A multi-objective algorithm for virtual machine placement in cloud environments using a hybrid of particle swarm optimization and flower pollination optimization

The demand for virtual machine requests has increased recently due to the growing number of users and applications. Therefore, virtual machine placement (VMP) is now critical for the provision of efficient resource management in cloud data centers. The VMP process considers the placement of a set of virtual machines onto a set of physical machines, in accordance with a set of criteria. The optimal solution for multi-objective VMP can be determined by using a fitness function that combines the objectives. This paper proposes a novel model to enhance the performance of the VMP decision-making process. Placement decisions are made based on a fitness function that combines three criteria: placement time, power consumption, and resource wastage. The proposed model aims to satisfy minimum values for the three objectives for placement onto all available physical machines. To optimize the VMP solution, the proposed fitness function was implemented using three optimization algorithms: particle swarm optimization with Lévy flight (PSOLF), flower pollination optimization (FPO), and a proposed hybrid algorithm (HPSOLF-FPO). Each algorithm was tested experimentally. The results of the comparative study between the three algorithms show that the hybrid algorithm has the strongest performance. Moreover, the proposed algorithm was tested against the bin packing best fit strategy. The results show that the proposed algorithm outperforms the best fit strategy in total server utilization.

Multi-objective flower pollination algorithm applied to 5G vehicular networks communication

The Cellular Vehicle-to-Everything (C-V2X) technology, as a widest version of Vehicular Ad-hoc Network (VANET), aims to interconnect vehicles and any other latest technological infrastructures. In this context, the fifth generation of mobile networks (5G) based on millimeter waves (mmWave) is an excellent alternative for the implementation of vehicular networks, mainly because it is capable of providing high data rates (Gbps) and ultra-low latency, requirements of C-V2X. On the other hand, mmWave signals are highly susceptible to blocking, causing low quality of service (QoS) in VANETs, compromising network functionality and the safety of drivers and pedestrians. Thus, in this work evolutionary computing techniques are applied in the simulation of a 5G vehicular network based on millimeter waves, exploring Media Access Control (MAC) sublayer parameters to optimize packet loss, latency and throughput, in order to optimize inter-vehicular communication. The Multi-objective Flower Pollination Algorithm (MOFPA) was used for this purpose. The results obtained show that the adopted approach can reach results close to the optimal pareto of non-dominated solutions, with a 75% reduction in exploration time in relation to the exhaustive search process. Finally, the performance of the metaheuristics adopted is compared with the non-dominated genetic classification algorithm (NSGA-II) and the multi-objective differential evolutionary algorithm (MODE).