# A Parallel Particle Swarm Optimisation for Selecting Optimal Virtual Machine on Cloud Environment
###### Abstract  
Cloud computing has a significant role in healthcare services, especially in medical applications. In cloud computing, the best choice of virtual machines (Virtual_Ms) has an essential role in the quality improvement of cloud computing by minimising the execution time of medical queries from stakeholders and maximising utilisation of medicinal resources. Besides, the best choice of Virtual_Ms assists the stakeholders to reduce the total execution time of medical requests through turnaround time and maximise CPU utilisation and waiting time. For that, this paper introduces an optimisation model for medical applications using two distinct intelligent algorithms: genetic algorithm (GA) and parallel particle swarm optimisation (PPSO). In addition, a set of experiments was conducted to provide a competitive study between those two algorithms regarding the execution time, the data processing speed, and the system efficiency. The PPSO algorithm was implemented using the MATLAB tool. The results showed that the PPSO algorithm gives accurate outcomes better than the GA in terms of the execution time of medical queries and efficiency by 3.02% and 37.7%, respectively. Also, the PPSO algorithm has been implemented on the CloudSim package. The results displayed that the PPSO algorithm gives accurate outcomes better than default CloudSim in terms of final implementation time of medicinal queries by 33.3%. Finally, the proposed model outperformed the state-of-the-art methods in the literature review by a range from 13% to 67%. eurmurmed digital applications, machine learning,  
dental and complementary health facilities, and nursing [1,2]. From the technical point of view, the cloud is composed of datacentres, hosts, Virtual_Ms, resources, and storage that stakeholder's access to predict or diagnose diseases [8,9]. Datacentres provide the housing for servers. Hosts are servers that host several Virtual_Ms that process and store medical resources and allow stakeholders to retrieve those resources. Cloud computing uses virtualisation to share the resources of a host between various stakeholders and organisations [10]. Virtualisation is divided into hardware, operating system (OS), and storage virtualisation. Hardware virtualisation allows multiple users to share the resources of a host [9,11]. Operating system virtualisation provides container isolation for running different applications on a single computer and storage virtualisation abstracts physical storage, allowing multiple aggregate devices into single storage [12,13]. Our primary objective is to find the best choice of Virtual_Ms to minimise the total execution time of medical requests on the cloud environment and maximise the utilisation of resources. Some of the swarm intelligence approaches used for optimal Virtual_Ms selection are bee colony optimisation (BCO), ant colony optimisation (ANT), bat optimisation [14, 15, 16], and particle swarm optimisation (PSO). Still, most of these approaches cannot use parallel computing. Bee colony optimisation (BCO) is a population-based algorithm which is inspired by the smart behaviour of honeybees but has two main disadvantages: (1) it has slow convergence and (2) many control parameters. ANT is used to identify the optimal solution from a set of solutions, based on the behaviour of ants probing food. Still, it is not appropriate to do a parallel search in a population. Particle swarm optimisation is used to find the best solution from a set of solutions by using particles to simulate population and swarm to reach a solution. Still, it also does not use parallel computation, and it cannot handle the problem of scattering. However, parallel particle swarm optimisation (PPSO) currently is used selecting Virtual_Ms for medical processing requests, which come from stakeholders and overcome the drawbacks of the existing system [9]. PPSO divides the population into sub-populations and applies the algorithm separately to these sub-populations to minimise execution time [2,17].