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A benchmark study of modern distributed databases is an important source of information to select the right technology for managing data in the cloud-edge paradigms. To make the right decision, it is required to conduct an extensive experimental study on a variety of hardware infrastructures. While most of the state-of-the-art studies have investigated only response time and scalability of distributed databases, focusing on other various metrics (e.g., energy, bandwidth, and storage consumption) is essential to fully understand the resources consumption of the distributed databases. Also, existing studies have explored the response time and scalability of these databases either in private or public cloud. Hence, there is a paucity of investigation into the evaluation of these databases deployed in a hybrid cloud, which is the seamless integration of public and private cloud. To address these research gaps, in this paper, we investigate energy, bandwidth and storage consumption of the most used and common distributed databases. For this purpose, we have evaluated four open-source databases (Cassandra, Mongo, Redis and MySQL) on the hybrid cloud spanning over local OpenStack and Microsoft Azure, and a variety of edge computing nodes including Raspberry Pi, a cluster of Raspberry Pi, and low and high power servers. Our extensive experimental results reveal several helpful insights for the deployment selection of modern distributed databases in edge-cloud environments.
In this paper, we propose the DN-tree that is a data structure to build lossy summaries of the frequent data access patterns of the queries in a distributed graph data management system. These compact representations allow us an efficient communicati
Todays storage systems expose abstractions which are either too low-level (e.g., key-value store, raw-block store) that they require developers to re-invent the wheels, or too high-level (e.g., relational databases, Git) that they lack generality to
Bandwidth slicing is introduced to support federated learning in edge computing to assure low communication delay for training traffic. Results reveal that bandwidth slicing significantly improves training efficiency while achieving good learning accuracy.
Blockchain has come a long way: a system that was initially proposed specifically for cryptocurrencies is now being adapted and adopted as a general-purpose transactional system. As blockchain evolves into another data management system, the natural
We study here fundamental issues involved in top-k query evaluation in probabilistic databases. We consider simple probabilistic databases in which probabilities are associated with individual tuples, and general probabilistic databases in which, add