![]() Unlocking the full potential of Knowledge Graphs (KGs) to enable or enhance various semantic and other applications requires Data Management Systems (DMSs) to efficiently store and process the content of KGs. These systems demonstrate that our ideas in network-centric designs achieve orders of magnitude higher efficiency compared to the state of the art at hyperscale. Finally, it presents MimicNet, a fine-grained simulation framework that evaluates network proposals at datacenter scale with machine learning approximation. It details TELEPORT, a compute pushdown feature that eliminates data processing performance bottlenecks in disaggregated data centers, and Redy, which provides high-performance caches using remote disaggregated memory. ![]() It then introduces data processing in disaggregated data centers, a promising new cloud proposal. It first discusses GraphRex, a network-aware system that combines classic database and systems techniques to push the performance of massive graph queries in current data centers. ![]() This dissertation presents a line of systems work that covers all three directions. To transform the network to future designs, we facilitate network innovation at scale. In future networks, we propose new interfaces and services that the cloud infrastructure offers to applications and codesign data processing systems to achieve optimal query processing performance. In current networks, we redesign data processing systems with network-awareness to minimize the cost of moving data in the network. Rather than optimize these massive layers in silos, we build systems across them with principled network-centric designs. These massive scales fundamentally challenge the designs of both data processing systems and data center networks, and the classic layered designs are no longer sustainable. Enabling and expanding with these workloads are highly scalable data center networks that connect up to hundreds of thousands of networked servers. Due to unprecedented data growth and the end of Moore’s Law, these workloads have ballooned to the hyperscale level, encompassing billions to trillions of data items and hundreds to thousands of machines per query. ![]() Today’s largest data processing workloads are hosted in cloud data centers. The primary purpose of this paper is to assist businesses and individuals in understanding how cloud computing may provide them with dependable, personalized, and cost-effective services in many applications. It focuses on the different parameters to assess their performance, such as ease of software portability, transaction capabilities, and the maximum amount of data stored. It also highlights the characteristics, deployment, and service model of cloud computing and the performance and functionality of the various SQL and NoSQL cloud database applications and services required to evaluate them. This paper provides an overview of cloud computing, Cloud database architecture and kinds, and database as a service. These tools can build new business strategies and demonstrate scalability and elasticity while managing vast amounts of data with reliable, customized, and cost-effective services in various applications. Cloud databases are primarily used for data storage, retrieval, modification, and analysis by tools like business intelligence. Cloud Database focuses on new, conventional databases, specific applications for scalability, dynamic devices, and ease of use. Database performance plays an essential part in the market. Cloud intrusion has modified the comparisons in recent years. The storage costs gradually have slowed down and quickly expanded the storage capacity. The scales in databases have recently increased, and more expansion is expected in the future.
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