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enhanced cuckoo search algorithm for virtual machine placement in cloud data centres

INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2018年第1期9卷 1-17页

作者： Barlaskar, Esha Singh, Yumnam Jayanta Issac, Biju Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland Natl Inst Elect & Informat Technol NIELIT Sect 1 Kolkata 64 India Teesside Univ Sch Comp Media & Arts Middlesbrough Cleveland England

In order to enhance resource utilisation and power efficiency in cloud data centres it is important to perform Virtual Machine (VM) placement in an optimal manner. VM placement uses the method of mapping virtual machines to physical machines (PM). Cloud computing researchers have recently introduced various meta-heuristic algorithms for VM placement considering the optimised energy consumption. However, these algorithms do not meet the optimal energy consumption requirements. This paper proposes an enhanced cuckoo search (ECS) algorithm to address the issues with VM placement focusing on the energy consumption. The performance of the proposed algorithm is evaluated using three different workloads in CloudSim tool. The evaluation process includes comparison of the proposed algorithm against the existing Genetic algorithm (GA), Optimised Firefly search (OFS) algorithm, and Ant Colony (AC) algorithm. The comparision results illustrate that the proposed ECS algorithm consumes less energy than the participant algorithms while maintaining a steady performance for SLA and VM migration. The ECS algorithm consumes around 25% less energy than GA, 27% less than OFS, and 26% less than AC.

关键词： virtual machine placement meta-heuristic algorithms enhanced cuckoo search algorithm cloud computing

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A novel enhanced cuckoo search algorithm for contrast enhancement of gray scale images

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APPLIED SOFT COMPUTING 2019年 85卷 105749-000页

作者： Kamoona, Ammar Mansoor Patra, Jagdish Chandra Univ Kufa Fac Engn Dept Elect Engn Najaf Iraq Swinburne Univ Technol Fac Sci Engn & Technol Melbourne Vic Australia

A good contrast image has a significant role in different image processing applications and computer vision algorithms. One of the most common contrast enhancement approaches is histogram equalization (HE) that enhances the contrast of an image globally. However, it gives rise to some over-enhanced regions, loss of detail information, and enhancement of noise. In order to improve the performance of the HE algorithm, local HE and adaptive HE algorithms have been proposed but with limited success. Recently, an evolutionary algorithm named cuckoo search (CS) algorithm has been employed for automatic image contrast enhancement showing promising performance. In this paper, we propose a novel enhanced cuckoo search (ECS) algorithm for image contrast enhancement. In addition, we propose a new range of search space for the parameters of the local/global enhancement (LGE) transformation that need to be optimized. The proposed ECS algorithm is applied to several low contrast test images and its performance is compared with that of the CS algorithm. Next, we compare the performance of the ECS algorithm with artificial bee colony algorithm using the proposed LGE transformation and a global transformation. In the last stage of performance evaluation, the ECS algorithm is compared with several image enhancement algorithms, namely, HE, CLAHE, Particle Swarm Optimization (PSO), CS, modified CS and CS-PSO algorithms. In all cases, we have shown the superiority of the ECS algorithm in terms of several performance measures. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Optimization algorithms Image contrast enhancement cuckoo search algorithm enhanced cuckoo search algorithm

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