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In recent years, cloud computing research, specifically data replication techniques and their applications, has been growing. If the replicas number is raised and put in multiple positions, it will be expensive to maintain the data usability, performance and stability of the application systems. In this paper, two bio-inspired algorithms were proposed to improve both selection and placement of data replicas in the cloud environment. The suggested algorithms for dynamic data replication are multi-objective particle swarm optimization (MO-PSO) and ant colony optimization (MO-ACO). The first suggested algorithm, i.e., MO-PSO, is employed to obtain the best selected data replica depend on the most frequent one. However, the second suggested algorithm, i.e., MO-ACO, is employed to obtain the best data replica placement depend on the shortest distance, and the replicas availability. A simulation of the suggested strategy was carried out using CloudSim. Each data center (DC) includes hosts with set of virtual machines (VMs). The data replication order is determined at random from a thousand cloudlets. All replication files are randomly distributed in the proposed architecture. The performance of suggested techniques was evaluated against several approaches including: Adaptive Replica Dynamic Strategy (ARDS), Enhance Fast Spread (EFS), Genetic Algorithm (GA), Replica Selection and Placement (RSP), Popular File Replication First (PFRF), and Dynamic Cost-aware Re-replication and Re-balancing Strategy (DCR2S). The simulation results prove that MOPSO gives improved data replication compared against other algorithms. Additionally, MOACO realizes higher data availability, lower cost, and less bandwidth consumption compared with other algorithms.

Reconstruction of the lower extremity is considered a major challenge due to frequent bone exposure and the absence of local tissue redundancy, as well as often due to the presence of vascular insufficiency. Many surgeons have preferred free flaps especially for reconstructing the more distal lower limb defects until the evolution of pedicled perforator flaps and propeller flaps in particular provided a like-with-like reconstruction of the lower extremity without affecting the main vessels of the limb or the underlying muscles, and without the risk of any microanastomosis especially in patients with multiple comorbidities. Perforator-pedicled propeller flaps as local flaps in the lower extremity are best suited for small- and medium-sized defects with minimal donor-site morbidity, regardless of the cause of the defect. Any of the three source vessels of the leg can provide reliable perforators for propeller flap coverage of the distal leg and foot. The three main risk factors that are relative contraindications may be advanced age, diabetes mellitus, and atherosclerotic peripheral vascular disease.

In recent years, there has been increasing interest in cloud computing research, especially replication strategies and their applications. When the number of replicas is increased and placed in different places, maintaining the system's data availability, performance and reliability will increase the cost. In this paper, two multi-objectives swarm intelligence algorithms are used to optimize the data replication selection and placement in a cloud environment. These algorithms are namely, multi-objective particle swarm optimization (MOPSO) and multi-objective ant colony optimization (MOACO). The first algorithm, (MOPSO), is used to find the best selected data replica according to the most popular data replication strategy. The improved time-based decay function (ITBDF), is used to enhance the proposed model. The second algorithm, (MOACO), is used to find the best data replica placement according to the minimum distance, the number of data transmissions and the availability of data replication. A simulation of the suggested strategy has been performed using CloudSim. the Cloud is formed to simulate different kinds of datacenters (DCs) with different structures. Moreover, 21 DCs are used. Each DC consists of a host that contains a set of virtual machines (VMs) that provides blocks of available data replications. Three different data placements for high datacenters were created. A total of one thousand cloudlets are randomly confirmed for the data replication order. All replication files are placed in high datacenters and randomly distributed in the suggested system. The performance of proposed strategy was evaluated relative to many well-known strategies such as, Enhance Fast Spread (EFS), Dynamic Cost-aware Re-replication and Re-balancing Strategy (DCR2S), Genetic Algorithm (GA), Genetic adaptive Selection Algorithm (GASA), Replica Selection and Placement (RSP), Dynamic Replica Selection Ant Colony Optimization (DRSACO), Adaptive Replica Dynamic Strategy (ARDS), Popular File Replication First (PFRF). The experimental results show that MOPSO, achieves better data replication than compared algorithms. Additionally, MOACO, achieves higher data availability, lower cost, and less bandwidth consumption than compared algorithms.