scholarly journals Deep Learning-Based Service Scheduling Mechanism for GreenRSUs in the IoVs

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jitong Li ◽  
Chao Wang ◽  
Daehee Seo ◽  
Xiaoman Cheng ◽  
Yunhua He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Scheduling Algorithm ◽  
Traffic Prediction ◽  
Internet Of Vehicles ◽  
Communication Scheduling ◽  
Service Scheduling ◽  
Service Efficiency ◽  
High Power Consumption ◽  
Scheduling Mechanisms ◽  
Simulation Results

Green roadside units (RSUs), also called renewable energy-powered RSUs, are utilized recently rather than the traditional electric-powered RSUs with high power consumption and the large infrastructure deployment cost in the Internet of vehicles (IoVs). However, the power of the green RSUs is limited and unstable, which is affected by the battery size and charging environment. Therefore, a big challenge to deploy green RSUs in the IoVs is to schedule their service process properly, in order to extend the service efficiency of RSUs. In this paper, a deep learning-based communication scheduling mechanism is proposed regarding the service scheduling problem. In particular, a three-part scheduling algorithm consisting of RSU clustering, deep learning-based traffic prediction, and a vehicle access scheduling algorithm is presented to maximize the service number of vehicles and minimize the energy cost. An extensive simulation is done, and the simulation results indicate that our algorithm can serve more vehicles with less energy consumption compared with other scheduling mechanisms under different scenarios.

QoS aware Multi-Path Routing using Link Scheduling Algorithm

2019 ◽  
Vol 09 ◽  
Author(s):  
Satyasrikanth Palle ◽  
Shivashankar
Keyword(s):  
Cellular Networks ◽  
Scheduling Algorithm ◽  
Research Work ◽  
Scheduling Algorithms ◽  
Turnaround Time ◽  
Link Scheduling ◽  
Spatial Reuse ◽  
Mobile Cellular ◽  
Simulation Results ◽  
End To End

Objective: The demand for Cellular based multimedia services is growing day by day, in order to fulfill such demand the present day cellular networks needs to be upgraded to support excessive capacity calls along with high data accessibility. Analysis of traffic and huge network size could become very challenging issue for the network operators for scheduling the available bandwidth between different users. In the proposed work a novel QoS Aware Multi Path scheduling algorithm for smooth CAC in wireless mobile networks. The performance of the proposed algorithm is assessed and compared with existing scheduling algorithms. The simulation results show that the proposed algorithm outperforms existing CAC algorithms in terms of throughput and delay. The CAC algorithm with scheduling increases end-to-end throughput and decreases end-to-end delay. Methods: The key idea to implement the proposed research work is to adopt spatial reuse concept of wireless sensor networks to mobile cellular networks. Spatial reusability enhances channel reuse when the node pairs are far away and distant. When Src and node b are communicating with each other, the other nodes in the discovered path should be idle without utilizing the channel. Instead the other nodes are able to communicate parallelly the end-to-end throughput can be improved with acceptable delay. Incorporating link scheduling algorithms to this key concept further enhances the end-to-end throughput with in the turnaround time. So, in this research work we have applied spatial reuse concept along with link scheduling algorithm to enhance end-to-end throughput with in turnaround time. The proposed algorithm not only ensures that a connection gets the required bandwidth at each mobile node on its way by scheduling required slots to meet the QoS requirements. By considering the bandwidth requirement of the mobile connections, the CAC module at the BS not only considers the bandwidth requirement but also conforming the constrains of system dealy and jitter are met. Result: To verify the feasibility and effectiveness of our proposed work, with respect to scheduling the simulation results clearly shows the throughput improvement with Call Admission Control. The number of dropped calls is significantly less and successful calls are more with CAC. The percentage of dropped calls is reduced by 9 % and successful calls are improved by 91%. The simulation is also conducted on time constraint and ratio of dropped calls are shown. The total time taken to forward the packets and the ration of dropped calls is less when compared to non CAC. On a whole the CAC with scheduling algorithms out performs existing scheduling algorithms. Conclusion: In this research work we have proposed a novel QoS aware scheduling algorithm that provides QoS in Wireless Cellular Networks using Call Admission Control (CAC). The simulation results show that the end-to-end throughput has been increased by 91% when CAC is used. The proposed algorithm is also compared with existing link scheduling algorithms. The results reveal that CAC with scheduling algorithm can be used in Mobile Cellular Networks in order to reduce packet drop ratio. The algorithm is also used to send the packets within acceptable delay.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1400
Author(s):  
Muhammad Adnan ◽  
Jawaid Iqbal ◽  
Abdul Waheed ◽  
Noor Ul Amin ◽  
Mahdi Zareei ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Traffic Management ◽  
Vehicular Networks ◽  
Scheduling Algorithm ◽  
Main Research ◽  
First Come First Serve ◽  
Computing Framework ◽  
Simulation Results

Modern vehicles are equipped with various sensors, onboard units, and devices such as Application Unit (AU) that support routing and communication. In VANETs, traffic management and Quality of Service (QoS) are the main research dimensions to be considered while designing VANETs architectures. To cope with the issues of QoS faced by the VANETs, we design an efficient SDN-based architecture where we focus on the QoS of VANETs. In this paper, QoS is achieved by a priority-based scheduling algorithm in which we prioritize traffic flow messages in the safety queue and non-safety queue. In the safety queue, the messages are prioritized based on deadline and size using the New Deadline and Size of data method (NDS) with constrained location and deadline. In contrast, the non-safety queue is prioritized based on First Come First Serve (FCFS) method. For the simulation of our proposed scheduling algorithm, we use a well-known cloud computing framework CloudSim toolkit. The simulation results of safety messages show better performance than non-safety messages in terms of execution time.

