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Article | 09-April-2018

Multi Objective Optimization of Virtual Machine Migration Placement Based on Cloud Computing

How to improve the resource utilization of the cloud computing system has been one of the key content of the research of the cloud computing. The traditional multi-objective ant colony optimization was improved, studied the virtual machine live migration framework, combined with the elimination method to solve virtual machine migration and placement of multi-objective optimization problem, the load balanced specific strategies are integrated into the framework of a dynamic migration, simulation

Sun Hong, Tang Qing, Xu Liping, Chen Shiping

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 2, 120–129

Article | 11-June-2016

AN IMPROVED MULTI-OBJECTIVE EVOLUTIONARY OPTIMIZATION ALGORITHM FOR SUGAR CANE CRYSTALLIZATION

Yanmei Meng, Wenxing Li, Qingwei Chen, Xian Yu, Kangyuan Zheng, Guancheng Lu

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 953–978

Article | 30-November-2019

A Study of Edge Computing Offloading Based on Security

under time constraints and joint optimization to improve QoE. 3) An offloading decision solving method based on the Multi-Objective Optimization Hybrid Quantum Evolution Algorithm (MHQEA) is proposed. 4) Through simulation and comparison with conventional algorithms, it is proved that the energy saving cost proposed in this paper is lower, the total energy consumption of the system is lower, and the safety of the system is guaranteed. II. SECURITY-BASED MEC CALCULATION OFFLOADING SCHEME The

Luo Pei, Lei Juchao

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 2, 58–63

Review | 06-August-2019

COMPUTATIONAL DESIGN FOR THE PRESERVATION AND ENHANCEMENT OF HISTORICAL HERITAGE. A PREFABRICATED COVERING PROTOTYPE FOR ARCHAEOLOGICAL SITES IN RURAL CONTEXT

Bollinger + Grohmann Ingenieure, used for multi-objective optimization. –Shortest Walk, developed by Giulio Piacentino, used to find the best route to visit the archaeological site. The generative process can be divided into 3 steps: creation of the basic structure; positioning of the pillars; creation of the visit path (Fig. 5). Figure 5. Illustrations of the steps of the generative process (drawing author: F. Iardella) By loading a 2D and 3D survey of the archaeological site in the algorithm and

Marco Giorgio BEVILACQUA, Filippo IARDELLA, Marco PALAZZUOLI, Rodrigo Rubio CUADRADO

Architecture, Civil Engineering, Environment, Volume 12 , ISSUE 2, 17–26

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