An Improved Chaotic Bat Swarm Scheduling Learning Model on Edge Computing

Jian, Chengfeng; Chen, Jiawei; Ping, Jing; Zhang, Meiyu

doi:10.1109/access.2019.2914261

Cited by 23 publications

(9 citation statements)

References 27 publications

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“…In Bat algorithm makes a balance between exploration and exploitation with the help of two main parameters which are frequency and emission rate. The wavelength and pulse rate adjusted automatically the distance upon the distance and prey [ 14 ]. A lot of work has already has been published about optimization in cloud computing in the section of VM task allocation system some of them are summaries below.…”

Section: Recent Workmentioning

confidence: 99%

Improvement for tasks allocation system in VM for cloud datacenter using modified bat algorithm

Ullah

Chakir

2022

Multimed Tools Appl

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Since its inception, cloud computing has greatly transformed our lives by connecting the entire world through shared computational resources over the internet. The COVID-19 pandemic has also disrupted the traditional learning and businesses and led us towards an era of cloud-based activities. Virtual machine is one of the main elements of virtualization in cloud computing that represents physical server into the virtual machine. The utilizations of these VM’s are important to achieved effective task scheduling mechanism in cloud environment. This paper focuses on improvment of the task distribution system in VM for cloud computing using load balancing technique. For that reason modification took place at Bat algorithm fitness function value this section used in load balancer section. When algorithm iteration are complete then time to distribute the task among different VM therefore in this section of algorithm was modified. The second modification took place at the search process of Bat at dimension section. The proposed algorithm is known as modified Bat algorithm. Four parameter are used to check the performance of the system which are throughput, makespan, degree of imbalance and processing time. The proposed algorithm provides efficient result as compaire to other standard technique. Hence the proposed algorithm improved cloud data center accuracy and efficiency.

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Section: Recent Workmentioning

confidence: 99%

Improvement for tasks allocation system in VM for cloud datacenter using modified bat algorithm

Ullah

Chakir

2022

Multimed Tools Appl

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“…From the perspective of optimization objective, these studies focus on the scheduling optimization of single or multi-objective, such as optimization of resource utilization [13], scheduling efficiency [14], task completion time [15], QoS [16] and so on. From the perspective of scheduling algorithm, the scheduling optimization based on heuristic algorithm [17]- [22], mathematical model [23]- [25], automata theory [26]- [32], and other algorithms are studied. This paper summarizes the research progress of task scheduling from the perspective of algorithm classification.…”

Section: Related Workmentioning

confidence: 99%

A Self-Adapting Task Scheduling Algorithm for Container Cloud Using Learning Automata

Zhu

Huang

et al. 2021

IEEE Access

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With the rapid development of cloud computing and container technology, more and more applications are deployed to the cloud, and the scale of cloud platform is expanding. Due to the large number of container instances running in the platform, complex dependency relationship, fast version iteration and other characteristics, the update of business can often cause the change of the whole cloud resource environment, which triggers the repetitive scheduling problem of related tasks and affects stability of the business. In this paper, we propose a self-adapting task scheduling algorithm (ADATSA) using learning automata to solve these problems. Firstly, we design a learning automata model and objective function for the system on task scheduling problem. Then, we realize an effective reward-penalty mechanism for scheduling actions in combination with the idle state of resources and the running state of tasks in the current environment. Meanwhile, the environment is modeled by cluster, node and task, and the probability of action selected is optimized by scheduling execution, thus enhancing the adaptability to the cloud environment of the scheduling and accelerating convergence. Finally, we construct a framework of task load monitoring with buffer queue to achieve dynamic scheduling based on priority. The experimental part verifies the effectiveness of proposed algorithm with different angles such as resource imbalance degree, resource residual degree and QoS. Compared with other learning automata scheduling models such as LAEAS, non-automata technology based algorithms such as PSOS and K8S scheduling engine, ADATSA shows the better performance of environment adaptability, resource optimization efficiency and QoS in dynamic scheduling. The theoretical analysis was consistent with the experimental results.

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“…Another study [ 22 ] proposed another edge computing task scheduling model, which transformed the waiting time minimization problem into an overall planning problem, and then carried out optimal scheduling through dynamic programming. A previous study [ 23 ] proposed an improved chaotic bat swarm algorithm. Based on the bat algorithm, chaos factors and second-order oscillation were introduced to accelerate the update of dynamic parameters and thus improve the convergence of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…However, the proposed methods [ 12 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] result in unbalancing resource allocation for vehicular tasks due to dynamic network environment driven by humans. In order to achieve the load-balancing, literatures [ 29 , 30 ] used the idea of software defined network to obtain the more network status parameters.…”

Section: Related Workmentioning

confidence: 99%

Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

Peng

2021

Sensors

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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.

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An Improved Chaotic Bat Swarm Scheduling Learning Model on Edge Computing

Cited by 23 publications

References 27 publications

Improvement for tasks allocation system in VM for cloud datacenter using modified bat algorithm

Improvement for tasks allocation system in VM for cloud datacenter using modified bat algorithm

A Self-Adapting Task Scheduling Algorithm for Container Cloud Using Learning Automata

Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

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