Optimization of multitask parallel mobile edge computing strategy based on deep learning architecture

Liu, Zong-Kai; Yang, Xiao Qiang; Shen, Jinxing

doi:10.1007/s10617-019-09222-5

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…To clarify the scheduling problem in the cloud computing environment, the task scheduling problem in the cloud computing environment can be described as follows: in the cloud computing environment, n independent subtasks are assigned to M VM nodes for execution [12]. Where task set T = fsubtask 1 , subtask 2 , subtask 3 , ⋯, subtask n g and VM resource node VM = fvm 1 , vm 2 , ⋯, vm m g, subtask j ðj = 1, 2, ⋯, nÞ represent the JTH task, and VM i ði = 1, 2, ⋯, mÞ represent the ith VM resource.…”

Section: Description Of Task Scheduling Problems In Cloudmentioning

confidence: 99%

Research on Optimization Strategy of Task Scheduling Software Based on Genetic Algorithm in Cloud Computing Environment

2022

Wireless Communications and Mobile Computing

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In order to improve the task scheduling strategy, a method based on genetic algorithm in cloud computing environment was proposed. First, the independent task scheduling algorithm and associated task scheduling algorithm commonly used in cloud computing are studied and compared, respectively, and their application characteristics, advantages, and disadvantages are analyzed in detail. Second, an independent task scheduling strategy based on multipopulation genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost, and system resource utilization of task set. The implementation steps of the algorithm are given in detail. Finally, the simulation experiment is carried out on Cloud Sim platform. The experimental results show that computing resource M is 10, subtask N is 2000, population size S is 80, and ETC matrix and RCU array are randomly generated by the system. As the number of iterations increases, the scheduling scheme formed by MCGA and CGA is more obvious and close to the subtask execution cost optimization. Finally, the optimized scheme is basically formed. However, the scheduling scheme formed by TGA has no obvious optimization effect on the subtask execution cost. It is proved that the algorithm proposed in this paper can effectively optimize the task scheduling efficiency and improve the utilization of cloud computing resources at the same time, providing a feasible idea and method for task scheduling in the cloud computing environment.

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Section: Description Of Task Scheduling Problems In Cloudmentioning

confidence: 99%

Research on Optimization Strategy of Task Scheduling Software Based on Genetic Algorithm in Cloud Computing Environment

2022

Wireless Communications and Mobile Computing

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“…In [33], a partitioned fixed-priority real-time scheduling based on dependent tasks split on homogeneous multicore platform was proposed, which converted dependent tasks into a series of sequential jobs and obtained the interrelated subtasks path as well as synthetic deadlines through the B-tree task model. In [34], the authors creatively proposed a deep learning architecture based on tightly connected network and proposed a corresponding multitask parallel scheduling algorithm. In [35], a peer-to-peer (P2P) enhanced task scheduling framework to minimize the average task duration in device-to-device (D2D) network was proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Obviously, these factors also have a great impact on the efficiency of subsequent task scheduling. Although papers [31][32][33][34][35] focus on these two factors to optimize the task offloading process, they are not considered as a whole. However, task slicing is closely related to its offloading sequence, and different slicing schemes should correspond to different offloading sequence to optimize the execution delay of the task to the maximum extent.…”

Section: Related Workmentioning

confidence: 99%

An Effective Task Offloading Method for Separable Complex Mobile Terminal Tasks

Liu

Zhou

Wang

et al. 2022

Wireless Communications and Mobile Computing

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Due to limited energy and computing power of IoT devices, they cannot handle complex tasks. Edge computing technology effectively solves the requirements of computing power and response delay for complex tasks in devices by migrating computing power to the vicinity of IoT devices. For a separable complex task on IoT terminal, we focus on the effects of data distribution, dependencies, and offloading sequence of subtasks on its total delay when it is offloaded to edge servers. Through comprehensively considering these factors, we study the slicing and choreographing method during the offloading process of a complex task. Firstly, a task slicing method based on hierarchical clustering is presented and an improved hierarchical clustering algorithm is used to obtain the optimal solution of task partitioning. Secondly, a task choreographing method based on overlapping the longest path is presented. Finally, through the simulation experiments of complex tasks with different structures and loads, the effectiveness of our method is verified.

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“…The combination of cloud server and edge device can provide more reliable services to users on the edge. Liu et al [4] proposed a deep learning architecture based on the close connection network, transplanted it into the mobile edge algorithm, and found through the simulation that the algorithm had obvious overall efficiency advantage. Zhang et al [5] proposed the weight based algorithm and mobile prediction based heuristic algorithm for users with certain and uncertain mobile tracks to reduce the network overhead caused by task migration and found through experiments that the two algorithms could effectively reduce the network overhead caused by task migration.…”

Section: Introductionmentioning

confidence: 99%

Research on Resource Allocation and Management of Mobile Edge Computing Network

Zhang¹,

Shi²

2020

IJCAI

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The popularity of mobile Internet makes the application of mobile terminals need more computing resources, and cloud computing enables mobile terminals to handle application tasks that need high computing resources under the premise of maintaining small specifications. However, it is difficult to obtain high-quality low latency services as the mobile Internet edge is far away from the cloud computing center; hence mobile edge computing (MEC) is proposed. This study introduced computing resource allocation methods based on power iteration and system utility, applied them to the mobile edge computing network, and carried out simulation experiments in MATLAB software. The experimental results showed that the network throughput and system utility under two resource allocation methods increased and the average transfer rate decreased with the increase of users in the mobile edge network; under the same number of access users, the edge network based on the system utility allocation method had higher throughput, average transfer rate and system utility. Povzetek: Robno računalništvo (edge computing) omogoča boljše delovanje mrež, ker podatke v oblaku prestavi na rob mreže. Prispevek se ukvarja z analizo in izdelavo tovrstnih metod.

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Optimization of multitask parallel mobile edge computing strategy based on deep learning architecture

Cited by 4 publications

References 18 publications

Research on Optimization Strategy of Task Scheduling Software Based on Genetic Algorithm in Cloud Computing Environment

Research on Optimization Strategy of Task Scheduling Software Based on Genetic Algorithm in Cloud Computing Environment

An Effective Task Offloading Method for Separable Complex Mobile Terminal Tasks

Research on Resource Allocation and Management of Mobile Edge Computing Network

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