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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.
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Learning I/O Access Patterns to Improve Prefetching in SSDs
Abstract. Flash based solid state drives (SSDs) have established them- selves as a higher-performance alternative to hard disk drives in cloud and mobile environments. Nevertheless, SSDs remain a performance bot- tleneck of computer systems due to their high I/O access latency. A com- mon approach for improving the access latency is prefetching. Prefetch- ing predicts future block accesses and preloads them into main memory ahead of time. In this paper, we discuss the challenges of prefetching in SSDs, explain why prior approaches fail to achieve high accuracy, and present a neural network based prefetching approach that signi cantly outperforms the state-of the-art. To achieve high performance, we ad- dress the challenges of prefetching in very large sparse address spaces, as well as prefetching in a timely manner by predicting ahead of time. We collect I/O trace les from several real-world applications running on cloud servers and show that our proposed approach consistently outper- forms the existing stride prefetchers by up to 800 and prior prefetching approaches based on Markov chains by up to 8. Furthermore, we pro- pose an address mapping learning technique to demonstrate the applica- bility of our approach to previously unseen SSD workloads and perform a hyperparameter sensitivity study.
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- Award ID(s):
- 1823559
- PAR ID:
- 10187652
- Date Published:
- Journal Name:
- ICML-PKDD
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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