A multi-objective optimization model for virtual machine mapping in cloud data centres

Abstract

Modern cloud computing environments exploit virtualization for efficient resource management in order to reduce computational cost and energy budget. Virtual machine (VM) migration is a technique that enables flexible resource allocation and increases the computation power and communication capability within cloud data centers. VM migration helps successfully cloud providers to achieve various resource management objectives such as load balancing, power management, fault tolerance, and system maintenance. However, the process of VM migration can affect applications performance unless attended by smart optimization methods. This paper presents a multi-objective optimization model for this matter. The objectives are minimizing power consumption, maximizing resource utilization, and minimizing VM transfer time. The fuzzy particle swarm optimization (PSO), which improves the efficiency of convectional PSO by using fuzzy logic systems, is relied upon to solve the optimization problem. The model is implemented in a cloud simulator to investigate the performance. The results verify the performance of the proposed model.

LBPSGORA: Create Load Balancing with Particle Swarm Genetic Optimization Algorithm to Improve Resource Allocation and Energy Consumption in Clouds Networks

“…e issue of proper placement of virtual machines in physical machines has a great impact on optimizing energy consumption. In [30], the ant colony system has been used to place virtual machines. In this research, the dynamic placement system of virtual machines has been considered.…”

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

“…One more power-aware PSO design [37] took as an input this linear power model, dimension, and the current utilization of PMs in order to get out an efficient VM provisioning in cloud environment. However, to tune vagueness of the inertia weight of the PSO, a multiobjective smart fuzzy particle swarm optimization [38] was supported to minimize power consumption, idle resources (CPU, memory) and VM transfer time during the load balancing issue. So, after creating an initial population array of every particle with random positions and velocities in the search space, vector position value was converted from continuous value to discrete one to better determine the optimal pattern for mapping.…”

Analisis Prediksi Problem Virtual Machine Server Menggunakan Metode Fuzzy Logic

“…Logika Fuzzy Mamdani memiliki beberapa kelebihan yaitu, lebih intuitif, diterima oleh banyak pihak. Penggunaan Fuzzy Mamdani sama seperti dengan penggunaan metode peramalan pada bidang statistik [15] [4] 2017 Mengatasi masalah skalabilitas dengan mengelompokkan VM dengan perilaku penggunaan sumber daya yang serupa [5] 2017 Metode prediksi beban kerja mesin virtual fuzzy untuk lingkungan cloud [6] 2017 Analisis getaran berdasarkan LabVIEW untuk klasifikasi kesalahan mesin menggunakan algoritma fuzzy logic [7] 2016 Alokasi sumber daya yang dinamis untuk VM denga fuzzy [8] 2019 Menyelesaikan permasalahan berupa banyaknya jenis VM dan permintaan besar dari client, sehingga sangat penting untuk diprediksi agar alokasi dan waktu penyebaran dapat secara efektif dikurangi [9] 2019 Mendukung manajemen memori VM secara efisien. [10] 2018 masalah mendesain sebuah pengamat yang kuat dan pengontrol umpan balik output adaptif fuzzy Pada study literatur diatas bisa dilihat bahwa hasil penelitian masih belum ada yang menyelesaikan problem VM menggunakan fuzzy logic dan begitu juga fuzzy logic untuk menyelesaikan masalah VM.…”