A cache-based data movement infrastructure for on-demand scientific cloud computing

Abstract

As cloud computing has become the de facto standard for big data processing, there is interest in using a multi-cloud environment that combines public cloud resources with private on-premise infrastructure. However, by decentralizing the infrastructure, a uniform storage solution is required to provide data movement between different clouds to assist on-demand computing. This paper presents a solution based on our earlier work, the MeDiCI (Metropolitan Data Caching Infrastructure) architecture. Specially, we extend MeDiCI to simplify the movement of data between different clouds and a centralized storage site. It uses a hierarchical caching system and supports most popular infrastructure-as-a-service (IaaS) interfaces, including Amazon AWS and OpenStack. As a result, our system allows the existing parallel data intensive application to be offloaded into IaaS clouds directly. The solution is illustrated using a large bioinformatics application, a Genome Wide Association Study (GWAS), with Amazons AWS, HUAWEI Cloud, and a private centralized storage system. The system is evaluated on Amazon AWS and the Australian national cloud.