EdgeKV: Sınır bilişim için dağıtık, ölçeklenir ve tutarlı veri depolama sistemi

Edge computing moves the computation closer to the data and the data closer to the user to overcome the high latency communication of cloud computing. Storage at the edge allows data access with high speeds that enable latency-sensitive appli- cations in areas such as autonomous driving, machine vision, smart grid, and virtual reality. However, several distributed services are typically designed for the cloud. In addition, building an efficient edge-enabled storage system is challenging because of the distributed and heterogeneous nature of the edge and its limited resources. In this thesis, we propose EdgeKV, a decentralized storage system designed for the network edge. EdgeKV does not require any central management and can scale with the edge infrastructure resources and the number of users. Besides, EdgeKV offers fast and reliable storage against failures, utilizing data replication while provid- ing strong consistency guarantees. With a location-transparent and interface-based design, EdgeKV can scale with a heterogeneous system of edge nodes and varying resource capabilities. Combining Replicated State Machines (RSM) that provide consistent fault-tolerant storage with a Distributed Hash Table (DHT) that allows fair load balancing results in a geo-distributed system that can be easily configured to serve different use cases. Latency-sensitive applications typically have two types of data: local data that is relevant only to a small number of users close to each other and requires strict latency requirements, and global data that should be avail- able to all users in the system and can tolerate higher latencies. EdgeKV considers this variation in latency requirements and provides low-latency access to local data, stored in local edge groups, and availability throughout the system to global data, fairly distributed over edge groups in different locations, with acceptable performance. We implement a prototype of the EdgeKV modules in Golang and evaluate it in both the edge and cloud settings with an emulated setup on the Grid'5000 testbed. We utilize the Yahoo! Cloud Serving Benchmark (YCSB) to analyze the system's performance under realistic workloads. Our evaluation results show that EdgeKV outperforms the cloud storage setting with both local and global data access with an average write response time and throughput improvements of 26% and 19% respec- tively under the same settings. Our evaluations also show that EdgeKV can scale with the number of clients, without sacrificing performance. Finally, we discuss the energy efficiency improvement when utilizing edge resources with EdgeKV instead of a centralized cloud.