2019
DOI: 10.3390/app9224893
|View full text |Cite
|
Sign up to set email alerts
|

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

Abstract: The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polyn… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
45
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
5
4

Relationship

2
7

Authors

Journals

citations
Cited by 96 publications
(45 citation statements)
references
References 98 publications
0
45
0
Order By: Relevance
“…These combinations help in getting the highest accuracies in classification and further reducing computation complexities in load balancing over the number of applications. In [52], the authors proposed a hybrid metaheuristic algorithm called WOA-AEFS. They solved the resource scheduling problem in cloud computing.…”
Section: Literature Reviewmentioning
confidence: 99%
“…These combinations help in getting the highest accuracies in classification and further reducing computation complexities in load balancing over the number of applications. In [52], the authors proposed a hybrid metaheuristic algorithm called WOA-AEFS. They solved the resource scheduling problem in cloud computing.…”
Section: Literature Reviewmentioning
confidence: 99%
“…(1) In order to improve the local search capability of foraging behavior on BSA, we put forward equation 8based on the dynamic multi-swarm method. (2) In order to get the guiding vector to improve the global search capability of foraging behavior on BSA, we put forward equations (9), (11), and (12) based on differential evolution. (3) In order to expand the initialization search space of the bird to improve the global search capability on BSA, we put forward equations (16) and 17based on quantum behavior.…”
Section: E Initialization Of Search Space Based On Quantummentioning
confidence: 99%
“…According to the foraging behavior, vigilance behavior, and flight behavior of the bird swarms in nature, Meng et al proposed a novel swarm intelligence algorithm called bird swarm algorithm (BSA) [7]. Meanwhile, due to these advantages above, swarm intelligence algorithms have been applied to optimize various fields, such as PSO for mutation testing problems [8], genetic algorithm (GA) for convolutional neural networks parameters [9], FA for convolutional neural network problems [10], and whale optimization algorithm (WOA) for cloud computing environments [11]. So, BSA which will be used in this paper has been widely applied to engineering optimization problems.…”
Section: Introductionmentioning
confidence: 99%
“…Some of the notable swarm intelligence algorithms are particle swarm optimization (PSO) [25], artificial bee colony (ABC) [26][27][28], ant colony optimization (ACO) [29], bacterial foraging optimization (BFO) [30], the firefly algorithm (FA) [31], the whale optimization algorithm (WOA) [32,33], the bat algorithm (BA) [34], cuckoo search (CS) [35,36], elephant herding optimization (EHO) [37], Monarch butterfly optimization (MBO) [38], and the tree growth algorithm (TGA) [39]. There are many successful applications of swarm intelligence algorithms on different real-life problems that were performed by many authors such as .…”
Section: Introductionmentioning
confidence: 99%