Power Aware-Based Workflow Model of Grid Computing Using Ant-Based Heuristic Approach

Reddy, T. Sunil Kumar; Raju, Dasari Naga; Kumar, Pardeep; Kumar, Sanjeev

doi:10.1007/978-981-10-6620-7_18

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Inrecentlife,IoT-based-cloudplaysamainroleintheprocessingofdatatocloudfromIoT gadgets.Itgeneratesvirtualizedcomputerresourcesthathaveanassortmentofvariousassignments andvastnumberoftasksthatallowsthemtobepreparedandscaled-outviathefastprovisioning asastrategyandonmeteredclaimoverthenetwork.ManagementofresourceinIoT-based-cloud todeveloptheinfoisintendedtodealwithhugedatavolume.Thepolicyofresourcemanagement workswiththescalableandsystemperformanceisbalancingthetasksallocationandworkload (Bohn etal.,2011).Mostoftheorganizationslike,Yahoo,Amazon,SunandGoogleimplementvirtual datacenterstosustainactivenetworkbandwidth,decreasetheconsumptionofenergy(Bhadani& Chaudhary,2010)andreducethetimemakespan.However,Amazonprocessesthedatahundred andthousandsofmattersfordatagenerationandrelocatesallinformationtothecloud (Giangeta., 2015),theoperatorsalsofamiliarizethisviatheorganization;Amazonisoneofthemaininventors inthemaintenanceofthecloud.Amazonreformstheirowndatacenters,whichcarriedaboutnotable raisesininternalproductivity.In2005,Amazon'scloudcomputingframeworkcalledAmazonweb serviceswasconsidered (Royetal.,2017).Amazonwastheoneofthefirstlinkstooffercloud computing facility (Reddy et al, 2018). IoT-based-cloud environment is in dynamic nature, and itallocatestheworkloadandtaskstoresourcesinthedevelopmentofFogcomputingscenarioin real-timeenvironment,whichisastimulatingarea (Ojhaetal.,2014).Toprogresseffectiveresource managementinFogcomputingparadigmthatreducesenergyconsumptionandlatency,toofferreal geographicaldistributiontoconnectIoTdevicestointernetdevices(basestations(BS),ISP,insternet serviceproviders(ISP))andtoincreasethesystemscalabilityisafrantictask (Mishraetal.,2018).…”

Section: Recourse Management and Motivation In Iot-based-cloudmentioning

confidence: 99%

Feedback-Based Fuzzy Resource Management in IoT-Based-Cloud

Mallikarjuna

2020

International Journal of Fog Computing

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The main aim of Internet of Things (IoT) is to get every “thing” (sensors, smart cameras, wearable devices, and smart home appliances) to connect to the internet. Henceforth to produce the high volume of data required for data processing between IoT devices, large storage and the huge number of applications to offer cloud computing as a service. The purpose of IoT-based-cloud is to manage the resources, and effective utilization of tasks in cloud. The end user applications are essential to enhance the QoS parameters. As per the QoS parameters, the service provider makes the speed up of tasks. There is a requirement for assigning responsibilities based on priority. The cloud services are increased to the network edge, and the planned model is under the Fog computing paradigm to reduce the makespan of time. The priority based fuzzy scheduling approach is brought by the dynamic feedback-based mechanism. The planned mechanism is verified with the diverse prevailing algorithms and evidenced that planned methodology is supported by effective results.

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Section: Recourse Management and Motivation In Iot-based-cloudmentioning

confidence: 99%

Feedback-Based Fuzzy Resource Management in IoT-Based-Cloud

Mallikarjuna

2020

International Journal of Fog Computing

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“…The energy consumption also conflicts with the execution time and makespan of the data centers. Hence, there is a need of proper scheduling mechanisms to perform the trade-off between the user requirements and operating performance (George Amalarethinam and Kavitha, 2018;Reddy et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

MOEAGAC: an energy aware model with genetic algorithm for efficient scheduling in cloud computing

Marri

Rajalakshmi

2021

IJICC

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PurposeMajority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling.Design/methodology/approachCloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption.FindingsThe energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.Originality/valueThis paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.

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Feedback-based fuzzy resource management in IoT using fog computing

Reddy

Krishna

2020

Evol. Intel.

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Power Aware-Based Workflow Model of Grid Computing Using Ant-Based Heuristic Approach

Cited by 3 publications

References 18 publications

Feedback-Based Fuzzy Resource Management in IoT-Based-Cloud

Feedback-Based Fuzzy Resource Management in IoT-Based-Cloud

MOEAGAC: an energy aware model with genetic algorithm for efficient scheduling in cloud computing

Feedback-based fuzzy resource management in IoT using fog computing

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