Algorithmic Model for Constraint Satisfaction in Cloud Network Resource Allocation

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@article{1711334,
      author = {Kabir Sholagberu Ahmed, Olushola Damilare Odejobi, Theophilus Onyekachukwu Oshoba },
      title = {Algorithmic Model for Constraint Satisfaction in Cloud Network Resource Allocation},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {12},
      pages = {516-532},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711334.pdf},
      abstract = {The increasing complexity of cloud computing infrastructures, combined with escalating demands for efficient and reliable resource utilization, necessitates advanced algorithmic models for network resource allocation. Cloud environments face the dual challenges of dynamically allocating resources?such as compute, storage, and bandwidth?while satisfying a variety of operational, performance, and service-level constraints. This paper proposes an algorithmic model designed to optimize cloud network resource allocation through constraint satisfaction techniques, ensuring that resources are allocated efficiently without violating system-level and user-defined requirements. The model integrates formal constraint satisfaction problem (CSP) formulations with heuristic and metaheuristic algorithms, enabling scalable and adaptive resource management across heterogeneous cloud infrastructures. By defining constraints related to latency, bandwidth, energy consumption, workload dependencies, and quality-of-service (QoS) objectives, the framework ensures that allocation decisions meet both technical and business requirements. Dynamic constraint handling and priority-based scheduling further allow the model to adapt to fluctuating workloads and varying network conditions, maintaining system stability and service continuity. To enhance performance, the proposed approach leverages hybrid techniques, combining deterministic methods for constraint verification with AI-driven optimization strategies for resource selection and load balancing. Simulation results demonstrate that the model can reduce resource contention, improve utilization rates, and minimize SLA violations while maintaining low computational overhead. The approach is also capable of supporting multi-tenant cloud deployments, ensuring fairness in resource allocation and enabling efficient orchestration in distributed and federated environments. Overall, this algorithmic model provides a structured and systematic methodology for cloud network resource allocation under complex operational constraints. Its application supports enhanced performance, operational efficiency, and service reliability, making it a critical tool for cloud providers and enterprise IT teams. The study highlights the potential of constraint satisfaction and algorithmic optimization to address contemporary challenges in cloud network management, enabling sustainable, scalable, and adaptive cloud operations.},
      keywords = {Algorithmic Model, Constraint Satisfaction, Cloud Networks, Resource Allocation, Optimization, Computational Complexity, Constraint Programming, Heuristic Algorithms, Metaheuristics, Integer Linear Programming (ILP), Network Virtualization},
      month = {June},
  }