Unified programming concepts for unobtrusive integration of cloud-based and local parallel computing

Mehrabi, Mostafa; Giacaman, Nasser; Sinnen, Oliver

doi:10.1016/j.future.2020.09.024

Cited by 6 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, Zhao et al (2018) used the improved SVM model with a better nonlinear mapping ability to forecast the cloud load and the cloud load time series was initially preprocessed through the chaotic analysis. The improved SVM model reliability is verified by simulation experiments (Calheiros et al, 2015;Mehrabi et al, 2021;Yang et al, 2014). SVM model has strong nonlinear mapping ability, but it is sensitive to the number of samples and only suitable for solving relatively small samples.…”

Section: Literature Reviewmentioning

confidence: 86%

“…Cloud computing is applied to process massive data in a short time on the distributed parallel computing basis, and has strong network service ability (Kim and Jeong, 2017). Achieving efficient resource management has always been the most concerned issue for cloud service operators and providers (Khalilpourazari and Khalilpourazary, 2018;Ye et al, 2019;Mehrabi et al, 2021). Cloud load prediction is necessary for efficient network regulation in the cloud environment (Xu et al, 2017).…”

Section: A Cloud Load Forecasting Model With Nonlinear Changes Using Whale Optimization Algorithm Hybrid Strategy -Extreme Learning Machimentioning

confidence: 99%

See 1 more Smart Citation

A Cloud Load Forecasting Model with Nonlinear Changes using Whale Optimization Algorithm Hybrid Strategy - extreme Learning Machine

Peng¹,

Wen²,

Tseng

et al. 2021

Preprint

View full text Add to dashboard Cite

This study proposes a novel cloud load prediction model and combines hybrid whale optimizer (HWOA) and extreme learning machine (ELM) together for strong nonlinear mapping ability. Accurate cloud load prediction improves the cloud service efficiency and serves as the foundation for network scheme due to traditional linear forecasting models are unable to predict cloud computing resources with nonlinear changes on massive multiplication and cloud computing data complexity, effectively. The proposed cloud load forecasting model is to employ HWOA optimizer to optimize the ELM model random parameters. The contributions of this study are as follows. (1) the HWOA optimizer is to solve the whale optimizer local extremum problem; (2) the proposed HWOA optimizer reduces the ELM random parameters on cloud load forecasting; (3) the convergence performance verifies the benchmark testing functions; and (4) three simulation experiments are conducted to test the cloud load forecast effect. The result indicated that the convergence analysis reveals the HWOA optimizer outperforms the prior optimizers. The proposed cloud load prediction model obtains better forecasting results. The mean absolute percentage error and root mean square error of the proposed model are less than 14% and 11, respectively. Accurate cloud load forecasting lays a foundation for effective deployment of cloud computing resources and maximization of economic benefits.

show abstract

Section: Literature Reviewmentioning

confidence: 86%

Section: A Cloud Load Forecasting Model With Nonlinear Changes Using Whale Optimization Algorithm Hybrid Strategy -Extreme Learning Machimentioning

confidence: 99%

A Cloud Load Forecasting Model with Nonlinear Changes using Whale Optimization Algorithm Hybrid Strategy - extreme Learning Machine

Peng¹,

Wen²,

Tseng

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…For instance, in the course of employing the digital twin in PM solutions, the computational burdens involve so many simultaneous computations that require the initiation of both distributed and parallel computations. The computing task can be accomplished using a dedicated local distributed (or parallel) computing network, fog/edge distributed (or parallel) computing network, distributed (or parallel) cloud computing infrastructures, or the integration of two or more of the platforms [109]. Irrespective of the platform of choice, the computing nodes will communicate with one another to share tasks and resources.…”

Section: High-speed Computational Platformsmentioning

confidence: 99%

Enabling Precision Medicine via Contemporary and Future Communication Technologies: A Survey

2023

View full text Add to dashboard Cite

Precision medicine (PM) is an innovative medical approach that considers differences in the individuals' omics, medical histories, lifestyles, and environmental information in treating diseases. To fully achieve the envisaged gains of PM, various contemporary and future technologies have to be employed, among which are nanotechnology, sensor network, big data, and artificial intelligence. These technologies and other applications require a communication network that will enable them to work in tandem for the benefit of PM. Hence, communication technology serves as the nervous system of PM, without which the entire system collapses. Therefore, it is essential to explore and determine the candidate communication technology requirements that can guarantee the envisioned gains of PM. To the best of our knowledge, no work exploring how communication technology directly impacts the development and deployment of PM solutions exists. This survey paper is designed to stimulate discussions on PM from the communication engineering perspective. We introduce the fundamentals of PM and the demands in terms of quality of service that each of the enabling technologies of PM places on the communication network. We explore the information in the literature to suggest the ideal metric values of the key performance indicators for the implementation of the different components of PM. The comparative analysis of the suitability of the contemporary and future communication technologies for PM implementation is discussed. Finally, some open research challenges for the candidate communication technologies that will enable the full implementation of PM solutions are highlighted.INDEX TERMS Precision medicine, fourth industrial revolution (4IR) technologies, communication technologies, 5G, 6G.

show abstract

A cloud load forecasting model with nonlinear changes using whale optimization algorithm hybrid strategy

2021

View full text Add to dashboard Cite

Unified programming concepts for unobtrusive integration of cloud-based and local parallel computing

Cited by 6 publications

References 17 publications

A Cloud Load Forecasting Model with Nonlinear Changes using Whale Optimization Algorithm Hybrid Strategy - extreme Learning Machine

A Cloud Load Forecasting Model with Nonlinear Changes using Whale Optimization Algorithm Hybrid Strategy - extreme Learning Machine

Enabling Precision Medicine via Contemporary and Future Communication Technologies: A Survey

A cloud load forecasting model with nonlinear changes using whale optimization algorithm hybrid strategy

Contact Info

Product

Resources

About