A Hybrid Swarm Optimization Algorithm For Reducing Energy Consumption In The Cloud

Background: The Internet of Thing is a network that enables multiple hardware devices, sensors and other home applications from electronically communicate with each other. New era such technology is increasing importance mainly due to the revolutionary development of information technologies. Methods: However, energy efficiency is still a big challenge facing IoT technology. Thus, it becomes an interesting topic for many researchers to investigate. Current work aims to reduce the energy consumption thereby introducing the shortest path technique and another new practicing for Particle Swarm Optimization algorithm in the Internet of Thing cooperative clusters. Results: The main concept is based on cluster heads cooperation with each other known as Cooperative Clusters to transfer information to the base station. The Primary results reveals a 17% and 16% reduction in energy consumption was achieved over the shortest path technique and Particle Swarm Optimization algorithm respectively. Results also show a remarkable improvement in the system lifetime due to the new applied scheme. Conclusion: The other method is by PSO algothrim at beginig it sending Randomly then it will select the path by using the feed back acknowledgment after that it will collect the information by sending it for the Cluster heads by updating the information status automatically. That’s why we discovered the PSO advantages than the others.

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An Optimized Framework for Energy-Resource Allocation in a Cloud Environment based on the Whale Optimization Algorithm

Sensors ◽

10.3390/s21051583 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1583 ◽

Cited By ~ 1

Author(s):

Shanky Goyal ◽

Shashi Bhushan ◽

Yogesh Kumar ◽

Abu ul Hassan S. Rana ◽

Muhammad Raheel Bhutta ◽

...

Keyword(s):

Cloud Computing ◽

Energy Consumption ◽

Optimization Algorithm ◽

Search Algorithm ◽

Energy Resource ◽

Whale Optimization Algorithm ◽

Cloud Environment ◽

Swarm Optimization ◽

Cloud Technology ◽

Whale Optimization

Cloud computing offers the services to access, manipulate and configure data online over the web. The cloud term refers to an internet network which is remotely available and accessible at anytime from anywhere. Cloud computing is undoubtedly an innovation as the investment in the real and physical infrastructure is much greater than the cloud technology investment. The present work addresses the issue of power consumption done by cloud infrastructure. As there is a need for algorithms and techniques that can reduce energy consumption and schedule resource for the effectiveness of servers. Load balancing is also a significant part of cloud technology that enables the balanced distribution of load among multiple servers to fulfill users’ growing demand. The present work used various optimization algorithms such as particle swarm optimization (PSO), cat swarm optimization (CSO), BAT, cuckoo search algorithm (CSA) optimization algorithm and the whale optimization algorithm (WOA) for balancing the load, energy efficiency, and better resource scheduling to make an efficient cloud environment. In the case of seven servers and eight server’s settings, the results revealed that whale optimization algorithm outperformed other algorithms in terms of response time, energy consumption, execution time and throughput.

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