In the mobile edge computing (MEC) environment, edge servers with storage and computing resources are deployed at base stations within users’ geographic proximity to extend the capabilities of cloud computing to the network edge. Edge storage system (ESS), is comprised by connected edge servers in a specific area, which ensures low-latency services for users. However, high data storage overheads incurred by edge servers’ limited storage capacities is a key challenge in ensuring the performance of applications deployed on an ESS. Data deduplication, as a classic data reduction technology, has been widely applied in cloud storage systems. It also offers a promising solution to reducing data redundancy in ESSs. However, the unique characteristics of MEC, such as edge servers’ geographic distribution and coverage, render cloud data deduplication mechanisms obsolete. In addition, data distribution must be balanced over edge storage systems to accommodate future data demands, which cannot be undermined by data deduplication. Thus, balanced edge data deduplication (BEDD) must consider deduplication ratio, data storage benefits, and resource balance systematically under the latency constraint. In this article, we model the novel BEDD problem formally and prove its $\mathcal {NP}$ -hardness. Then, we propose an optimal approach for solving the BEDD problem exactly in small-scale scenarios and a sub-optimal approach to solve large-scale BEDD problems with a theoretical performance guarantee. Extensive and comprehensive experiments conducted on a real-world dataset demonstrate the significant performance improvements of our approaches against four representative approaches.

中文翻译：

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在移动边缘计算中启用平衡的重​​复数据删除

在移动边缘计算（MEC）环境中，具有存储和计算资源的边缘服务器部署在用户地理邻近范围内的基站，以将云计算的能力扩展到网络边缘。边缘存储系统(ESS)，由特定区域连接的边缘服务器组成，确保为用户提供低延迟服务。然而，边缘服务器有限的存储容量导致的高数据存储开销是确保部署在 ESS 上的应用程序性能的关键挑战。重复数据删除作为一种经典的数据缩减技术，在云存储系统中得到了广泛的应用。它还为减少 ESS 中的数据冗余提供了一个有前途的解决方案。然而，MEC 的独特特性，如边缘服务器的地理分布和覆盖范围，使得云数据重复删除机制已经过时。此外，数据分布必须在边缘存储系统上保持平衡，以适应未来的数据需求，而这种需求不能被重复数据删除所破坏。因此，平衡边缘数据去重(BEDD) 必须在延迟约束下系统地考虑重复数据删除率、数据存储优势和资源平衡。在这篇文章中，我们对新的 BEDD 问题进行了形式化建模并证明了它$\数学{NP}$ -硬度。然后，我们提出了一种在小规模场景中精确解决 BEDD 问题的最优方法，以及一种在理论性能保证下解决大规模 BEDD 问题的次优方法。在真实世界的数据集上进行的广泛而全面的实验证明了我们的方法相对于四种代表性方法的显着性能改进。