Optimizing Distributed Query Processing in Heterogeneous Multi-Cloud Environments: A Framework for Dynamic Data Sharding and Fault-Tolerant Replication

Basically, in a data-driven period as the one we are in, organizations seek distributed query processing as a way of recovering relevant information from unwieldy amounts of data sprawled across several types of multi-clouds.But it is not easy to do so because one has to ensure that the queries have been optimized and right data is available in these kinds of settings.Heterogeneities in cloud providers, storage systems, and computing capabilities make the management of data complex, while on the other hand, risks associated with the failures of nodes or clouds impact data consistency and availability.It is with this background that this paper proposes a novel framework for distributed query processing in heterogeneous multi-clouds to deal with problems and challenges.This means our framework devised a versatile method whereby dynamic sharding guarantees the partitioning and dissemination of data across multiple clouds to attain optimal query performance and scalability.