Energy-Aware Multi-objective Differential Evolution in Cloud Computing

Kollu, Archana; Sucharita, V.

doi:10.1007/978-981-10-5520-1_40

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…Researchers in [16] use multi-objective modified differential evolution algorithm for resource allocation. The proposed algorithm is able to manage the energy and resource utilization efficiently with accounting different resources of the system.…”

Section: Related Workmentioning

confidence: 99%

VM Consolidation Plan for Improving the Energy Efficiency of Cloud

Satveer

Aswal

2021

Cybernetics and Information Technologies

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Achieving energy-efficiency with minimal Service Level Agreement (SLA) violation constraint is a major challenge in cloud datacenters owing to financial and environmental concerns. The static consolidation of Virtual Machines (VMs) is not much significant in recent time and has become outdated because of the unpredicted workload of cloud users. In this paper, a dynamic consolidation plan is proposed to optimize the energy consumption of the cloud datacenter. The proposed plan encompasses algorithms for VM selection and VM placement. The VM selection algorithm estimates power consumption of each VM to select the required VMs for migration from the overloaded Physical Machine (PM). The proposed VM allocation algorithm estimates the net increase in Imbalance Utilization Value (IUV) and power consumption of a PM, in advance before allocating the VM. The analysis of simulation results suggests that the proposed dynamic consolidation plan outperforms other state of arts.

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Section: Related Workmentioning

confidence: 99%

VM Consolidation Plan for Improving the Energy Efficiency of Cloud

Satveer

Aswal

2021

Cybernetics and Information Technologies

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“…We set the population size as 50, maximum number of iterations is set to 150 for ensuring global exploration and each experiment is executed continuously for 30 times and the mean of each run was duly noted due to the stochastic nature of algorithms. We compare our proposed PHJA with the modified best-fit decreasing (MBFD) algorithm (Beloglazov et al , 2012), GA (Wu et al , 2012), PSO (Dashti and Rahmani, 2016), multi-objective differential evolution (MODE) (Kollu and Sucharita, 2018). For GA (Wu et al , 2012), the mutation probability is 50%, crossover probability is 70% and random selection method is used to select chromosomes for crossover and mutation.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…For PSO (Dashti and Rahmani, 2016), the learning factors (k1, k2) are initialized to 2 and 2, respectively, to prevent divergence of the particles. For MODE (Kollu and Sucharita, 2018), the mutation strategy DE/current – to – rand/1/bin and initial parameters setting NP is varying from 20 to 50, k = 0.5, F = 0.8, CR = 0.9. The physical host description and the characteristics for each VM are illustrated in Tables 1 and 2.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The recent reports signify that 55% of the energy is used by the servers and data equipment’s of DCs and energy is one of the critical total cost of ownership (TCO) factors in managing a cloud DC (Cao and Zhu, 2013). Massive DCs have extensive effects on the environment, as well as the higher energy consumption inevitably leads to more carbon emission (Kollu and Sucharita, 2018). Moreover, inefficient use of resources is one of the major factors for the excessive energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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Energy efficiency in cloud data centres using parallel hybrid Jaya algorithm

Kollu

Sucharita

2020

IJPCC

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Purpose Data centres evolve constantly in size, complexity and power consumption. Energy-efficient scheduling in a cloud data centre is a critical and challenging research problem. It becomes essential to minimize the overall operational costs as well as environmental impact and to guarantee the service-level agreements for the services provided by the cloud data centres. Resource scheduling in cloud data centres is NP-hard and often requires substantial computational resources. Design/methodology/approach To overcome these problems, the authors propose a novel model that leads to nominal operational cost and energy consumption in cloud data centres. The authors propose an effective approach, parallel hybrid Jaya algorithm, that performs parallel processing of Jaya algorithm and genetic algorithm using multi-threading and shared memory for interchanging the information to enhance convergence premature rate and global exploration. Findings Experimental results reveal that the proposed approach reduces the power consumption in cloud data centres up to 38% and premature convergence rate up to 60% compared to other algorithms. Originality/value Experimental results reveals that our proposed approach reduces the power consumption in cloud data centres up to 38% and premature convergence rate up to 60% compared to other algorithms.

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A Novel Feature Descriptor for Face Anti-Spoofing Using Texture Based Method

Satveer¹,

Aswal²

2020

Cybernetics and Information Technologies

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In this paper we propose a novel approach for face anti-spoofing called Extended Division Directional Ternary Co-relation Pattern (EDDTCP). The EDDTCP encodes co-relation of ternary edges based on the centre pixel gray values with its immediate directional neighbour and its next immediate average directional neighbour, which is calculated by using the average of cornered neighbours with directional neighbours. The proposed method is robust against presentation attacks by extracting the spatial information in all directions. Three Experiments were performed by using all the four texture descriptors (LBP, LTP, LGS and EDDTCP) and the results are compared. The proposed face anti-spoofing method performs better than LBP, LTP and LGS.

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Energy-Aware Multi-objective Differential Evolution in Cloud Computing

Cited by 3 publications

References 11 publications

VM Consolidation Plan for Improving the Energy Efficiency of Cloud

VM Consolidation Plan for Improving the Energy Efficiency of Cloud

Energy efficiency in cloud data centres using parallel hybrid Jaya algorithm

A Novel Feature Descriptor for Face Anti-Spoofing Using Texture Based Method

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