Neural network inspired efficient scalable task scheduling for cloud infrastructure

The rapid development of Cloud Computing in the 21st Century is landmark occasion, not only in the field of technology, but also in the field of engineering and services. The development in cloud architecture and services has enabled fast and easy transfer of data from one unit of a network to other...

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Main Authors: Punit Gupta, Arnaav Anand, Pratyush Agarwal, Gavin McArdle
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2024-01-01
Series:Internet of Things and Cyber-Physical Systems
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Online Access:http://www.sciencedirect.com/science/article/pii/S2667345224000051
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author Punit Gupta
Arnaav Anand
Pratyush Agarwal
Gavin McArdle
author_facet Punit Gupta
Arnaav Anand
Pratyush Agarwal
Gavin McArdle
author_sort Punit Gupta
collection DOAJ
description The rapid development of Cloud Computing in the 21st Century is landmark occasion, not only in the field of technology, but also in the field of engineering and services. The development in cloud architecture and services has enabled fast and easy transfer of data from one unit of a network to other. Cloud services support the latest transport services like smart cars, smart aviation services and many others. In the current trend, smart transport services depend on the performance of cloud Infrastructure and its services. Smart cloud services derive real time computing and allows it to make smart decision. For further improvement in cloud services, cloud resource optimization is a vital cog that defines the performance of cloud. Cloud services have certainly aimed to make the optimum use of all available resources to the become as cost efficient and time efficient as possible. One of the issues that still occur in multiple Cloud Environments is a failure in task execution. While there exist multiple methods to tackle this problem in task scheduling, in the recent times, the use of smart scheduling techniques has come to prominence. In this work, we aim to use the Harmony Search Algorithm and neural networks to create a fault aware system for optimal usage of cloud resources. Cloud environments are in general expected to be free of any errors or faults but with time and experience, we know that no system can be faultless. With our approach, we are looking to create the best possible time-efficient system for faulty environments, Where the result shows that the proposed harmony search-inspired ANN model provides least execution time, number of task failures, power consumption and high resource utilization as compared to recent Red fox and Crow search inspired models.
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institution Kabale University
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publishDate 2024-01-01
publisher KeAi Communications Co., Ltd.
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series Internet of Things and Cyber-Physical Systems
spelling doaj-art-6c5ebdeeaf9747d799b404c10c2f981e2025-01-27T04:22:37ZengKeAi Communications Co., Ltd.Internet of Things and Cyber-Physical Systems2667-34522024-01-014268279Neural network inspired efficient scalable task scheduling for cloud infrastructurePunit Gupta0Arnaav Anand1Pratyush Agarwal2Gavin McArdle3University College Dublin, Ireland; Corresponding author.Manipal University Jaipur, Jaipur, IndiaManipal University Jaipur, Jaipur, IndiaUniversity College Dublin, IrelandThe rapid development of Cloud Computing in the 21st Century is landmark occasion, not only in the field of technology, but also in the field of engineering and services. The development in cloud architecture and services has enabled fast and easy transfer of data from one unit of a network to other. Cloud services support the latest transport services like smart cars, smart aviation services and many others. In the current trend, smart transport services depend on the performance of cloud Infrastructure and its services. Smart cloud services derive real time computing and allows it to make smart decision. For further improvement in cloud services, cloud resource optimization is a vital cog that defines the performance of cloud. Cloud services have certainly aimed to make the optimum use of all available resources to the become as cost efficient and time efficient as possible. One of the issues that still occur in multiple Cloud Environments is a failure in task execution. While there exist multiple methods to tackle this problem in task scheduling, in the recent times, the use of smart scheduling techniques has come to prominence. In this work, we aim to use the Harmony Search Algorithm and neural networks to create a fault aware system for optimal usage of cloud resources. Cloud environments are in general expected to be free of any errors or faults but with time and experience, we know that no system can be faultless. With our approach, we are looking to create the best possible time-efficient system for faulty environments, Where the result shows that the proposed harmony search-inspired ANN model provides least execution time, number of task failures, power consumption and high resource utilization as compared to recent Red fox and Crow search inspired models.http://www.sciencedirect.com/science/article/pii/S2667345224000051HSANNTask schedulingMetaheuristicGenetic algorithmCloud infrastructure
spellingShingle Punit Gupta
Arnaav Anand
Pratyush Agarwal
Gavin McArdle
Neural network inspired efficient scalable task scheduling for cloud infrastructure
Internet of Things and Cyber-Physical Systems
HS
ANN
Task scheduling
Metaheuristic
Genetic algorithm
Cloud infrastructure
title Neural network inspired efficient scalable task scheduling for cloud infrastructure
title_full Neural network inspired efficient scalable task scheduling for cloud infrastructure
title_fullStr Neural network inspired efficient scalable task scheduling for cloud infrastructure
title_full_unstemmed Neural network inspired efficient scalable task scheduling for cloud infrastructure
title_short Neural network inspired efficient scalable task scheduling for cloud infrastructure
title_sort neural network inspired efficient scalable task scheduling for cloud infrastructure
topic HS
ANN
Task scheduling
Metaheuristic
Genetic algorithm
Cloud infrastructure
url http://www.sciencedirect.com/science/article/pii/S2667345224000051
work_keys_str_mv AT punitgupta neuralnetworkinspiredefficientscalabletaskschedulingforcloudinfrastructure
AT arnaavanand neuralnetworkinspiredefficientscalabletaskschedulingforcloudinfrastructure
AT pratyushagarwal neuralnetworkinspiredefficientscalabletaskschedulingforcloudinfrastructure
AT gavinmcardle neuralnetworkinspiredefficientscalabletaskschedulingforcloudinfrastructure