Energy-Efficient Resource Allocation in Data Centers Using a Hybrid Evolutionary Algorithm

Reddy, V. Dinesh; Gangadharan, G. R.; Rao, G Sudhakara; Aiello, Marco

doi:10.1007/978-981-15-3689-2_4

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…By comparing the discussed techniques with alternative methods, this analysis offers insights into the strengths and limitations of each approach, aiding researchers and practitioners in selecting the most suitable scheduling technique for their specific requirements. [62] Data locality ✗ ✓ ✗ N/A Parental prioritization-based task scheduling algorithm [64] Fairness N/A N/A ✓ ✗ Modified particle swarm optimization algorithm [63] N/A N/A N/A ✓ ✗ A hybrid of genetic and particle swarm optimization [65] N/A ✗ N/A ✓ ✓…”

Section: Previous Workmentioning

confidence: 99%

The scheduling techniques in the Hadoop and Spark of smart cities environment: a systematic review

Mirza,

Ali,

Ishak

2024

Bulletin EEI

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Processing extensive and diverse data in real-time is a significant challenge in the context of smart cities. Timely access to information and efficient analytics is essential for smart city services to make data-driven decisions and enhance urban living. Scheduling algorithms play a crucial role in ensuring the prompt delivery of services and efficient task completion. This paper explores various scheduling techniques, including static, dynamic, and hybrid schedulers, and compares their objectives and performance. Additionally, the study examines two prominent data processing frameworks, Hadoop and Spark, and compares their capabilities in handling big data in smart cities. With its ability to process large amounts of data quickly and efficiently, Spark has shown superiority over Hadoop in real-time data processing and performance optimization. The paper concludes by highlighting the strengths and limitations of each framework. It discusses the need for further research in optimizing scheduling techniques and exploring hybrid artificial intelligence scheduling for Spark. Overall, the findings contribute to a better understanding of data processing in real-time and provide insights for researchers and practitioners in smart cities.

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Section: Previous Workmentioning

confidence: 99%

The scheduling techniques in the Hadoop and Spark of smart cities environment: a systematic review

Mirza,

Ali,

Ishak

2024

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…Therefore, an optimization technique is needed to deal with such uncertainty situation [16,17]. Heuristic/ meta-heuristic approaches have been followed by the present resource scheduling techniques for scheduling and identifying the virtual machine (VM) [18,19]. Optimization process has been endorsed by the Meta-heuristic techniques which can capably deal with distributed, dynamic, and erratic demand, and based on the situation capable to manage VMs.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-EPO Optimization Technique for Optimised Resource Allocation and Minimum Energy Consumption with the Brownout Algorithm

Samriya

Tiwari

Obaidat

et al. 2023

Wireless Pers Commun

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Cloud Computing is an eminent and reputable agenda which relies on large-scale distributed processing to provide access to their resources and services. In the cloud environment a rigorous management system is mandatory to collect all information regarding task processing levels and proving impartial resource provisioning through the levels of Quality of Service (QoS). These concerns can be settled by employing a meta-heuristic optimization-based resource management. Subsequently, this paper presents a Fuzzy Emperor Penguin Optimization (Fuzzy-EPO) algorithmbased resource provisioning framework for heterogeneous cloud environment. To deploy the optimal set of virtual machines (VM) to physical machines the VM allocation model is employed. The proposed Fuzzy-EPO algorithm does the VM consolidation mainly to reallocate overloaded VM to under-loaded PM to minimize the migration time and the brownout mechanism is adopted to reduce the rate of energy consumption. CloudSim simulation platform is used to implement the proposed system. The simulation results expose that the proposed Fuzzy -EPO based system is effective in restraining the proportion of SLA violation and increasing QoS requirements for providing proficient cloud service.

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Section: Introductionmentioning

confidence: 99%

Fuzzy-epo Optimization Technique for Optimised Resource Allocation and Minimum Energy Consumption With the Brownout Algorithm.

Samariya

Tiwari

Obaidat

et al. 2022

Preprint

View full text Add to dashboard Cite

Cloud Computing is an eminent and reputable agenda which relies on large-scale distributed processing to provide access to their resources and services. In the cloud environment a rigorous management system is mandatory to collect all information regarding task processing levels and proving impartial resource provisioning through the levels of Quality of Service (QoS). These concerns can be settled by employing a meta-heuristic optimization-based resource management. Subsequently, this paper presents a Fuzzy Emperor Penguin Optimization (Fuzzy-EPO) algorithm-based resource provisioning framework for heterogeneous cloud environment. To deploy the optimal set of virtual machines (VM) to physical machines the VM allocation model is employed. The proposed Fuzzy-EPO algorithm does the VM consolidation mainly to reallocate overloaded VM to under-loaded PM to minimize the migration time and the brownout mechanism is adopted to reduce the rate of energy consumption. CloudSim simulation platform is used to implement the proposed system. The simulation results expose that the proposed Fuzzy -EPO based system is effective in restraining the proportion of SLA violation and increasing QoS requirements for providing proficient cloud service

show abstract

Energy-Efficient Resource Allocation in Data Centers Using a Hybrid Evolutionary Algorithm

Cited by 7 publications

References 34 publications

The scheduling techniques in the Hadoop and Spark of smart cities environment: a systematic review

The scheduling techniques in the Hadoop and Spark of smart cities environment: a systematic review

Fuzzy-EPO Optimization Technique for Optimised Resource Allocation and Minimum Energy Consumption with the Brownout Algorithm

Fuzzy-epo Optimization Technique for Optimised Resource Allocation and Minimum Energy Consumption With the Brownout Algorithm.

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