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Data Similarity-Aware Computation Infrastructure for the Cloud

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Abstract

The cloud is emerging for scalable and efficient cloud services. In order to meet the needs of handling massive data and decreasing data migration, the computation infrastructure requires efficient data placement and proper management for cached data. We propose an efficient and cost-effective multilevel caching scheme, called MERCURY, as computation infrastructure of the cloud. The idea behind MERCURY is to explore and exploit data similarity and support efficient data placement. In order to accurately and efficiently capture the data similarity, we leverage low-complexity Locality-Sensitive Hashing (LSH). In our design, in addition to the problem of space inefficiency, we identify that a conventional LSH scheme also suffers from the problem of homogeneous data placement. To address these two problems, we design a novel Multicore-enabled LSH (MC-LSH) that accurately captures the differentiated similarity across data. The similarity-aware MERCURY hence partitions data into L1 cache, L2 cache, and main memory based on their distinct localities, which help optimize cache utilization and minimize the pollution in the last-level cache. Besides extensive evaluation through simulations, we also implemented MERCURY in a system. Experimental results based on real-world applications and datasets demonstrate the efficiency and efficacy of our proposed schemes (©{2014}IEEE. Reprinted, with permission, from Ref. [1].).

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Authors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, Hubei, China

    Yu Hua

  2. McGill University, Montreal, QC, Canada

    Xue Liu

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  1. Yu Hua
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  2. Xue Liu
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Correspondence to Yu Hua .

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Hua, Y., Liu, X. (2019). Data Similarity-Aware Computation Infrastructure for the Cloud. In: Searchable Storage in Cloud Computing. Springer, Singapore. https://doi.org/10.1007/978-981-13-2721-6_7

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  • DOI: https://doi.org/10.1007/978-981-13-2721-6_7

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  • Online ISBN: 978-981-13-2721-6

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