dragonfly algorithm

Nowadays, reducing energy consumption in cloud computing is of great interest due to the high operational costs and its impact on climate change. The consolidation solution is an effective method for minimizing the number of physical machines (PMs) and reducing energy consumption. The virtual machine consolidation process encounters the challenge of reducing energy consumption while effectively managing resource allocation. The aim of this paper is to address these challenges through the classification of PMs and the use of the dragonfly algorithm. The quartile parameter is utilized to classify PMs into three categories: underloaded, medium load, and overloaded. First, we identified the overloaded PMs in the overloaded category. Then, we presented a solution to select virtual machines from an overloaded PM based on resource usage. Additionally, the Dragonfly algorithm is utilized to select destinations for hosting migrant virtual machines in the medium load category. Furthermore, we identified underloaded PMs in the underloaded categories using this algorithm. The proposed solution is evaluated using the CloudSim toolkit and tested with workloads consisting of over a thousand data points from virtual machines based on PlanetLab data. The results from the simulation experiments indicate that the proposed solution, while avoiding SLA violations and minimizing additional migrations, has significantly reduced energy consumption.

The progression towards smart grids, integrating renewable energy resources, has increased the integration of distributed generators (DGs) into power distribution networks. However, several economic and technical challenges can result from the unsuitable incorporation of DGs in existing distribution networks. Therefore, optimal placement and sizing of DGs are of paramount importance to improve the performance of distribution systems in terms of power loss reduction, voltage profile, and voltage stability enhancement. This paper proposes a methodology based on Dragonfly Optimization Algorithm (DA) for optimal allocation and sizing of DG units in distribution networks to minimize power losses considering variations of load demand profile. Load variations are represented as lower and upper bounds around base levels. Efficiency of the proposed method is demonstrated on IEEE 33-bus and IEEE 69-bus radial distribution test networks. The results show the performance of this method over other existing methods in the literature.</span></span></span></div>