential of providing required computational resources for data analytics in Bioinformatics. However, challenges such as lack of data privacy and data-aware cloud federation keeps cloud computing from realizing the full potential for data-intensive applications. At the same time, non-standardized cloud interfaces make it complex to migrate big data applications between platforms thus preventing cloud users from achieving optimal cost-performance ratio for their applications by encouraging vendor lock-in.

In this talk, we  provide an introduction to the MELODIC H2020 project and show how it can be of great value in Bioinformatics. The vision of MELODIC is to enable federated cloud computing for data-intensive applications, and provide the user with an easy-to-use unified cloud environment, hiding the complexity of a multi-cloud. The MELODIC platform enables big data applications to transparently take advantage of distinct characteristics of available private and public clouds by dynamically optimizing resource allocations considering data locality and user's performance and privacy needs. From the perspective of the user, the MELODIC framework appears as an infrastructure-agnostic middleware platform supporting development, deployment, and execution of data-intensive applications on distributed and heterogeneous multi-clouds. For the Bioinformatics community, this could mean utilizing the resources available for multiple cloud providers and private infrastructures in a secure, transparent, efficient, cost-effective, and reliable manner for their big data workloads.

Citation Key25439