Mathematical Optimization Techniques for AI-Enabled Resource Allocation in Cloud Platforms
DOI:
https://doi.org/10.64137/3108-2637/IJMAR-V2I3P101Keywords:
Mathematical Optimization, Artificial Intelligence, Cloud Computing, Resource Allocation, AI-Enabled Resource Management, Linear Programming, Multi-Objective Optimization, Workload Prediction, Resource Utilization, Cloud Platforms, Energy Efficiency, Quality of ServiceAbstract
The explosive growth of cloud computing and artificial intelligence (AI) has driven demands for effective resource allocation mechanisms that can adapt to dynamic workloads, diverse infrastructure, and changing user patterns. Traditional resource allocation methods usually have difficulty in precisely utilizing computing resources while preserving quality service, minimizing operational expenses and following strict specification limits. To solve these problems in a systematic manner, we formulate resource allocation as an optimization problem where resource usage can be optimally managed while minimizing objectives such as the wastage of said resources or energy consumption, which impacts swiftness during computation (latency), and operational cost, hence maximizing performance and utilization against certain constraints using mathematical formulation techniques. In this research, we investigate the use of mathematical optimization methods in AI-based cloud resource allocation. AI models can enable workload predictions, and techniques such as linear programming, integer-based solutions (e.g., mixed-integer nonlinear optimization), convex-based non-linearoptimization problems or algorithm approaches, but also multi-objective optimizations to help guide decision-making or heuristic approaches. Prediction mechanisms powered by AI can help anticipate demand for resources over time, and optimization algorithms tell the system how to technically allocate virtual machines, containers, storage or network. With the synergy of these approaches, cloud platforms can now dynamically adapt their resource allocation to volatile workloads. The new framework lets cloud platforms intelligently and mathematically optimize their resource management, leading to a more efficient, scalable, reliable and sustainable system. Together, predictive AI capabilities with optimization-based decision-making can help the cloud providers achieve better workload balancing, leading to low energy consumption, lower operational costs and improved quality of service. The approach also offers a basis for building adaptive cloud infrastructures that can adjust based on real-time resource needs, making mathematical optimization an essential part of future AI-driven cloud computing systems.
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