Towards Green Service Composition Approach in the Cloud

Wang, Shangguang; Zhou, Aoying; Bao, Ruo; Wu, Chang; Yau, Stephen S.

doi:10.1109/tsc.2018.2868356

Cited by 34 publications

(15 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NSGA-III is an accurate and robust method for addressing the optimization problem with multiple objectives. Here, NSGA-III is adopted to solve the multi-objective optimization problem presented in [18]. Firstly, we encode for the physical machines and then give the fitness function and constraints for the multiobjective optimization problem.…”

Section: Data Placement Based On Nsga-iiimentioning

confidence: 99%

“…On the one hand, the energy consumption of MCP is greatly increased in order to execute the meteorological task requests, which results in a great cost for the managers of the MCP [17]. On the other hand, how to solve the task requests accurately and timely is another problem of the MCP, which is closely related to the Quality of Service [18]. Hence, the MCP needs a data placement method that takes into account both energy consumption and data access time [19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ruan

Huang³

et al. 2019

J Wireless Com Network

View full text Add to dashboard Cite

Meteorological cloud platforms (MCP) are gradually replacing the traditional meteorological information systems to provide information analysis services such as weather forecasting, disaster warning, and scientific research. However, the explosive growth of meteorological data resources has brought new challenges to the placement and management of big data in MCP. On the one hand, managers of MCP need to save energy to achieve cost savings. On the other hand, users need shorter data access time to improve user's experience. Hence, a big data placement method in MCP is proposed in this paper to deal with challenges above. First, the resource utilization, the data access time, and the energy consumption in MCP with the fat-tree topology are analyzed. Then, a corresponding data placement method, using the improved non-dominated sorting genetic algorithm III (NSGA-III), is designed to optimize the resource usage, energy saving, and efficient data access. Finally, extensive experimental evaluations validate the efficiency and effectiveness of our proposed method.

show abstract

Section: Data Placement Based On Nsga-iiimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ruan

Huang³

et al. 2019

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…However, the measurement network system is subject to many factors such as low density and the complexity of the precipitation phenomena would lead to a large error [10][11][12]. With the advantage of the wide measurement range, high spatial and temporal resolution and the real-time data transmission, the ground radar has been widely applied in meteorological industry, including precipitation estimation [13][14][15]. The traditional method employed in precipitation estimation is Z-R relationship model which utilizes the radar echo intensity and rainfall intensity to establish an equation to calculate the precipitation [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

Tian¹,

Lei²,

Liu³

et al. 2020

J Cloud Comp

View full text Add to dashboard Cite

Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84% and 82.30% lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75% compared to that with BPNN.

show abstract

“…In order to overcome the huge delay caused by remote application migration, a small cloud called "cloudlet" is deployed to a near place to provide enhanced cloud services for users [7][8][9]. The cloudlets can be divided into two types: first, users connect the small servers provided by network operators to provide small servers, through the access to access points (AP), and this is self-organization [2,10,11]; second, the cloudlets that provide the formation of idle resources through a number of P2P networks under the mobile devices [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

ECPM: an energy-efficient cloudlet placement method in mobile cloud environment

Shen

Xue

2019

J Wireless Com Network

View full text Add to dashboard Cite

The development of mobile cloud computing has greatly improved the computing and storage performance of mobile devices. And mobile cloud computing is undoubtedly the necessary way to solve the performance of the process for mobile applications with high performance requirements. However, migrating the mobile applications to the cloud brings about a migration delay, which is intolerable for high real-time demanding applications. This can be technically achieved by expanding mobile cloudlets, co-located with access points (AP). Then, how to deploy the cloudlet to reduce the energy consumption has become a part of major challenges in the current study. In this paper, we propose an energy-efficient cloudlet placement method, named ECPM, to effectively reduce the number of cloudlets, so as to achieve the energy savings. Specifically, the clustering of mobile devices at each time is obtained, and then the cloudlet will move to the clustering to achieve the best use of energy. Finally, the experimental results demonstrate that the proposed method is energy saving.

show abstract

Towards Green Service Composition Approach in the Cloud

Cited by 34 publications

References 47 publications

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

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

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

ECPM: an energy-efficient cloudlet placement method in mobile cloud environment

Contact Info

Product

Resources

About