Information interaction and partial growth-based multi-population growable genetic algorithm for multi-dimensional resources utilization optimization of cloud computing

Optimizing multi-dimensional resource utilization is a critical research area in distributed computing, particularly in cloud computing, where various heterogeneous resources are integrated to offer a wide range of services. Addressing this issue necessitates the simultaneous consideration of multiple resource bottlenecks. This paper presents a new solution, called the Multi-Population Growth Genetic Algorithm (MPGGA), which consists of a central management unit responsible for executing information interaction and growth quota reallocation, and multiple population evolution executors to perform crossover and regeneration within each population. The proposed MPGGA combines elite sharing and priority support for the weaker population (ESPW), resulting in better convergence and optimality than other combinations of strategies. This outcome is corroborated by extensive ablation experiments on various strategies. Furthermore, the experimental results for minimizing the maximum utilization of resources in each dimension indicate that MPGGA-ESPW outperforms other popular algorithms, such as GHW-NSGA II (1.363x), GHW-MOEA/D (1.339x), NSGA II (1.948x), and MOEA/D (2.151x) in terms of convergence speed. For energy consumption-related optimization problems, the experimental results demonstrate that the adaptability of a single algorithm in MPGGA family is limited by the algorithm of growth route, while also showing that the MPGGA framework is flexible to allow various algorithms as its growth route to adapt to various scenarios.