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·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
• Information Sciences
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Issues up to 2022 co-published with and available at:
Developing a dynamic data partitioning model for multi-tenant database environments in cloud platforms
Dinesh Shravan Datardinesh.datar@vit.eduDepartment of Artificial Intelligence & Data Science Vishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile →
, *Amit GauravCorresponding authorcoe@niu.edu.inSchool of Engineering & Technology Noida International UniversityDepartment of School of Engineering & Technology Noida International UniversityGreater Noida, Uttar Pradesh, 203201, IndiaView full profile →
, R. ArivukkodiR.Arivukkodi@gmail.comDepartment of Computer Science Meenakshi College of Arts and Science Meenakshi Academy of Higher Education and ResearchChennai, Tamil Nadu, 600078, IndiaView full profile →
, Pradnya Borkarpradnyaborkar2@gmail.comDepartment of Computer Science and Engineering Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University)Department of Computer Science and Engineering Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University)Pune, Maharashtra, 440027, IndiaView full profile →
, Aparna Shrinivas Shirkandekaleaparna5@gmail.comDepartment of Electronics and Telecommunication Engineering S. B. Patil College of Engineering Indapur Affiliated to Savitribai Phule Pune University (SPPU) PunePune, Maharashtra, 413106, IndiaView full profile →
* Corresponding author · click or hover a name for details
The growing adoption of cloud computing and Software-as-a-Service (SaaS) platforms has intensified the demand for scalable and performance-isolated multi-tenant database (MTDB) architectures. Traditional partitioning schemes, including hash, range, and hybrid methods, remain static and fail to address the challenges of heterogeneous workloads, workload skew, and elastic scalability. This paper proposes DynaPartMT, a dynamic, workload-aware data partitioning model for MTDBs in cloud platforms. The framework integrates continuous workload monitoring, lightweight forecasting, and a multi-objective cost-aware partitioning planner that jointly minimizes cross-partition transactions, balances load, enforces tenant-level isolation, and bounds migration overhead. The model is mathematically formalized, with optimization objectives and constraints explicitly defined to guarantee scalability and SLA compliance.
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