Approximation Complexity Model for Cloud-Based Database Optimization Problems

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@article{1711333,
      author = {Olushola Damilare Odejobi, Nafiu Ikeoluwa Hammed, Kabir Sholagberu Ahmed},
      title = {Approximation Complexity Model for Cloud-Based Database Optimization Problems},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {9},
      pages = {401-415},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711333.pdf},
      abstract = {Cloud-based database systems have become critical infrastructure for modern enterprises, supporting large-scale data storage, analytics, and transactional processing. However, the optimization of such databases?including query execution, resource allocation, indexing, and replication?presents significant computational challenges due to the combinatorial nature of underlying problems and the scale of cloud environments. Exact solutions are often intractable, particularly when multiple performance, cost, and reliability constraints must be satisfied simultaneously. This develops an approximation complexity model to analyze and address the computational limits of cloud database optimization problems, providing a framework for designing efficient, scalable, and near-optimal solutions. The proposed model formalizes key database optimization tasks as computational problems and characterizes their approximation hardness, identifying classes of problems where polynomial-time algorithms can guarantee provable bounds on solution quality. By integrating approximation algorithms with heuristic and metaheuristic strategies, the model enables the practical resolution of complex optimization tasks under resource and SLA constraints. The framework further incorporates multi-dimensional performance metrics, including query latency, throughput, storage efficiency, energy consumption, and fault tolerance, allowing a balanced assessment of trade-offs between computational efficiency and solution optimality. In addition, the model addresses cloud-specific considerations such as elasticity, multi-tenancy, and geographically distributed resources, highlighting the interaction between database optimization complexity and dynamic cloud infrastructure. Analytical insights derived from the approximation complexity characterization guide the design of algorithmic solutions that are both theoretically grounded and practically deployable in real-world cloud environments. The outcomes of this, provide a structured methodology for understanding the computational boundaries of cloud database optimization, informing decision-making for query scheduling, indexing, replication, and resource management. Future extensions of the model could incorporate AI-driven predictive analytics and real-time monitoring to further improve approximation strategies, enabling autonomous, adaptive, and efficient cloud database operations that meet performance, cost, and reliability objectives at scale.},
      keywords = {Approximation Complexity, Cloud-Based Databases, Database Optimization, Query Optimization, Resource Allocation, Computational Complexity, Algorithm Design, Approximation Algorithms, Heuristics, Cloud Infrastructure, Cost-Aware Optimization, Latency Reduction, Load Balancing, Elastic Scaling, Performance Tuning, Data Partitioning},
      month = {March},
  }