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Syst."],"published-print":{"date-parts":[[2021,3,31]]},"abstract":"<jats:p>Modern science and engineering computing environments often feature storage systems of different types, from parallel file systems in high-performance computing centers to object stores operated by cloud providers. To enable easy, reliable, secure, and performant data exchange among these different systems, we propose Connector, a plug-able data access architecture for diverse, distributed storage. By abstracting low-level storage system details, this abstraction permits a managed data transfer service (Globus, in our case) to interact with a large and easily extended set of storage systems. Equally important, it supports third-party transfers: that is, direct data transfers from source to destination that are initiated by a third-party client but do not engage that third party in the data path. The abstraction also enables management of transfers for performance optimization, error handling, and end-to-end integrity. We present the Connector design, describe implementations for different storage services, evaluate tradeoffs inherent in managed vs. direct transfers, motivate recommended deployment options, and propose a model-based method that allows for easy characterization of performance in different contexts without exhaustive benchmarking.<\/jats:p>","DOI":"10.1145\/3452007","type":"journal-article","created":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T02:06:54Z","timestamp":1622340414000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Design and Evaluation of a Simple Data Interface for Efficient Data Transfer across Diverse Storage"],"prefix":"10.1145","volume":"6","author":[{"given":"Zhengchun","family":"Liu","sequence":"first","affiliation":[{"name":"Argonne National Laboratory, Lemont, IL"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajkumar","family":"Kettimuthu","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joaquin","family":"Chung","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rachana","family":"Ananthakrishnan","sequence":"additional","affiliation":[{"name":"The University of Chicago, Chicago, IL"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Link","sequence":"additional","affiliation":[{"name":"The University of Chicago, Chicago, IL"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ian","family":"Foster","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory and The University of Chicago"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,5,29]]},"reference":[{"volume-title":"A cache-based data movement infrastructure for on-demand scientific cloud computing","author":"Abramson David","key":"e_1_2_1_1_1","unstructured":"David Abramson , Jake Carroll , Chao Jin , Michael Mallon , Zane van Iperen , Hoang Nguyen , Allan McRae , and Liang Ming . 2019. 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