scholarly journals Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 955
Author(s):  
Zhiyuan Li ◽  
Ershuai Peng
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Dynamic Network ◽  
Parameter Tuning ◽  
Global Optimum ◽  
Research Field ◽  
Edge Computing ◽  
Transmission Delay ◽  
Smart Vehicles ◽  
Network Bandwidth

With the development of smart vehicles and various vehicular applications, Vehicular Edge Computing (VEC) paradigm has attracted from academic and industry. Compared with the cloud computing platform, VEC has several new features, such as the higher network bandwidth and the lower transmission delay. Recently, vehicular computation-intensive task offloading has become a new research field for the vehicular edge computing networks. However, dynamic network topology and the bursty computation tasks offloading, which causes to the computation load unbalancing for the VEC networking. To solve this issue, this paper proposed an optimal control-based computing task scheduling algorithm. Then, we introduce software defined networking/OpenFlow framework to build a software-defined vehicular edge networking structure. The proposed algorithm can obtain global optimum results and achieve the load-balancing by the virtue of the global load status information. Besides, the proposed algorithm has strong adaptiveness in dynamic network environments by automatic parameter tuning. Experimental results show that the proposed algorithm can effectively improve the utilization of computation resources and meet the requirements of computation and transmission delay for various vehicular tasks.

scholarly journals Computation Offloading Strategy in Mobile Edge Computing

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 191 ◽  
Author(s):  
Jinfang Sheng ◽  
Jie Hu ◽  
Xiaoyu Teng ◽  
Bin Wang ◽  
Xiaoxia Pan
Keyword(s):  
Decision Making ◽  
User Experience ◽  
Task Scheduling ◽  
Mobile Internet ◽  
Edge Computing ◽  
Computation Offloading ◽  
Smart Devices ◽  
Mobile Edge Computing ◽  
Network Bandwidth ◽  
Intelligent Devices

Mobile phone applications have been rapidly growing and emerging with the Internet of Things (IoT) applications in augmented reality, virtual reality, and ultra-clear video due to the development of mobile Internet services in the last three decades. These applications demand intensive computing to support data analysis, real-time video processing, and decision-making for optimizing the user experience. Mobile smart devices play a significant role in our daily life, and such an upward trend is continuous. Nevertheless, these devices suffer from limited resources such as CPU, memory, and energy. Computation offloading is a promising technique that can promote the lifetime and performance of smart devices by offloading local computation tasks to edge servers. In light of this situation, the strategy of computation offloading has been adopted to solve this problem. In this paper, we propose a computation offloading strategy under a scenario of multi-user and multi-mobile edge servers that considers the performance of intelligent devices and server resources. The strategy contains three main stages. In the offloading decision-making stage, the basis of offloading decision-making is put forward by considering the factors of computing task size, computing requirement, computing capacity of server, and network bandwidth. In the server selection stage, the candidate servers are evaluated comprehensively by multi-objective decision-making, and the appropriate servers are selected for the computation offloading. In the task scheduling stage, a task scheduling model based on the improved auction algorithm has been proposed by considering the time requirement of the computing tasks and the computing performance of the mobile edge computing server. Extensive simulations have demonstrated that the proposed computation offloading strategy could effectively reduce service delay and the energy consumption of intelligent devices, and improve user experience.

scholarly journals Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1666 ◽  
Author(s):  
Shuran Sheng ◽  
Peng Chen ◽  
Zhimin Chen ◽  
Lenan Wu ◽  
Yuxuan Yao
Keyword(s):  
Reinforcement Learning ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Edge Computing ◽  
Scheduling Problem ◽  
Satisfaction Degree ◽  
Markov Decision ◽  
Time Scheduling ◽  
Task Satisfaction

Edge computing (EC) has recently emerged as a promising paradigm that supports resource-hungry Internet of Things (IoT) applications with low latency services at the network edge. However, the limited capacity of computing resources at the edge server poses great challenges for scheduling application tasks. In this paper, a task scheduling problem is studied in the EC scenario, and multiple tasks are scheduled to virtual machines (VMs) configured at the edge server by maximizing the long-term task satisfaction degree (LTSD). The problem is formulated as a Markov decision process (MDP) for which the state, action, state transition, and reward are designed. We leverage deep reinforcement learning (DRL) to solve both time scheduling (i.e., the task execution order) and resource allocation (i.e., which VM the task is assigned to), considering the diversity of the tasks and the heterogeneity of available resources. A policy-based REINFORCE algorithm is proposed for the task scheduling problem, and a fully-connected neural network (FCN) is utilized to extract the features. Simulation results show that the proposed DRL-based task scheduling algorithm outperforms the existing methods in the literature in terms of the average task satisfaction degree and success ratio.

scholarly journals A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Shudong Wang ◽  
Yanqing Li ◽  
Shanchen Pang ◽  
Qinghua Lu ◽  
Shuyu Wang ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Completion Time ◽  
Fitness Function ◽  
Scheduling Algorithm ◽  
Critical Role ◽  
Global Optimum ◽  
Selection Strategy ◽  
Task Completion ◽  
Task Completion Time ◽  
Penalty Factor

Task scheduling plays a critical role in the performance of the edge-cloud collaborative. Whether the task is executed in the cloud and how it is scheduled in the cloud is an important issue. On the basis of satisfying the delay, this paper will schedule tasks on edge devices or cloud and present a task scheduling algorithm for tasks that need to be transferred to the cloud based on the catastrophic genetic algorithm (CGA) to achieve global optimum. The algorithm quantifies the total task completion time and the penalty factor as a fitness function. By improving the roulette selection strategy, optimizing mutation and crossover operator, and introducing cataclysm strategy, the search scope is expanded. Furthermore, the premature problem of the evolutionary algorithm is effectively alleviated. The experimental results show that the algorithm can address the optimal local issue while significantly shortening the task completion time on the basis of satisfying tasks delays.

