Red-ONION is presented, a high-speed disk-to-disk transfer system that connects research facilities, on campus and beyond, to a computing center, and the end-to-end optimizations across the transfer software, the operating system, and the storage that this requires are described.
Abstract
Modern experimental instruments produce data faster than general-purpose file transfer interfaces can move it, so delivery to the computing infrastructure has become a bottleneck in the research process. At many universities and research institutes, moreover, the instruments that generate research data and the high-performance computing systems that analyze it are separated both geographically and organizationally, because each demands its own expertise and installation environment. Connecting the two seamlessly is a pressing challenge for data-driven science. This article presents RED-ONION, a high-speed disk-to-disk transfer system that connects research facilities, on campus and beyond, to a computing center. The system combines data transfer nodes, a dedicated high-bandwidth network, an all-flash parallel file system, and multi-threaded transfer software that parallelizes network transmission and storage access. The design targets the wire rate both along the entire path, from the read on the sender storage to the write on the receiver storage, and for a single file between one pair of nodes rather than only in aggregate over many files or nodes. We describe the end-to-end optimizations across the transfer software, the operating system, and the storage that this requires. We evaluate a prototype deployed over a 100 Gbps transpacific path between Atlanta and Tokyo with a 150 ms round-trip time, on which a single 1 TB file transfer reached 90 Gbps, delivering a terabyte in approximately 95 s. Moving a dataset of this size therefore becomes a routine step, and the computing center serves an instrument as if the two were co-located.
HARE introduces a holistic allocation model that captures the demand correlation between cache size and other resources, and uses a novel two-phase harvest/redistribute method to optimize resource allocation across tenants, maximizing the throughput of each while maintaining fairness.
Chen-Hao Ye, Wan-Xiang Zhong, Andrea C. Arpaci-Dusseau et al.· 0 citations
The main result concerns the plain-load path: attained LDG bandwidth peaks at a small offered per-thread load (K ~ 2) and then declines, by about 35% from K=2 to K=8 at the authors' primary configuration.
We describe the integration of io_uring into Oracle Database's storage layer and the architectural decisions required to deploy it in a production multi-process RDBMS. Our design uses per-process ring contexts that eliminate inter-process synchronization, a shared buffer registration mechanism now part of the mainline...
R. Chowdhury, A. Shah, Margaret Susairaj et al.· 0 citations
High-energy physics analyses often process the same data as physicists refine algorithms and test new ideas. With data increasingly read from remote storage, each iteration is subject to network latency and depends on network bandwidth and shared-storage throughput, which can vary substantially under load. We present u...
D. Kovalskyi, J. Eysermans, M. D'Alfonso et al.· 0 citations
Nexus is presented, a quasi-user-space I/O stack with a split architecture: a user-space data plane and a lightweight kernel control plane that enables efficient memory usage through shared data structures and in-kernel page reclamation that coordinate global allocation and reclamation across all processes.
Riwei Pan, Yu Liang, Sam H. Noh et al.· Proceedings of the 18th ACM...· 0 citations
Near Data Processing (NDP) has the potential to significantly improve system performance and energy by alleviating data movement bottlenecks. However, most NDP proposals pose heavy requirements for the software stack, data layout, and/or the underlying hardware. To broaden NDP adoption, this work focuses on a modular h...
Shreyas Singh, Pratyush Nandi, Lin Jia et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.