Text Semantics-Driven Data Classification Storage Optimization
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Published:2024-01-30
Issue:3
Volume:14
Page:1159
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Yuan Zhu12ORCID, Lv Xueqiang12, Gong Yunchao12, Liu Boshan2, Yang Haixiang3, You Xindong2ORCID
Affiliation:
1. Computer College, Qinghai Normal University, Xining 810008, China 2. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China 3. Big Data Center of the Ministry of Public Security, Beijing 100070, China
Abstract
Data classification storage has emerged as an effective strategy, harnessing the diverse performance attributes of storage devices to orchestrate a harmonious equilibrium between energy consumption, cost considerations, and user accessibility. The traditional strategy of solely relying on access frequency for data classification is no longer suitable for today’s complex storage environment. Diverging from conventional methods, we explore from the perspective of text semantics to address this issue and propose an effective data classification storage method using text semantic similarity to extract seasonal features. First, we adopt a dual-layer strategy based on semantic similarity to extract seasonal features. Second, we put forward a cost-effective data classification storage framework based on text seasonal features. We compare our work with the data classification approach AS-H, which runs at full high performance. In addition, we also compare it with K-ear, which adopts K-means as the classification algorithm. The experimental results show that compared with AS-H and K-ear, our method reduces energy consumption by 9.51–13.35% and operating costs by 13.20–22.17%.
Funder
National Natural Science Foundation of China Natural Science Foundation of Beijing Application Platform of Graph Neural Network and Data Mining Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference32 articles.
1. Dobre, D., Viotti, P., and Vukolić, M. (2014, January 3–5). Hybris: Robust hybrid cloud storage. Proceedings of the ACM Symposium on Cloud Computing, Seattle, WA, USA. 2. Hybrid storage systems: A survey of architectures and algorithms;Niu;IEEE Access,2018 3. Exploration and Exploitation for Buffer-Controlled HDD-Writes for SSD-HDD Hybrid Storage Server;Wang;ACM Trans. Storage (TOS),2022 4. Yuan, Z., Lv, X., Xie, P., Ge, H., and You, X. (2022). CSEA: A Fine-Grained Framework of Climate-Season-Based Energy-Aware in Cloud Storage Systems. Comput. J. 5. Singh, G., Nadig, R., Park, J., Bera, R., Hajinazar, N., Novo, D., Gómez-Luna, J., Stuijk, S., Corporaal, H., and Mutlu, O. (2022, January 18–22). Sibyl: Adaptive and extensible data placement in hybrid storage systems using online reinforcement learning. Proceedings of the 49th Annual International Symposium on Computer Architecture, New York, NY, USA.
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