Parallel Scheduling Algorithms Investigation of Support Strict Resource Reservation from Grid

2014 ◽  
Vol 519-520 ◽  
pp. 108-113 ◽  
Author(s):  
Jun Chen ◽  
Bo Li ◽  
Er Fei Wang
Keyword(s):  
Parallel Computing ◽  
Resource Utilization ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Resource Reservation ◽  
Parallel Scheduling ◽  
Scheduling Model ◽  
Computing Grid ◽  
Simulation Results ◽  
Reservation Request

This paper studies resource reservation mechanisms in the strict parallel computing grid,and proposed to support the parallel strict resource reservation request scheduling model and algorithms, FCFS and EASY backfill analysis of two important parallel scheduling algorithm, given four parallel scheduling algorithms supporting resource reservation. Simulation results of four algorithms of resource utilization, job bounded slowdown factor and the success rate of Advanced Reservation (AR) jobs were studied. The results show that the EASY backfill + firstfit algorithm can ensure QoS of AR jobs while taking into account the performance of good non-AR jobs.

Feedback Controlled Adaptive Bandwidth Server Scheduling and its Application in NCS

2014 ◽  
Vol 631-632 ◽  
pp. 761-765
Author(s):  
Jing Cun Bi ◽  
Qi Li ◽  
Wei Jun Yang ◽  
Yan Fei Liu
Keyword(s):  
Control Systems ◽  
Scheduling Algorithm ◽  
Network Control ◽  
Periodic Tasks ◽  
Aperiodic Tasks ◽  
Network Control Systems ◽  
Feedback Scheduling ◽  
Simulation Results ◽  
Adaptive Bandwidth ◽  
Keyword Network

As for the aperiodic tasks of node in network control systems, the FC-ABS (Feedback Controlled Adaptive Bandwidth Server) scheduling algorithm is designed. The different scheduling methods are used according to time characteristics of aperiodic tasks, and feedback scheduling is used to mitigate the effect of aperiodic tasks on periodic tasks. The simulation results show that the method is effective. Keyword: Network Control Systems; Server Scheduling; Feedback Scheduling; FC-ABS.

scholarly journals Low–High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification

2019 ◽  
Vol 16 (5) ◽  
pp. 776-780 ◽  
Author(s):  
Juan M. Haut ◽  
Sergio Bernabe ◽  
Mercedes E. Paoletti ◽  
Ruben Fernandez-Beltran ◽  
Antonio Plaza ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Image Classification ◽  
High Power ◽  
Hyperspectral Image ◽  
Learning Models ◽  
Hyperspectral Image Classification ◽  
High Power Consumption

scholarly journals Randomized scheduling algorithm for virtual output queuing switch at the presence of non-uniform traffic

2019 ◽  
Vol 8 (1) ◽  
pp. 50
Author(s):  
Ali Ghiasian ◽  
Majid Jamali
Keyword(s):  
Randomized Algorithms ◽  
High Speed ◽  
Time Slot ◽  
Scheduling Algorithm ◽  
Superior Performance ◽  
High Complexity ◽  
Maximum Weight ◽  
Switch Architecture ◽  
Simulation Results ◽  
Maximum Weight Matching

<span>Virtual Output Queuing (VOQ) is a well-known queuing discipline in data switch architecture that eliminates Head Of Line (HOL) blocking issue. In VOQ scheme, for each output port, a separate FIFO is maintained by each input port. Consequently, a scheduling algorithm is required to determine the order of service to virtual queues at each time slot. Maximum Weight Matching (MWM) is a well-known scheduling algorithm that achieves the entire throughput region. Despite of outstanding attainable throughput, high complexity of MWM makes it an impractical algorithm for implementation in high-speed switches. To overcome this challenge, a number of randomized algorithms have been proposed in the literature. But they commonly perform poorly when input traffic does not uniformly select output ports. In this paper, we propose two randomized algorithms that outperform the well-known formerly proposed solutions. We exploit a method to keep a parametric number of heavy edges from the last time matching and mix it by randomly generated matching to produce a new schedule. Simulation results confirm the superior performance of the proposed algorithms.</span>

scholarly journals Optimal Deep Learning based Data Classification Model for Type-2 Diabetes Mellitus Diagnosis and Prediction System

2020 ◽  
Vol 9 (3) ◽  
pp. 1596-1604
Keyword(s):  
Deep Learning ◽  
Data Classification ◽  
Research Area ◽  
Classification Model ◽  
Prediction System ◽  
Pima Indians ◽  
Significant Research ◽  
Diabetes Mellitus Diagnosis ◽  
Simulation Results

In recent days, deep learning models become a significant research area because of its applicability in diverse domains. In this paper, we employ an optimal deep neural network (DNN) based model for classifying diabetes disease. The DNN is employed for diagnosing the patient diseases effectively with better performance. To further improve the classifier efficiency, multilayer perceptron (MLP) is employed to remove the misclassified instance in the dataset. Then, the processed data is again provided as input to the DNN based classification model. The use of MLP significantly helps to remove the misclassified instances. The presented optimal data classification model is experimented on the PIMA Indians Diabetes dataset which holds the medical details of 768 patients under the presence of 8 attributes for every record. The obtained simulation results verified the superior nature of the presented model over the compared methods.

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