Separation and optimization of encryption and erasure coding in decentralized storage systems

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-06-01 Epub Date: 2025-02-04 DOI:10.1016/j.future.2025.107739
Marcell Szabó , Ákos Recse , Róbert Szabó , Dávid Balla , Markosz Maliosz
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Abstract

Entering the cloud storage market requires a high upfront investment, thus it is dominated by a few players with existing capacity. Decentralized cloud storage solutions can disrupt the status quo by allowing businesses and individuals to sell their unused storage capacity, reducing the need for large upfront investments in service infrastructure. We show that network operators providing such service can significantly decrease the traffic volume carried on the transport network, which is essential when serving mobile users, while maintaining high data security by implementing our proposed solution, of leveraging controlled replication inside the core network. Upon data uploads encryption and erasure encoding are separated, with the latter moved inside the network, enabling the arbitrary replication of storable data pieces without straining the access network. We present simulation results, showing that the proposed method reduces traffic by 20% compared to the out-of-the-box solution. Moreover, we elaborate on optimal multi-proxy placements and even optimal storage node choosings in complex ISP networks, where deep data penetration is desired, by giving ILP optimization methods and results, achieving minimal overall network load and maximum data security.

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分散存储系统中加密与擦除编码的分离与优化
进入云存储市场需要很高的前期投资,因此它被少数拥有现有容量的参与者所主导。分散的云存储解决方案可以通过允许企业和个人出售其未使用的存储容量来打破现状,从而减少对服务基础设施的大量前期投资的需求。我们表明,提供此类服务的网络运营商可以显著减少传输网络上的流量,这在为移动用户提供服务时至关重要,同时通过实施我们提出的解决方案,利用核心网络内的受控复制,保持高数据安全性。在数据上传时,加密和擦除编码分离,擦除编码在网络内移动,从而可以任意复制可存储的数据块,而不会使接入网络紧张。我们给出的仿真结果表明,与开箱即用的解决方案相比,所提出的方法减少了20%的流量。此外,我们通过给出ILP优化方法和结果,详细阐述了复杂ISP网络中最优的多代理放置甚至最优存储节点选择,其中需要深度数据渗透,从而实现最小的整体网络负载和最大的数据安全性。
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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