scholarly journals Optimization-Based Resource Management Algorithms with Considerations of Client Satisfaction and High Availability in Elastic 5G Network Slices

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1882
Author(s):  
Chiu-Han Hsiao ◽  
Frank Yeong-Sung Lin ◽  
Evana Szu-Han Fang ◽  
Yu-Fang Chen ◽  
Yean-Fu Wen ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Programming Model ◽  
Optimal Level ◽  
High Availability ◽  
Management Approach ◽  
Cloud Computing Environment ◽  
5G Network ◽  
Efficiency And Effectiveness ◽  
Resource Management Algorithms

A combined edge and core cloud computing environment is a novel solution in 5G network slices. The clients’ high availability requirement is a challenge because it limits the possible admission control in front of the edge cloud. This work proposes an orchestrator with a mathematical programming model in a global viewpoint to solve resource management problems and satisfying the clients’ high availability requirements. The proposed Lagrangian relaxation-based approach is adopted to solve the problems at a near-optimal level for increasing the system revenue. A promising and straightforward resource management approach and several experimental cases are used to evaluate the efficiency and effectiveness. Preliminary results are presented as performance evaluations to verify the proposed approach’s suitability for edge and core cloud computing environments. The proposed orchestrator significantly enables the network slicing services and efficiently enhances the clients’ satisfaction of high availability.

Cloud Computing-Based Socially Important Locations Discovery on Social Media Big Datasets

2020 ◽  
Vol 19 (02) ◽  
pp. 469-497 ◽  
Author(s):  
Ahmet Sakir Dokuz ◽  
Mete Celik
Keyword(s):  
Social Media ◽  
Cloud Computing ◽  
Local Governments ◽  
Programming Model ◽  
Computing Systems ◽  
Apache Hadoop ◽  
Cloud Computing Environment ◽  
Emergency Situations ◽  
Hadoop Mapreduce ◽  
Dataset Size

Socially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provides valuable information, such as which locations are frequently visited by a social media user, which locations are common for a social media user group, and which locations are socially important for a group of urban area residents. However, discovering socially important locations is challenging due to huge volume, velocity, and variety of social media datasets, inefficiency of current interest measures and algorithms on social media big datasets, and the need of massive spatial and temporal calculations for spatial social media analyses. In contrast, cloud computing provides infrastructure and platforms to scale compute-intensive jobs. In the literature, limited number of studies related to socially important locations discovery takes into account cloud computing systems to scale increasing dataset size and to handle massive calculations. This study proposes a cloud-based socially important locations discovery algorithm of Cloud SS-ILM to handle volume and variety of social media big datasets. In particular, in this study, we used Apache Hadoop framework and Hadoop MapReduce programming model to scale dataset size and handle massive spatial and temporal calculations. The performance evaluation of the proposed algorithm is conducted on a cloud computing environment using Turkey Twitter social media big dataset. The experimental results show that using cloud computing systems for socially important locations discovery provide much faster discovery of results than classical algorithms. Moreover, the results show that it is necessary to use cloud computing systems for analyzing social media big datasets that could not be handled with traditional stand-alone computer systems. The proposed Cloud SS-ILM algorithm could be applied on many application areas, such as targeted advertisement of businesses, social media utilization of cities for city planners and local governments, and handling emergency situations.

scholarly journals Scalable Evolutionary Computation for Efficient Information Extraction from Remote Sensed Imagery

2014 ◽  
Vol XL-1 ◽  
pp. 27-34
Author(s):  
L. M. Almutairi ◽  
S. Shetty ◽  
H. G. Momm
Keyword(s):  
Cloud Computing ◽  
Genetic Programming ◽  
Information Extraction ◽  
Evolutionary Computation ◽  
Programming Model ◽  
Computational Cost ◽  
Evolutionary Process ◽  
Extraction Process ◽  
Computing Environment ◽  
Cloud Computing Environment

Evolutionary computation, in the form of genetic programming, is used to aid information extraction process from high-resolution satellite imagery in a semi-automatic fashion. Distributing and parallelizing the task of evaluating all candidate solutions during the evolutionary process could significantly reduce the inherent computational cost of evolving solutions that are composed of multichannel large images. In this study, we present the design and implementation of a system that leverages cloud-computing technology to expedite supervised solution development in a centralized evolutionary framework. The system uses the MapReduce programming model to implement a distributed version of the existing framework in a cloud-computing platform. The proposed system has two major subsystems; (i) data preparation: the generation of random spectral indices; and (ii) distributed processing: the distributed implementation of genetic programming, which is used to spectrally distinguish the features of interest from the remaining image background in the cloud computing environment in order to improve scalability. The proposed system reduces response time by leveraging the vast computational and storage resources in a cloud computing environment. The results demonstrate that distributing the candidate solutions reduces the execution time by 91.58%. These findings indicate that such technology could be applied to more complex problems that involve a larger population size and number of generations.

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