Biblio

The Agave Platform first appeared in 2011 as a pilot project for the iPlant Collaborative [11]. In its first two years, Foundation saw over 40% growth per month, supporting 1000+ clients, 600+ applications, 4 HPC systems at 3 centers across the US. It also gained users outside of plant biology. To better serve the needs of the general open science community, we rewrote Foundation as a scalable, cloud native application and named it the Agave Platform. In this paper we present the Agave Platform, a Science-as-a-Service (ScaaS) platform for reproducible science. We provide a brief history and technical overview of the project, and highlight three case studies leveraging the platform to create synergistic value for their users.

The public cloud is moving to a Platform-as-a-Service model where services such as data management, machine learning or image classification are provided by the cloud operator while applications are written in high-level languages and leverage these services. Managed languages such as Java, Python or Scala are widely used in this setting. However, while these languages can increase productivity, they are often associated with problems such as unpredictable garbage collection pauses or warm-up overheads. We argue that the reason for these problems is that current language runtime systems were not initially designed for the cloud setting. To address this, we propose seven tenets for designing future language runtime systems for cloud data centers. We then outline the design of a general substrate for building such runtime systems, based on these seven tenets.

Information Technology experts cite security and privacy concerns as the major challenges in the adoption of cloud computing. On Platform-as-a-Service (PaaS) clouds, customers are faced with challenges of selecting service providers and evaluating security implementations based on their security needs and requirements. This study aims to enable cloud customers the ability to quantify their security requirements in order to identify critical areas in PaaS cloud architectures were security provisions offered by CSPs could be assessed. With the use of an adaptive security mapping matrix, the study uses a quantitative approach to presents findings of numeric data that shows critical architectures within the PaaS environment where security can be evaluated and security controls assessed to meet these security requirements. The matrix can be adapted across different types of PaaS cloud models based on individual security requirements and service level objectives identified by PaaS cloud customers.