Proposing a load balancing method based on Cuckoo Optimization Algorithm for energy management in cloud computing infrastructures

Yakhchi, Moona; Ghafari, Seyed Mohssen; Yakhchi, Shahpar; Fazeli, Mahdi; Patooghi, Ahmad

doi:10.1109/icmsao.2015.7152209

Cited by 46 publications

(25 citation statements)

References 8 publications

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“…Furthermore, in our previous works (Ghafari, Fazeli, Patooghy, & Rikhtechi, 2013;Yakhchi, Ghafari, Yakhchi, Fazeli, & Patooghi, 2015a;Yakhchi, Ghafari, Yakhchi, Fazeli, & Patooghy, 2015b), we also proposed three different approaches based on heuristic algorithms, like ABC, Cuckoo Optimization Algorithm, and ICA to establish load balancing in cloud computing datacenters and decrease the energy consumption. Our experimental results indicate that the proposed heuristic approaches have better performance compared to state-of-the-art algorithms like Local Regression (Ghafari et al, 2013), Dynamic Voltage Frequency Scaling (Ghafari et al, 2013), Interquartile Range (Ghafari et al, 2013), and Median Absolute Deviation (Ghafari et al, 2013).…”

Section: Applications Of Heuristic Algorithmsmentioning

confidence: 99%

A survey on association rules mining using heuristics

Ghafari

Tjortjis

2019

WIREs Data Min & Knowl

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Association rule mining (ARM) is a commonly encountred data mining method. There are many approaches to mining frequent rules and patterns from a database and one among them is heuristics. Many heuristic approaches have been proposed but, to the best of our knowledge, there is no comprehensive literature review on such approaches, yet with only a limited attempt. This gap needs to be filled. This paper reviews heuristic approaches to ARM and points out their most significant strengths and weaknesses. We propose eight performance metrics, such as execution time, memory consumption, completeness, and interestingness, we compare approaches against these performance metrics and discuss our findings. For instance, comparison results indicate that SRmining, PMES, Ant‐ARM, and MDS‐H are the fastest heuristic ARM algorithms. HSBO‐TS is the most complete one, while SRmining and ACS require only one database scan. In addition, we propose a parameter, named GT‐Rank for ranking heuristic ARM approaches, and based on that, ARMGA, ASC, and Kua emerge as the best approaches. We also consider ARM algorithms and their characteristics as transactions and items in a transactional database, respectively, and generate association rules that indicate research trends in this area. This article is categorized under: Algorithmic Development > Association Rules Technologies > Association Rules Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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Section: Applications Of Heuristic Algorithmsmentioning

confidence: 99%

A survey on association rules mining using heuristics

Ghafari

Tjortjis

2019

WIREs Data Min & Knowl

Self Cite

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“…Both metaheuristics have various applications for practical NP-hard challenges, RFID network planning problem [54], training feed-forward neural networks [55] and constrained portfolio optimization [56], and were also tested for benchmark functions [57,58]. Some of the BA and CS implementations for cloud computing challenges include scheduling workflow applications [59,60], cloud service composition [61], task scheduling [62] and load balancing [63].…”

Section: Review Of Swarm Intelligence Metaheuristics and Its Applicatmentioning

confidence: 99%

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Strumberger

Tuba

Bačanin

et al. 2019

JSAN

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Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer networks (the Internet). Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state-of-the-art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management.

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“…Many research results have been proposed in this field, such as the migration cost-aware locally optimal placement algorithm (pMaP) [8], the peak clusteringbased placement (PCP) [8], the minimum migration time cuckoo optimization algorithm (COA-MMT) [9], the minimum migration time imperialism competitive algorithm (ICAMMT) [10], etc. However, three major problem-specific challenges make the solution a complex task:…”

Section: Introductionmentioning

confidence: 99%