A Novel Performance Enhancing Task Scheduling Algorithm for Cloud-Based E-Health Environment

2019 ◽  
Vol 10 (2) ◽  
pp. 102-117 ◽  
Author(s):  
Vijayakumar Pandi ◽  
Pandiaraja Perumal ◽  
Balamurugan Balusamy ◽  
Marimuthu Karuppiah
Keyword(s):  
Task Scheduling ◽  
Performance Optimization ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Computation Time ◽  
Cloud Services ◽  
Network Bandwidth ◽  
Task Scheduling Algorithm ◽  
Efficient Resource ◽  
The Cost

The fast-growing internet services have led to the rapid development of storing, retrieving and processing health-related documents from a public cloud. In such a scenario, the performance of cloud services offered is not guaranteed, since it depends on efficient resource scheduling, network bandwidth, etc. The trade-off which lies between the cost and the QoS is that the cost should be variably low on achieving high QoS. This can be done by performance optimization. In order to optimize the performance, a novel task scheduling algorithm is proposed in this article. The main advantage of this proposed scheduling algorithm is to improve the QoS parameters which comprises of metrics such as response time, computation time, availability and cost. The proposed work is simulated in Aneka and shows better performance compared to existing paradigms.

scholarly journals A Survey on Vehicular Edge Computing: Architecture, Applications, Technical Issues, and Future Directions

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Salman Raza ◽  
Shangguang Wang ◽  
Manzoor Ahmed ◽  
Muhammad Rizwan Anwar
Keyword(s):  
Service Providers ◽  
Research Field ◽  
Edge Computing ◽  
Future Research ◽  
Future Directions ◽  
Smart Vehicles ◽  
Close Proximity ◽  
Technical Issues ◽  
Smart Vehicle

A new networking paradigm, Vehicular Edge Computing (VEC), has been introduced in recent years to the vehicular network to augment its computing capacity. The ultimate challenge to fulfill the requirements of both communication and computation is increasingly prominent, with the advent of ever-growing modern vehicular applications. With the breakthrough of VEC, service providers directly host services in close proximity to smart vehicles for reducing latency and improving quality of service (QoS). This paper illustrates the VEC architecture, coupled with the concept of the smart vehicle, its services, communication, and applications. Moreover, we categorized all the technical issues in the VEC architecture and reviewed all the relevant and latest solutions. We also shed some light and pinpoint future research challenges. This article not only enables naive readers to get a better understanding of this latest research field but also gives new directions in the field of VEC to the other researchers.

scholarly journals List Scheduling Algorithm Based on Virtual Scheduling Length Table in Heterogeneous Computing System

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Naqin Zhou ◽  
Xiaowen Liao ◽  
Fufang Li ◽  
Yuanyong Feng ◽  
Liangchen Liu
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Efficient Implementation ◽  
Edge Computing ◽  
List Scheduling ◽  
Start Time ◽  
Current Task

Edge computing needs the close cooperation of cloud computing to better meet various needs. Therefore, ensuring the efficient implementation of applications in cloud computing is not only related to the development of cloud computing itself but also affects the promotion of edge computing. However, resource management and task scheduling strategy are important factors affecting the efficient implementation of applications. Therefore, aiming at the task scheduling problem in cloud computing environment, this paper proposes a new list scheduling algorithm, namely, based on a virtual scheduling length (BVSL) table algorithm. The algorithm first constructs the predicted remaining length table based on the prescheduling results, then constructs a virtual scheduling length table based on the predicted remaining length table, the current task execution cost, and the actual start time of the task, and calculates the task priority based on the virtual scheduling length table to make the overall path the longest task is scheduled first, thus effectively shorten the scheduling length. Finally, the processor is selected for the task based on the predicted remaining length table. The selected processor may not be the earliest for the current task, but it can shorten the finish time of the task in the next phase and reduce the scheduling length. To verify the effectiveness of the scheduling method, experiments were carried out from two aspects: randomly generated graphs and real-world application graphs. Experimental results show that the BVSL algorithm outperforms the latest Improved Predict Priority Task Scheduling (IPPTS) and RE-18 scheduling methods in terms of makespan, scheduling length ratio, speedup, and the number of occurrences of better quality of schedules while maintaining the same time complexity.

scholarly journals Crow Search based Scheduling Algorithm for Load Balancing in Cloud Environment

2019 ◽  
Vol 8 (9) ◽  
pp. 1058-1064
Keyword(s):  
Comparative Analysis ◽  
Load Balancing ◽  
Task Scheduling ◽  
Ant Colony Algorithm ◽  
Scheduling Algorithm ◽  
Research Paper ◽  
Global Optimum ◽  
Ant Colony ◽  
Load Balancing Algorithm ◽  
Optimum Solution

Task scheduling is a vital aspect in computer science, as it is the essence of how a computer executes various tasks and performs the related activities with accuracy and efficiency. In cloud computing, task scheduling is a typical NP-hard problem and scientists have been attempting to handle this problem for quite a long time. Although it is anything but difficult to accomplish a global optimum solution utilizing the ant colony algorithm and achieve promising outcomes. This research paper proposes a crow search based load balancing algorithm (CSLBA) for multi-objective task scheduling environment which concentrates on allocating best suitable resources for the task to be implemented with the consideration of various parameters like average makespan time (AMT), average waiting time (AWT) and average data center processing time (ADCPT). The present work provides a comparative analysis of proposed algorithm and Ant Colony Optimization based load balancing algorithm (ACOLBA). The experimentation is performed in steps and comparative analysis proved that proposed CSLBA is the optimal technique among the other scheduling technique considered in this research paper.

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