Efficient multi-attribute precedence-based task scheduling for edge computing in geo-distributed cloud environment

2022 ◽  
Author(s):  
Chunlin Li ◽  
Chaokun Zhang ◽  
Bingbin Ma ◽  
Youlong Luo
Keyword(s):  
Task Scheduling ◽  
Edge Computing ◽  
Cloud Environment ◽  
Distributed Cloud

scholarly journals Container-based task scheduling for edge computing in IoT-cloud environment using improved HBF optimisation algorithm

2020 ◽  
Vol 13 (1) ◽  
pp. 85
Author(s):  
Srichandan Sobhanayak ◽  
Kavita Jaiswal ◽  
Ashok Kumar Turuk ◽  
Bibhudatta Sahoo ◽  
Bhabendu Kumar Mohanta ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Edge Computing ◽  
Cloud Environment ◽  
Optimisation Algorithm

Container-based task scheduling for edge computing in IoT-cloud environment using improved HBF optimisation algorithm

2020 ◽  
Vol 13 (1) ◽  
pp. 85
Author(s):  
Bhabendu Kumar Mohanta ◽  
Bibhudatta Sahoo ◽  
Ashok Kumar Turuk ◽  
Srichandan Sobhanayak ◽  
Kavita Jaiswal ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Edge Computing ◽  
Cloud Environment ◽  
Optimisation Algorithm

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

Task Scheduling in cloud using ACO

2020 ◽  
Vol 13 ◽  
Author(s):  
Yuvaraj Natarajan ◽  
Srihari Kannan ◽  
Gaurav Dhiman
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Limited Resource ◽  
Cloud Environment ◽  
Clear Cut ◽  
Strategic Approach ◽  
User Demand ◽  
Scheduling Strategies ◽  
Cloud Users ◽  
And Task

Background: Cloud computing is a multi-tenant model for computation that offers various features for computing and storage based on user demand. With increasing cloud users, the usage increases that highlights the problem of load balancing with limited resource availability based on dynamic cloud environment. In such cases, task scheduling creates fundamental issue in cloud environment. Introduction: Certain problems such as, inefficiencies in load balancing latency, throughput ratio, proper utilization of the cloud resources, better energy consumption and response time have been observed. These drawbacks can be efficiently resolved through the incorporation of efficient load balancing and task scheduling strategies. Method: In this paper, we develop an efficient co-operative method to solve the most recent approaches against load balancing and task scheduling have been proposed using Ant Colony Optimization (ACO). These approaches enables in the clear cut identification of the problems associated with the load balancing and task scheduling strategies in the cloud environment. Results: The simulation is conducted to find the efficacy of the improved ACO system for load balancing in cloud than the other methods. The result shows that the proposed method obtains reduced execution time, reduced cost and delay. Conclusion: A unique strategic approach is developed in this paper, Load Balancing, which works with the ACO in relation to the cloud workload balancing task through the incorporation of the ACO technique. The strategy for determining the applicant nodes is based on which the load balancing approach would essentially depend. By incorporating two different approaches: the maximum minute rules and the forward-backward ant, this reliability task can be established. This method is intended to articulate the initialization of the pheromone and thus upgrade the relevant cloud-based physical properties.

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.

Adaptive priority-based data placement and multi-task scheduling in geo-distributed cloud systems

2021 ◽  
Vol 224 ◽  
pp. 107050
Author(s):  
Chunlin Li ◽  
Jun Liu ◽  
Weigang Li ◽  
Youlong Luo
Keyword(s):  
Task Scheduling ◽  
Data Placement ◽  
Cloud Systems ◽  
Distributed Cloud

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
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