A big data placement method using NSGA-III in meteorological cloud platform

Ruan, Feng; Gu, Renhao; Huang, Tao; Xue, Shengjun

doi:10.1186/s13638-019-1456-7

Cited by 11 publications

(3 citation statements)

References 40 publications

(54 reference statements)

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“…To further saving energy consumption, in our previous work [19], a novel three thresholds framework was proposed used for VM deployment to achieve energy saving within data centers. Some of them use the weighted sum [20] and normalization [21], [22] approaches to obtain a single optimal solution for addressing the energy-aware IoT-based techniques in CDC. Recently, the work [23] introduces a new priority, power and traffic-aware VM placement algorithm that aim to minimize the energy usage of the servers of incoming IoT traffics.…”

Section: Energy-aware Iot-based Techniquesmentioning

confidence: 99%

AFED-EF: An Energy-Efficient VM Allocation Algorithm for IoT Applications in a Cloud Data Center

Zhou

Shojafar

Alazab

et al. 2021

IEEE Trans. on Green Commun. Netw.

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Cloud Data Centers (CDCs) have become a vital computing infrastructure for enterprises. However, CDCs consume substantial energy due to the increased demand for computing power, especially for the Internet of Things (IoT) applications. Although a great deal of research in green resource allocation algorithms have been proposed to reduce the energy consumption of the CDCs, existing approaches mostly focus on minimizing the number of active Physical Machines (PMs) and rarely address the issue of load fluctuation and energy efficiency of the Virtual Machine (VM) provisions jointly. Moreover, existing approaches lack mechanisms to consider and redirect the incoming traffics to appropriate resources to optimize the Quality of Services (QoSs) provided by the CDCs. We propose a novel adaptive energy-aware VM allocation and deployment mechanism called AFED-EF for IoT applications to handle these problems. The proposed algorithm can efficiently handle the fluctuation of load and has good performance during the VM allocation and placement. We carried out extensive experimental analysis using a real-world workload based on more than a thousand PlanetLab VMs. The experimental results illustrate that AFED-EF outperforms other energy-aware algorithms in energy consumption, Service Level Agreements (SLA) violation, and energy efficiency.

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Section: Energy-aware Iot-based Techniquesmentioning

confidence: 99%

AFED-EF: An Energy-Efficient VM Allocation Algorithm for IoT Applications in a Cloud Data Center

Zhou

Shojafar

Alazab

et al. 2021

IEEE Trans. on Green Commun. Netw.

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“…Since the multi-channel-projection based field reconstructive method has been classified as MOOP, NSGA-III is chosen to solve this problem [13]. There are two significant points in implement of the algorithm.…”

Section: Implement Of Nsga-iii To the Reconstructive Algorithmmentioning

confidence: 99%

“…Compare D prj with standard projections D s in the first step, to get AE (i, j) and SE (i, j) matrix with equations (13), where i represents the number of one channel and j represents the number of one pixel in channel i.…”

Section: B Application Of Nsga-iii In the Reconstructive Algorithmmentioning

confidence: 99%

Simulation of an NSGA-III Based Fireball Inner-Temperature-Field Reconstructive Method

et al. 2020

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For the inner-temperature-field reconstruction of a fireball, a detecting method was proposed, using multi-channel visible spectral remote sensing theory. In our proposed method, the reconstructive algorithm based on multi-channel-detection was considered as a multi-objective optimization problem (MOOP), and a fast non-dominated sorting genetic algorithm based on reference-point strategy (NSGA-III) was employed as the solution of this problem. Besides, a so-called ambient pressure operator, based on the unique detecting model, was proposed and employed during the iteration process, for dynamic genetic parameter adjustment. To verify some performance of our proposed method, several numerical reconstructive simulations were carried out, from simple GA to NSGA-III, using several artificial 2-D virtual data, with different kinds of crossover and mutation functions. The simulation results show that, limited to our problems, the NSGA-III can effectively reconstruct different 2-D data, by a fixed crossover rate and a dynamic mutation rate under the proposed ambient pressure operator and an adaptive mutation rate function. The algorithm, limited to the field-reconstruction, also performs well on stability, but still has some deficiencies to be optimized.

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Improved quality of service with fuzzy-based optimised resource allocation with energy consumption-based IoT network application

Chandarapu,

Kasa

2023

Soft Comput

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A big data placement method using NSGA-III in meteorological cloud platform

Cited by 11 publications

References 40 publications

AFED-EF: An Energy-Efficient VM Allocation Algorithm for IoT Applications in a Cloud Data Center

AFED-EF: An Energy-Efficient VM Allocation Algorithm for IoT Applications in a Cloud Data Center

Simulation of an NSGA-III Based Fireball Inner-Temperature-Field Reconstructive Method

Improved quality of service with fuzzy-based optimised resource allocation with energy consumption-based IoT network application

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