Quantum Optimization for Intelligent User Data Allocation in Distributed Cloud Infrastructure
DOI:
https://doi.org/10.56294/dm20251328Keywords:
Quantum optimization, Data placement, Distributed cloud computing, Latency reduction, Quantum-inspired algorithmsAbstract
In a modern environment in which cloud computing is distributed globally, optimizing the placements of user data in the different cloud locations would ensure that the data can be accessed with minimum latency and fast access. Traditional heuristic and machine-learning methods are often prohibitively expensive to scale and have a hard time adjusting to a dynamic cloud environment. Scalable and adaptive optimization strategies are required when user demands and data volumes grow very fast. There is a fair likelihood that quantum-based methods, particularly, the metaheuristic methods, are an alternative that can effectively react to dynamic conditions. The proposed model will be based on Quantum Particle Swarm Optimization when moving user data to the most suitable places within distributed cloud canters. Through quantum-inspired probabilistic search, the algorithm becomes more adaptive and more efficient than traditional ones. Experiments, based on these simulations of the user request in the case of a geo-distributed cloud, have shown a significant reduction in latency up to 28% and better load balancing compared to the traditional approaches. Altogether, these results highlight the prospects of quantum computing when it comes to improving the efficiency and responsiveness of cloud infrastructure. The primary strength of the QPSO is that it can be easily modified to facilitate the rapid response to the rapidly changing environment to allow access to the distributed cloud systems in an efficient way and with a low latency.
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Copyright (c) 2025 Viji C, Rajkumar N, Stephen R, Gobinath R, Balusamy Nachiappan, Prabhu Shankar B (Author)
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