scholarly journals A Novel Resource Allocation and Spectrum Defragmentation Mechanism for IoT-Based Big Data in Smart Cities

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3443 ◽  
Author(s):  
Yuhuai Peng ◽  
Jiaying Wang ◽  
Aiping Tan ◽  
Jingjing Wu
Keyword(s):  
Resource Allocation ◽  
Big Data ◽  
Smart Cities ◽  
Blocking Probability ◽  
Spectrum Utilization ◽  
High Definition ◽  
Protection Mechanism ◽  
Backbone Networks ◽  
Efficient Resource ◽  
Routing And Spectrum Allocation

People’s demand for high-traffic applications and the need for Internet of Things (IoT) are enormous in smart cities. The amount of data generated by virtual reality, high-definition video, and other IoT applications is growing at an exponential rate that far exceeds our expectations, and the types of data are becoming more diverse. It has become critical to reliably accommodate IoT-based big data with reasonable resource allocation in optical backbone networks for smart cities. For the problem of reliable transmission and efficient resource allocation in optical backbone networks, a novel resource allocation and spectrum defragmentation mechanism for massive IoT traffic is presented in this paper. Firstly, a routing and spectrum allocation algorithm based on the distance-adaptive sharing protection mechanism (DASP) is proposed, to obtain sufficient protection and reduce the spectrum consumption. The DASP algorithm advocates applying different strategies to the resource allocation of the working and protection paths. Then, the protection path spectrum defragmentation algorithm based on OpenFlow is designed to improve spectrum utilization while providing shared protection for traffic demands. The lowest starting slot-index first (LSSF) algorithm is employed to remove and reconstruct the optical paths. Numerical results indicate that the proposal can effectively alleviate spectrum fragmentation and reduce the bandwidth-blocking probability by 44.68% compared with the traditional scheme.

A Mobility-Aware Optimal Resource Allocation Architecture for Big Data Task Execution on Mobile Cloud in Smart Cities

2018 ◽  
Vol 56 (2) ◽  
pp. 110-117 ◽  
Author(s):  
Asma Enayet ◽  
Md. Abdur Razzaque ◽  
Mohammad Mehedi Hassan ◽  
Atif Alamri ◽  
Giancarlo Fortino
Keyword(s):  
Resource Allocation ◽  
Big Data ◽  
Smart Cities ◽  
Mobile Cloud ◽  
Task Execution ◽  
Optimal Resource Allocation ◽  
Optimal Resource

Research on CLIB Routing and Spectrum Allocation Algorithm in Elastic Optical Networks

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Li Li ◽  
Zhai Ya-Fang ◽  
Li Hong-Jie
Keyword(s):  
Optical Networks ◽  
Blocking Probability ◽  
Rapid Development ◽  
Optical Network ◽  
Spectrum Allocation ◽  
Mobile Internet ◽  
High Definition ◽  
Allocation Algorithm ◽  
High Bandwidth ◽  
Routing And Spectrum Allocation

AbstractWith the rapid development of mobile Internet, high-definition video and cloud computing, users’ bandwidth demands are not only larger and larger but also more and more diverse. To solve this problem, there searchers put forward the concept of elastic optical network (EON). EON adopts the transmission mode of elastic grid, which can allocate spectrum resources flexibly and meet high bandwidth and diversity requirements at the same time. Routing and spectrum allocation (RSA) is an important issue in EON. In this paper, we present a heuristic algorithm named constrained-lower-indexed-block (CLIB) allocation algorithm for the RSA problem. The algorithm is based on the K candidate paths. When there are available spectrum blocks on multiple candidate paths, if the increase of the path length does not exceed a given threshold, the lower index spectrum would be selected for the connection request on a longer path. The aim of the algorithm is to concentrate the occupied frequency slices on one side of the spectrum and leave another side of the spectrum to the later arrived connection requests as much as possible, to reduce the blocking probability of connection requests. Simulation results show that comparing with the first-last-fit and hybrid grouping algorithms, the CLIB algorithm can reduce the blocking probability of connection requests.

scholarly journals Resource Allocation in Heterogeneous Buffered Cognitive Radio Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
B. S. Awoyemi ◽  
B. T. Maharaj ◽  
A. S. Alfa
Keyword(s):  
Resource Allocation ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Blocking Probability ◽  
Radio Networks ◽  
Base Station ◽  
Limiting Factor ◽  
Efficient Resource ◽  
Overall Performance ◽  
Performance Results

Resources available for operation in cognitive radio networks (CRN) are generally limited, making it imperative for efficient resource allocation (RA) models to be designed for them. However, in most RA designs, a significant limiting factor to the RA’s productivity has hitherto been mostly ignored, the fact that different users or user categories do have different delay tolerance profiles. To address this, in this paper, an appropriate RA model for heterogeneous CRN with delay considerations is developed and analysed. In the model, the demands of users are first categorised and then, based on the distances of users from the controlling secondary user base station and with the assumption that the users are mobile, the user demands are placed in different queues having different service capacities and the resulting network is analysed using queueing theory. Furthermore, to achieve optimality in the RA process, an important concept is introduced whereby some demands from one queue are moved to another queue where they have a better chance of enhanced service, thereby giving rise to the possibility of an improvement in the overall performance of the network. The performance results obtained from the analysis, particularly the blocking probability and network throughput, show that the queueing model incorporated into the RA process can help in achieving optimality for the heterogeneous CRN with buffered data.

Efficient Resource Allocation for Multiclass Services in Multiuser OFDM Systems

2009 ◽  
Vol E92-B (2) ◽  
pp. 533-543 ◽  
Author(s):  
Jae Soong LEE ◽  
Jae Young LEE ◽  
Soobin LEE ◽  
Hwang Soo LEE
Keyword(s):  
Resource Allocation ◽  
Ofdm Systems ◽  
Efficient Resource ◽  
Multiuser Ofdm

Big Data Tools, Techniques and Technologies Looking at Insights in Smart Cities

2015 ◽  
Author(s):  
Fahimeh Tabatabaei ◽  
Tahir Wani ◽  
Nastran Hajiheidari
Keyword(s):  
Big Data ◽  
Smart Cities

Resource Allocation in Cloud using Multi Bidding Model with User Centric Behavior Analysis

2020 ◽  
Vol 13 (5) ◽  
pp. 1008-1019
Author(s):  
N. Vijayaraj ◽  
T. Senthil Murugan
Keyword(s):  
Resource Allocation ◽  
Behavior Analysis ◽  
User Behavior ◽  
Low Cost ◽  
Cloud Services ◽  
Online Auction ◽  
Analysis Model ◽  
Efficient Resource ◽  
User Centric ◽  
Cloud Resources

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.

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