Cloud Database Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 2559

Special Issue Editors


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Guest Editor
1. Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea
2. Graduate School of Data Science, Seoul National University, Seoul 08826, Korea
Interests: next-generation big data AI platform; machine learning lifecycle management; data-based planning; disruptive innovation

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Guest Editor
Graduate School of Data Science, Seoul National University, Seoul 08826, Korea
Interests: databases; distributed computing; machine learning; IoT; intelligent applications

Special Issue Information

Dear Colleagues,

As most new database deployments will be on the cloud, and many traditional on-premises databases are being migrated to the cloud, the development of data systems has shifted to cloud-native databases and the best practices to address operational and integration challenges surrounding vendors’ and providers’ hosting landscapes. This trend is producing a new breed of research topics in architectural designs, development and operational intelligence, and requires compliance with global data privacy and protection standards. Cloud preferences for data management will eventually reduce the landscape’s complexity and cost of ownership/operations for cloud database users. During the transition period, the vendors and the service providers need to deal with issues in multi-cloud, multi-data models, multi-tenant environments that demand new scheme for data process, governance, and integration while maintaining low cost and economics of scale.

This Special Issue on cloud data systems invites novel research articles covering, but not limited to, technical advances in data management on the clouds, solutions to issues related to operational intelligence, application development practices, cost management, and data governance regulations.

Prof. Dr. Sang Kyun Cha
Prof. Dr. Wen-Syan Li
Guest Editors

Manuscript Submission Information

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Keywords

  • Cloud native databases
  • Architectural design for cloud native databases
  • Massive parallel could database
  • Data science exploration and deep learning
  • Interoperability with machine learning
  • Application on cloud databases
  • Hybrid deployment
  • Scalability, availability, and reliability
  • Security
  • Intelligent provisioning
  • Intelligent develop practice
  • Operational intelligence
  • Multi-tenant and multi-model support
  • Stream and event processing
  • Data warehouse and analytics on the cloud
  • Provider cost management
  • Pricing for the cloud database services
  • Economic aspect of cloud database operations
  • Legal aspect of global data standard in cloud databases
  • Impact of geopolitics to cloud database business and operations
  • Data governance and integration in the multi-cloud setting
  • Ecosystem

Published Papers (1 paper)

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Research

26 pages, 1235 KiB  
Article
Cloud Storage Service Architecture Providing the Eventually Consistent Totally Ordered Commit History of Distributed Key-Value Stores for Data Consistency Verification
by Beom-Heyn Kim and Young Yoon
Electronics 2021, 10(21), 2702; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10212702 - 05 Nov 2021
Viewed by 1933
Abstract
Cloud storage services are one of the most popular cloud computing service types these days. Various cloud storage services such as Amazon S3, DropBox, Google Drive, and Microsoft OneDrive currently support billions of users. Nevertheless, data consistency of the underlying distributed key-value store [...] Read more.
Cloud storage services are one of the most popular cloud computing service types these days. Various cloud storage services such as Amazon S3, DropBox, Google Drive, and Microsoft OneDrive currently support billions of users. Nevertheless, data consistency of the underlying distributed key-value store of cloud storage services remains a serious concern, making potential customers of cloud services hesitate to migrate their data to the cloud. Researchers have explored how to allow clients to verify the behavior of untrusted cloud storage services with respect to consistency models. However, previous proposals are limited because they rely on a strongly consistent history server to provide a totally ordered history for clients. This work presents Relief, a novel cloud storage service exposing an eventually consistent totally ordered commit history of the underlying distributed key-value store to enable client-side data consistency verification for various consistency models. By empirically evaluating our system, we demonstrate that Relief is an efficient solution to overcome the limitation of previous approaches. Full article
(This article belongs to the Special Issue Cloud Database Systems)
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