Algorithmic Aspects of Distributed Hash Tables on Cloud, Fog, and Edge Computing Applications: A Survey

In the current era, where data is expanding due to the unforeseen volume, velocity, and variety of data types produced by IoT devices, there is an imperative need to manage such data in remote IoT environments. However, these complexities have been inadequately addressed by conventional data management methods. In such scenarios, Distributed Hash Tables (DHTs) have emerged as an effective solution for efficient data storage and retrieval. Conversely, the dynamizature of IoT data presents its own set of challenges, such as decreased performance, inconsistent data, and increased overhead. To improve the performance of DHTs, we examine their algorithmic properties in cloud, fog, and edge computing environments, taking into account network designs, resource availability, latency requirements, and data proximity. This survey explores the adaptation of algorithmic elements in DHTs for optimal data administration in these cloud computing environments. Moreover, we examine advanced techniques such as effective hashing, adaptive routing, defect tolerance mechanisms, and load balancing. In addition, we address the challenges of managing vast and diverse volumes of IoT data, taking into account the unique features and constraints of cloud, fog, and edge environments. We also conduct contemporary research on security and privacy, focusing on algorithmic and architectural solutions for data integrity, confidentiality, and availability. This work enhances our comprehension of dynamic DHT algorithms and their potential for effective data management across multiple computing paradigms by investigating state-of-the-art research.