Abstract

Cloud storage adoption has increased over the years given the high demand for fast processing, low access latency, and ever-increasing amount of data being generated by, e.g., Internet of Things applications. In order to meet the users’ demands and provide a cost-effective solution, cloud service providers offer tiered storage; however, keeping the data in one tier is not cost-effective. In this respect, cloud storage tier optimization involves aligning data storage needs with the most suitable and cost-effective storage tier, thus reducing costs while ensuring data availability and meeting performance requirements. Ideally, this process considers the trade-off between performance and cost, as different storage tiers offer different levels of performance and durability. It also encompasses data lifecycle management, where data is automatically moved between tiers based on access patterns, which in turn impacts the storage cost. In this respect, this article explores two novel classification approaches, rule-based and game theory-based, to optimize cloud storage cost by reassigning data between different storage tiers. Four distinct storage tiers are considered: premium, hot, cold, and archive. The viability and potential of the proposed approaches are demonstrated by comparing cost savings and analyzing the computational cost using both fully-synthetic and semi-synthetic datasets with static and dynamic access patterns. The results indicate that the proposed approaches have the potential to significantly reduce cloud storage cost, while being computationally feasible for practical applications. Both approaches are lightweight and industry- and platform-independent.

中文翻译：

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通过存储对象分类优化云存储层

摘要

鉴于对快速处理、低访问延迟以及物联网应用等生成的数据量不断增加的高需求，云存储的采用多年来不断增加。为了满足用户的需求并提供经济高效的解决方案，云服务提供商提供分层存储；然而，将数据保留在一层中并不划算。在这方面，云存储层优化涉及将数据存储需求与最合适且最具成本效益的存储层结合起来，从而在保证数据可用性和满足性能要求的同时降低成本。理想情况下，此过程考虑性能和成本之间的权衡，因为不同的存储层提供不同级别的性能和耐用性。它还包括数据生命周期管理，数据根据访问模式在各层之间自动移动，这反过来又会影响存储成本。在这方面，本文探讨了基于规则和基于博弈论的两种新颖的分类方法，通过在不同存储层之间重新分配数据来优化云存储成本。考虑四个不同的存储层：高级、热、冷和归档。通过使用具有静态和动态访问模式的全合成和半合成数据集来比较成本节约和分析计算成本，证明了所提出方法的可行性和潜力。结果表明，所提出的方法有可能显着降低云存储成本，同时在实际应用中计算上可行。这两种方法都是轻量级的并且独立于行业和平台。