Hybrid soft computing approach for energy efficiency in cloud computing

Jasuja, Kumari Parul; Kaur, Khushdeep

doi:10.1109/cesys.2016.7889945

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…This is executed using the combination of two algorithms namely: a) niche genetic algorithm, and, b) random depth-first search. Jasuja et al [ 24 ] proposed a hybrid approach for optimizing the energy that produces good results in migration and energy consumption terms. The proposed system uses a combination of algorithms, namely ACO and a gravitational search algorithm.…”

Section: Related Workmentioning

confidence: 99%

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

Goyal

Bhushan²,

Kumar³

et al. 2021

Sensors

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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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Section: Related Workmentioning

confidence: 99%

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

Goyal

Bhushan²,

Kumar³

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…Static methods never reduce the energy because that is still running after completing the execution of the task. The manager is deployed over the consolidation process, which decides the less power resource [3]. Most of the power consumed by the cloud is CPU because of the processing capability.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Power Aware Algorithm for Optimizing Energy Consumption of Cloud Resources Using Multi Agent Model

Prabha¹,

Ramesh²,

Renjith³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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Cloud computing offers different kinds of resources to the user based on the current need. The resource includes hardware, software and platform through virtualization. User requirements are mapped to the corresponding Virtual Machine (VM) by the host of the data center. Every resource in the data center consumed a considerable amount of energy. The existing power aware models are used to minimize the energy consumption but it follows only one specific layer in the cloud. These methods suffer from performance problems due to excess energy by the idle resources. The resources like data center, host and VM consumed energy in case of idle workload and minimum workload. Idle resources are identified and removed from the resource list in order to minimize the excess energy consumption. The resource with the minimum workload is migrated to the other suitable resources using the migration method. The proposed power aware model uses multi agent support for minimizing the energy in Data center, Host and VM level. The overall analysis is carried out based on the performance parameter like Service Level Agreement (SLA) based host shutdown and violation etc. The proposed method consumes minimum energy when compared to other existing models. It achieves maximum performance by considering power aware parameters in the cloud.

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