scholarly journals Computational Intelligence in Cloud Computing

2019 ◽  
pp. 111-127
Author(s):  
Ruggero Donida Labati ◽  
Angelo Genovese ◽  
Vincenzo Piuri ◽  
Fabio Scotti ◽  
Sarvesh Vishwakarma
Keyword(s):  
Cloud Computing ◽  
Computational Intelligence

An Ant-Colony-Based Meta-Heuristic Approach for Load Balancing in Cloud Computing

2021 ◽  
pp. 873-903
Author(s):  
Santanu Dam ◽  
Gopa Mandal ◽  
Kousik Dasgupta ◽  
Parmartha Dutta
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Computational Intelligence ◽  
Optimal Solution ◽  
Ant Colony ◽  
Multi Agent Systems ◽  
Distributed Environment ◽  
Intelligent Behavior ◽  
Computational Intelligence Technique ◽  
Multi Agent

This book chapter proposes use of Ant Colony Optimization (ACO), a novel computational intelligence technique for balancing loads of virtual machine in cloud computing. Computational intelligence(CI), includes study of designing bio-inspired artificial agents for finding out probable optimal solution. So the central goal of CI can be said as, basic understanding of the principal, which helps to mimic intelligent behavior from the nature for artifact systems. Basic strands of ACO is to design an intelligent multi-agent systems imputed by the collective behavior of ants. From the perspective of operation research, it's a meta-heuristic. Cloud computing is a one of the emerging technology. It's enables applications to run on virtualized resources over the distributed environment. Despite these still some problems need to be take care, which includes load balancing. The proposed algorithm tries to balance loads and optimize the response time by distributing dynamic workload in to the entire system evenly.

An Ant-Colony-Based Meta-Heuristic Approach for Load Balancing in Cloud Computing

2018 ◽  
pp. 204-232 ◽  
Author(s):  
Santanu Dam ◽  
Gopa Mandal ◽  
Kousik Dasgupta ◽  
Parmartha Dutta
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Computational Intelligence ◽  
Optimal Solution ◽  
Ant Colony ◽  
Multi Agent Systems ◽  
Distributed Environment ◽  
Intelligent Behavior ◽  
Computational Intelligence Technique ◽  
Multi Agent

This book chapter proposes use of Ant Colony Optimization (ACO), a novel computational intelligence technique for balancing loads of virtual machine in cloud computing. Computational intelligence(CI), includes study of designing bio-inspired artificial agents for finding out probable optimal solution. So the central goal of CI can be said as, basic understanding of the principal, which helps to mimic intelligent behavior from the nature for artifact systems. Basic strands of ACO is to design an intelligent multi-agent systems imputed by the collective behavior of ants. From the perspective of operation research, it's a meta-heuristic. Cloud computing is a one of the emerging technology. It's enables applications to run on virtualized resources over the distributed environment. Despite these still some problems need to be take care, which includes load balancing. The proposed algorithm tries to balance loads and optimize the response time by distributing dynamic workload in to the entire system evenly.

scholarly journals Computational Intelligence in the Context of Industry 4.0

2021 ◽  
pp. 27-94
Author(s):  
Alexander Hošovský ◽  
Ján Piteľ ◽  
Monika Trojanová ◽  
Kamil Židek
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Computational Intelligence ◽  
Industry 4.0 ◽  
Typical Feature ◽  
Theory And Practice ◽  
The Internet ◽  
Transmission Of Information ◽  
The Internet Of Things

AbstractIndustry 4.0 is affecting almost every area of the industry, and as a result of its effects, systems, technologies, and the way information is processed are being transformed. Its typical feature is transmission of information in the system environment provided by the Internet of Things. All information should be stored and shared through cloud computing. As a result, access to information should be unrestricted. This chapter is focused on Computational Intelligence (CI) in the context of Industry 4.0. Each subchapter provides fundamentals of some paradigms, followed by the use of CI in the concrete paradigm. The ending part of the chapter is focused on connecting theory and practice in a case study, which lists industrial parts recognition by convolutional neural networks for assisted assembly.

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