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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Improving Cache Performance for Large-Scale Photo Stores via Heuristic Prefetching Scheme
Photo service providers are facing critical challenges of dealing with the huge amount of photo storage, typically in a magnitude of billions of photos, while ensuring national-wide or world-wide satisfactory user experiences. Distributed photo caching architecture is widely deployed to meet high performance expectations, where efficient still mysterious caching policies play essential roles. In this work, we present a comprehensive study on internet-scale photo caching algorithms in the case of QQPhoto from Tencent Inc., the largest social network service company in China. We unveil that even advanced cache algorithms can only perform at a similar level as simple baseline algorithms and there still exists a large performance gap between these cache algorithms and the theoretically optimal algorithm due to the complicated access behaviors in such a large multi-tenant environment. We then expound the reasons behind this phenomenon via extensively investigating the characteristics of QQPhoto workloads. Finally, in order to realistically further improve QQPhoto cache efficiency, we propose to incorporate a prefetcher in the cache stack based on the observed immediacy feature that is unique to the QQPhoto workload. The prefetcher proactively prefetches selected photos into cache before are requested for the first time to eliminate compulsory misses and promote hit ratios. Our extensive evaluation results show that with appropriate prefetching we improve the cache hit ratio by up to 7.4%, while reducing the average access latency by 6.9% at a marginal cost of 4.14% backend network traffic compared to the original system that performs no prefetching.
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- NSF-PAR ID:
- 10106222
- Date Published:
- Journal Name:
- IEEE Transactions on Parallel and Distributed Systems
- ISSN:
- 1045-9219
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TPDS.2019.2902392
