scholarly journals Intelligent Scheduling Method Supporting Stadium Sharing

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lei Fang
Keyword(s):  
Task Scheduling ◽  
User Satisfaction ◽  
Mobile Terminal ◽  
Load Level ◽  
Edge Computing ◽  
Average System ◽  
Calculation Results ◽  
Lower Load ◽  
Scheduling Method ◽  
Intelligent Scheduling

At present, the fast-paced work and life make people under great pressure, and people’s enthusiasm for fitness is getting higher and higher, which is in contradiction with the shortage of existing stadiums. So it is considerably significant to open shared stadiums near where citizens live for booking. Therefore, how to allow citizens to book a suitable stadium according to their own needs through mobile phones or computers is an urgent problem to be solved. The booking of the shared stadium can be regarded as a mobile edge computing (MEC) scenario, and the problem can be transformed into task scheduling research under MEC through intelligent scheduling method. When using edge computing (EC) technology for service calculation, the mobile terminal needs to offload the service to the edge computing server. After the server completes the calculation, the calculation results will be sent back to the mobile terminal. Therefore, the calculation time and system energy consumption in the calculation process can be further reduced through task scheduling to improve user satisfaction. In this study, joint scheduling of service caching and task algorithm is proposed to reduce the latency of booking shared stadium request and improve user experience. The simulation results show that the proposed algorithm with edge cooperation idea can achieve lower average system latency at lower load level and can significantly reduce the cloud offloading ratio under low and middle pressure. In addition, the proposed algorithm uses the secondary transfer of more tasks to reduce the pressure of local task running. Finally, the quality of experience (QoE) satisfaction rate under low pressure is guaranteed.

A Task Scheduling Method for Edge Computing in Intelligent Building System

2021 ◽  
Author(s):  
Lingzhi Yi ◽  
Xiaodong Feng ◽  
Xieyi Gao ◽  
Lv Fan ◽  
Huina Song ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Edge Computing ◽  
Intelligent Building ◽  
Scheduling Method ◽  
Building System

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.

Imitation-Learning-Enabled Vehicular Edge Computing: Toward Online Task Scheduling

IEEE Network ◽  
2021 ◽  
Vol 35 (3) ◽  
pp. 102-108
Author(s):  
Laisen Nie ◽  
Xiaojie Wang ◽  
Wentao Sun ◽  
Yongkang Li ◽  
Shengtao Li ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Edge Computing ◽  
Imitation Learning

scholarly journals An Overhead-Optimizing Task Scheduling Strategy for Ad-hoc Based Mobile Edge Computing

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 5609-5622 ◽  
Author(s):  
Li Tianze ◽  
Wu Muqing ◽  
Zhao Min ◽  
Liao Wenxing
Keyword(s):  
Task Scheduling ◽  
Ad Hoc ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Scheduling Strategy

scholarly journals Blockchain-Enhanced Fair Task Scheduling for Cloud-Fog-Edge Coordination Environments: Model and Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Wenjuan Li ◽  
Shihua Cao ◽  
Keyong Hu ◽  
Jian Cao ◽  
Rajkumar Buyya
Keyword(s):  
Task Scheduling ◽  
User Satisfaction ◽  
Chain Structure ◽  
Computing Model ◽  
Blockchain Technology ◽  
Node Distribution ◽  
Proposed Model ◽  
Task Processing ◽  
Scheduling Models ◽  
Different Levels

The cloud-fog-edge hybrid system is the evolution of the traditional centralized cloud computing model. Through the combination of different levels of resources, it is able to handle service requests from terminal users with a lower latency. However, it is accompanied by greater uncertainty, unreliability, and instability due to the decentralization and regionalization of service processing, as well as the unreasonable and unfairness in resource allocation, task scheduling, and coordination, caused by the autonomy of node distribution. Therefore, this paper introduces blockchain technology to construct a trust-enabled interaction framework in a cloud-fog-edge environment, and through a double-chain structure, it improves the reliability and verifiability of task processing without a big management overhead. Furthermore, in order to fully consider the reasonability and load balance in service coordination and task scheduling, Berger’s model and the conception of service justice are introduced to perform reasonable matching of tasks and resources. We have developed a trust-based cloud-fog-edge service simulation system based on iFogsim, and through a large number of experiments, the performance of the proposed model is verified in terms of makespan, scheduling success rate, latency, and user satisfaction with some classical scheduling models.

scholarly journals ROA-CONS: Raccoon Optimization for Job Scheduling

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2270
Author(s):  
Sina Zangbari Koohi ◽  
Nor Asilah Wati Abdul Hamid ◽  
Mohamed Othman ◽  
Gafurjan Ibragimov
Keyword(s):  
User Satisfaction ◽  
High Performance ◽  
Job Scheduling ◽  
Response Times ◽  
Scheduling Algorithm ◽  
Desktop Computer ◽  
Network Heterogeneity ◽  
Parallel Job Scheduling ◽  
Scheduling Method ◽  
And Performance

High-performance computing comprises thousands of processing powers in order to deliver higher performance computation than a typical desktop computer or workstation in order to solve large problems in science, engineering, or business. The scheduling of these machines has an important impact on their performance. HPC’s job scheduling is intended to develop an operational strategy which utilises resources efficiently and avoids delays. An optimised schedule results in greater efficiency of the parallel machine. In addition, processes and network heterogeneity is another difficulty for the scheduling algorithm. Another problem for parallel job scheduling is user fairness. One of the issues in this field of study is providing a balanced schedule that enhances efficiency and user fairness. ROA-CONS is a new job scheduling method proposed in this paper. It describes a new scheduling approach, which is a combination of an updated conservative backfilling approach further optimised by the raccoon optimisation algorithm. This algorithm also proposes a technique of selection that combines job waiting and response time optimisation with user fairness. It contributes to the development of a symmetrical schedule that increases user satisfaction and performance. In comparison with other well-known job scheduling algorithms, the simulation assesses the effectiveness of the proposed method. The results demonstrate that the proposed strategy offers improved schedules that reduce the overall system’s job waiting and response times.

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