Dynamic Load Balancing Model Based on Server Status (DLBS) for Green Computing

Hasan, Raed Abdulkareem; Mohammed, Muamer N.; Ameedeen, Mohammed Ariff Bin; Khalaf, Emad Taha

doi:10.1166/asl.2018.13016

Cited by 9 publications

(8 citation statements)

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“…In ACO, artificial "ants" (simulation agents) find optimal solutions by traveling through a parameter space, signifying every single conceivable solution. The guideline of an ant colony algorithm is that a unique chemical trail (pheromone) is deposited on the soil amid trips and directs other ants toward an objective solution [9]. The path taken by ants has a high degree of pheromone deposition, and a high pheromone deposition rate encourages more ants taking the same path.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

“…Some of these are non-deterministic NP-hard problems [8]. Reported research and practice on these procedures supported researchers for the continuous extension and intensification of SI and metaheuristics [9] The historical backdrops of metaheuristics and SI are particularly interwoven. Among the first and highly acknowledged metaheuristics are Tabu Search, Genetic Algorithms, and Simulated Annealing (SA).…”

Section: Introductionmentioning

confidence: 99%

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Correlation with the fundamental PSO and PSO modifications to be hybrid swarm optimization

Hasan

Shahab

Ahmed

2021

ijcsm

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A swarm is a group of a single species in which the members interact with one another and with the immediate environment without a principle for control or the emergence of a global intriguing behavior. Swarm-based metaheuristics, including nature-inspired populace-based methods, have been developed to aid the creation of quick, robust, and low-cost solutions for complex problems. Swarm intelligence was proposed as a computational modeling of swarms and has been successfully applied to numerous optimization tasks since its introduction. A correlation with the fundamental Particle Swarm Optimization (PSO) and PSO modifications demonstrates that hybrid swarm optimization outperforms existing strategies. The downside of hybrid swarm optimization is that it frequently tends to arrive at suboptimal solutions. As such, efforts are being made into combining HSO and other algorithms to arrive at better quality solutions

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Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correlation with the fundamental PSO and PSO modifications to be hybrid swarm optimization

Hasan

Shahab

Ahmed

2021

ijcsm

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“…This is a recent development [14][15][16][17][18] in data classification which performs prediction by selecting the majority class at each leaf. The incorporation of Naive Bayes models at the tree leaves can improve the predictive accuracy of HT.…”

Section: Hoeffding Tree (Ht)mentioning

confidence: 99%

Efficient method for breast cancer classification based on ensemble hoffeding tree and naïve Bayes

Alhayali

Ahmed

Mohialden

et al. 2020

IJEECS

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<p><span>The most dangerous type of cancer suffered by women above 35 years of age is breast cancer. Breast Cancer datasets are normally characterized by missing data, high dimensionality, non-normal distribution, class imbalance, noisy, and inconsistency. Classification is a machine learning (ML) process which has a significant role in the prediction of outcomes, and one of the outstanding supervised classification methods in data mining is Naives Bayess Classification (NBC). Naïve Bayes Classifications is good at predicting outcomes and often outperforms other classifications techniques. Ones of the reasons behind this strong performance of NBC is the assumptions of conditional Independences among the initial parameters and the predictors. However, this assumption is not always true and can cause loss of accuracy. Hoeffding trees assume the suitability of using a small sample to select the optimal splitting attribute. This study proposes a new method for improving accuracy of classification of breast cancer datasets. The method proposes the use of Hoeffding trees for normal classification and naïve Bayes for reducing data dimensionality.</span></p>

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“…Considering the PSO algorithm, position and velocity computation resemble the basic PSO algorithm. The position calculation is smoothed by an additional velocity factor [24,25]; that is, a factor is activated in case the average swarm velocity is higher than the maximum velocity. A comparison to the basic PSO and PSO modifications proves HSO outperformed the known methods.…”

Section: The Metaheuristic Search (Aco and Pso) Algorithmsmentioning

confidence: 99%

HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in the Cloudlets

et al. 2018

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Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers' information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made.

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Dynamic Load Balancing Model Based on Server Status (DLBS) for Green Computing

Cited by 9 publications

References 0 publications

Correlation with the fundamental PSO and PSO modifications to be hybrid swarm optimization

Correlation with the fundamental PSO and PSO modifications to be hybrid swarm optimization

Efficient method for breast cancer classification based on ensemble hoffeding tree and naïve Bayes

HSO: A Hybrid Swarm Optimization Algorithm for Reducing Energy Consumption in the Cloudlets

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