In parallel with big data processing and analysis dominating the usage of distributed and cloud infrastructures, the demand for distributed metadata access and transfer has increased. In many application domains, the volume of data generated exceeds petabytes, while the corresponding metadata amounts to terabytes or even more. In this paper, we propose a novel solution for efficient and scalable metadata access for distributed applications across wide-area networks, dubbed SMURF. Our solution combines novel pipelining and concurrent transfer mechanisms with reliability, provides distributed continuum caching and prefetching strategies to sidestep fetching latency, and achieves scalable and high-performance metadata fetch/prefetch services in the cloud. We also study the phenomenon of semantic locality in real trace logs which is not well utilized in metadata access prediction. We implement our predictor based on this observation and compare it with three existing state-of-the-art prefetch schemes on Yahoo! Hadoop audit traces. By effectively caching and prefetching metadata based on the access patterns, our continuum caching and prefetching mechanism greatly improves local cache hit rate and reduces the average fetching latency. We replayed approximately 20 Million metadata access operations from real audit traces, in which our system achieved 80% accuracy during prefetch prediction and reduced the average fetch latency 50% compared to the state-of-the-art mechanisms.
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SMURF: Efficient and Scalable Metadata Access for Distributed Applications
In parallel with big data processing and analysis dominating the usage of distributed and Cloud infrastructures, the demand for distributed metadata access and transfer has increased. The volume of data generated by many application domains exceeds petabytes, while the corresponding metadata amounts to terabytes or even more. This paper proposes a novel solution for efficient and scalable metadata access for distributed applications across wide-area networks, dubbed SMURF. Our solution combines novel pipelining and concurrent transfer mechanisms with reliability, provides distributed continuum caching and semantic locality-aware prefetching strategies to sidestep fetching latency, and achieves scalable and high-performance metadata fetch/prefetch services in the Cloud. We incorporate the phenomenon of semantic locality awareness for increased prefetch prediction rate using real-life application I/O traces from Yahoo! Hadoop audit logs and propose a novel prefetch predictor. By effectively caching and prefetching metadata based on the access patterns, our continuum caching and prefetching mechanism significantly improves the local cache hit rate and reduces the average fetching latency. We replay approximately 20 Million metadata access operations from real audit traces, where SMURF achieves 90% accuracy during prefetch prediction and reduced the average fetch latency by 50% compared to the state-of-the-art mechanisms.
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- Award ID(s):
- 2007829
- NSF-PAR ID:
- 10442698
- Date Published:
- Journal Name:
- IEEE transactions on parallel and distributed systems
- Volume:
- 33
- Issue:
- 12
- ISSN:
- 2161-9883
- Page Range / eLocation ID:
- 3915-3928
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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