Constrained Quantum Optimization for Resource Distribution Management

The cloud computing field suffers from the heavy processing caused by the exponentially increasing data traffic. Therefore, optimizing the network performance and achieving a better quality of service (QoS) became a central goal. In cloud computing, the problem of energy consumption of resource distribution management system (RDMS) is presented as an optimization problem. Most of the existing classical optimization approaches, such as heuristic and metaheuristic have high computational complexity. In this work, we proposed a quantum optimization strategy that executes the tasks exponentially faster and with high accuracy named constrained quantum optimization algorithm (CQOA). We exploit the CQOA in RDMS as a toy example for pointing out the efficiency of the proposed quantum strategy in reducing energy consumption and computational complexity. Following that, we investigate the CQOA's implementation, setup, and computational complexity. Finally, we create a simulation environment to evaluate the efficiency of the suggested implemented constrained quantum strategy.

@article{Gaily2021,
  title     = {Constrained Quantum Optimization for Resource Distribution Management},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {8},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Sara El Gaily and Sándor Imre},
  doi       = {10.14569/IJACSA.2021.0120806},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120806}
}