A parallel particle swarm optimisation for selecting optimal virtual machine on cloud environment

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%.

[1]  Jing Zhang,et al.  MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement , 2015, Int. J. Distributed Sens. Networks.

[2]  Alcides Calsavara,et al.  Solving the Virtual Machine Placement Problem as a Multiple Multidimensional Knapsack Problem , 2014 .

[3]  Chitra Arjun,et al.  Diagnosis of Diabetes Using Support Vector Machine and Ensemble Learning Approach , 2015 .

[4]  Alaa Mohamed Riad,et al.  A machine learning model for improving healthcare services on cloud computing environment , 2018 .

[5]  Amir Masoud Rahmani,et al.  Dynamic VMs placement for energy efficiency by PSO in cloud computing , 2016, J. Exp. Theor. Artif. Intell..

[6]  Mohamed Elhoseny,et al.  Intelligent Algorithms for Optimal Selection of Virtual Machine in Cloud Environment, Towards Enhance Healthcare Services , 2017, AISI.

[7]  M. Vidhya,et al.  Parallel Particle Swarm Optimization for Reducing Data Redundancy in Heterogeneous Cloud Storage , 2015 .

[8]  Seyed Taghi Akhavan Niaki,et al.  Optimizing a hybrid vendor-managed inventory and transportation problem with fuzzy demand: An improved particle swarm optimization algorithm , 2014, Inf. Sci..

[9]  M. Hemalatha,et al.  CLUSTER BASED BEE ALGORITHM FOR VIRTUAL MACHINE PLACEMENT IN CLOUD DATA CENTRE , 2013 .

[10]  Biju Issac,et al.  Energy-efficient virtual machine placement using enhanced firefly algorithm , 2016, Multiagent Grid Syst..

[11]  Aditi Gavhane,et al.  Prediction of Heart Disease Using Machine Learning , 2018, 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA).

[12]  Taskeen Zaidi,et al.  Virtual Machine Allocation Policy in Cloud Computing Environment using CloudSim , 2018 .

[13]  Jun Zhang,et al.  An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing , 2018, IEEE Transactions on Evolutionary Computation.

[14]  Monjur Ahmed,et al.  Cloud Computing and Security Issues in the Cloud , 2014, Trinity Journal of Management, IT & Media.

[15]  Shailesh Sawant,et al.  A Genetic Algorithm Scheduling Approach for Virtual Machine Resources in a Cloud Computing Environment , 2011 .

[16]  Alaaeldin M. Hafez,et al.  Task Scheduling in Cloud Computing using Lion Optimization Algorithm , 2017 .

[17]  Mounir Ben Ayed,et al.  An enhanced healthcare system in mobile cloud computing environment , 2016, Vietnam Journal of Computer Science.

[18]  Meryeme Alouane,et al.  Virtualization in Cloud Computing: Existing solutions and new approach , 2016, 2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech).

[19]  Amir Hossein Gandomi,et al.  Bat algorithm for constrained optimization tasks , 2012, Neural Computing and Applications.

[20]  Rutvik Mehta,et al.  An Approach for VM Allocation in Cloud Environment , 2015 .

[21]  Xiao-Dong Fu,et al.  A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform , 2014, TheScientificWorldJournal.

[22]  Amir Hossein Gandomi,et al.  Chaotic bat algorithm , 2014, J. Comput. Sci..

[23]  Samir Tata,et al.  Optimal Virtual Machine Placement in Large-Scale Cloud Systems , 2014, 2014 IEEE 7th International Conference on Cloud Computing.

[24]  M. Varun Kumar,et al.  Data Analysis and Prediction of Hepatitis Using Support Vector Machine ( SVM ) , 2014 .

[25]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[26]  Gaochao Xu,et al.  A Heuristic Placement Selection of Live Virtual Machine Migration for Energy-Saving in Cloud Computing Environment , 2014, PloS one.

[27]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[28]  Amir H. Gandomi,et al.  Optimal virtual machine selection for anomaly detection using a swarm intelligence approach , 2019, Appl. Soft Comput..

[29]  S. D. Madhu Kumar,et al.  Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm , 2017 .

[30]  Rajalakshmi Shenbaga Moorthy,et al.  An Effective Mechanism for Virtual Machine Placement using Aco in IAAS Cloud , 2017 .

[31]  Chen Zhou,et al.  Virtual machine selection and placement for dynamic consolidation in Cloud computing environment , 2015, Frontiers of Computer Science.

[32]  Cristian Mateos,et al.  Dynamic Scheduling based on Particle Swarm Optimization for Cloud-based Scientific Experiments , 2014, CLEI Electron. J..

[33]  Saeed Sharifian,et al.  Dynamic prediction scheduling for virtual machine placement via ant colony optimization , 2015, 2015 Signal Processing and Intelligent Systems Conference (SPIS).