Optimizing Power and Energy Efficiency in Cloud Computing

Khan, Naveed Ahmed; Shrestha, Raju

doi:10.5220/0007723503800387

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Edge cloudbased framework using three layers for wireless resource scheduling of data offloading service computation is performed. This algorithm can save 80% of energy [27]. Cloud resources status is monitored by analyzing data obtained from tasks.…”

Section: Related Workmentioning

confidence: 99%

Novel Dynamic Scaling Algorithm for Energy Efficient Cloud Computing

Kumar¹,

Venkatachalam²,

Masud³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Huge data processing applications are stored efficiently using cloud computing platform. Few technologies like edge computing, Internet of Things (IoT) model helps cloud computing framework for executing data with less energy and latencies for better infrastructure. Recently researches focused on providing excellent services to cloud computing users. Also, cloud-based services are highly developed over IT field. Energy a level varies based on the cloud setup like speed, memory, service capability and bandwidth. The user job requirements are varied based its nature. The process of identifying efficient energy resources for the user job is main aim of this research work. Initially IoT, Edge devices capture the job and process to help cloud infrastructure. Data transferring process is a Non-deterministic Polynomial (NP) hard problem which can be easily supported by technologies like IoT and Edge computing. Main issue is, tremendous increase of data over the cloud causes difficult to manage consumption of energy. In this research work server clouds with edge devices and centralized cloud servers are combined to work for providing efficient energy consumption. Here, in this paper we implement novel dynamic speed scaling (NDS) algorithm. The CPU workload is first computed using NDS algorithm for incoming application. The less energy computation is achieved using energy internet of things (EIoT). Fine methodology called speed processor scaling, helps to consume less energy and less computation price. High energy consumption of processor is due to high computation speed. likewise, less energy is consumed during less processor computation speed. This technique is building in NDS algorithm and computed in Edge devices for less energy consumption. The result evaluation proves that proposed technique consumes only 10 s for data computation when compared to other existing techniques.

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Section: Related Workmentioning

confidence: 99%

Novel Dynamic Scaling Algorithm for Energy Efficient Cloud Computing

Kumar¹,

Venkatachalam²,

Masud³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Findings offered a good understanding of how power and energy usage were affected by various workloads. The tools and structure presented can be used for research and improving energy efficiency in any cloud environment and of any scale [20]. A problem of energy optimization has been modeled whereas the task dependence, transfer of data, and some constraints such as response time, and cost have been considered and solved by genetic algorithms.…”

Section: Related Workmentioning

confidence: 99%

An optimized cost-based data allocation model for heterogeneous distributed computing systems

Tarun

Dubey

Batth

et al. 2022

IJECE

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<span lang="EN-US">Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in the fields of connectivity technologies, network programs, high processing components, and storage helps to improvise results. However, concerns such as slowness in response, long execution time, and long completion time have been identified as stumbling blocks that hinder performance and require additional attention. These defects increased the total system cost and made the data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has been strengthened to improve data allocation performance. To do this, an abstract job model is employed, and a data query file containing input data is processed on a directed acyclic graph. The jobs are executed on the processing engine with the lowest execution cost, and the system's total cost is calculated. The total cost is computed by summing the costs of communication, computation, and network. The total cost of the system will be reduced using a Swarm intelligence algorithm. In heterogeneous distributed computing systems, the suggested approach attempts to reduce the system's total cost and improve data distribution. According to simulation results, the technique efficiently lowers total system cost and optimizes partitioned data allocation.</span>

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Optimizing Power and Energy Efficiency in Cloud Computing

Cited by 2 publications

References 9 publications

Novel Dynamic Scaling Algorithm for Energy Efficient Cloud Computing

Novel Dynamic Scaling Algorithm for Energy Efficient Cloud Computing

An optimized cost-based data allocation model for heterogeneous distributed computing systems

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