scholarly journals A collaborative cloud-edge computing framework in distributed neural network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Shihao Xu ◽  
Zhenjiang Zhang ◽  
Michel Kadoch ◽  
Mohamed Cheriet
Keyword(s):  
Neural Network ◽  
Ant Colony Algorithm ◽  
Scheduling Algorithm ◽  
Task Assignment ◽  
Edge Computing ◽  
Network Training ◽  
Task Scheduling Algorithm ◽  
Current Task ◽  
Computing Framework ◽  
The Internet Of Things

Abstract The emergence of edge computing provides a new solution to big data processing in the Internet of Things (IoT) environment. By combining edge computing with deep neural network, it can make better use of the advantages of multi-layer architecture of the network. However, the current task offloading and scheduling frameworks for edge computing are not well applicable to neural network training tasks. In this paper, we propose a task model offloading algorithm by considering how to optimally deploy neural network model into the edge nodes. An adaptive task scheduling algorithm is also designed to adaptively optimize the task assignment by using the improved ant colony algorithm. Based on them, a collaborative cloud-edge computing framework is proposed, which can be used in the distributed neural network. Moreover, this framework sets up some mechanisms so that the cloud can collaborate with edge computing in the work. The simulation results show that the framework can reduce time delay and energy consumption, and improve task accuracy.

scholarly journals A Machine Learning Solution for Distributed Environments and Edge Computing

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Javier Penas-Noce ◽  
Óscar Fontenla-Romero ◽  
Bertha Guijarro-Berdiñas
Keyword(s):  
Neural Network ◽  
Data Privacy ◽  
Edge Computing ◽  
Raspberry Pi ◽  
The Internet ◽  
Massive Data ◽  
Distributed Environments ◽  
Local Data ◽  
Local Networks ◽  
The Internet Of Things

In a society in which information is a cornerstone the exploding of data is crucial. Thinking of the Internet of Things, we need systems able to learn from massive data and, at the same time, being inexpensive and of reduced size. Moreover, they should operate in a distributed manner making use of edge computing capabilities while preserving local data privacy. The aim of this work is to provide a solution offering all these features by implementing the algorithm LANN-DSVD over a cluster of Raspberry Pi devices. In this system, every node first learns locally a one-layer neural network. Later on, they share the weights of these local networks to combine them into a global net that is finally used at every node. Results demonstrate the benefits of the proposed system.

Ant Colony Algorithm and Application in Inspection of Concrete Structure Defects

2012 ◽  
Vol 204-208 ◽  
pp. 3201-3205
Author(s):  
Wei Hua Zheng ◽  
Zong Hua Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Bp Algorithm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Structure Defects ◽  
Network Training ◽  
Network Method ◽  
Network Identification

BP neural network detecting concrete defect, convergence is slower and accuracy is not high. In order to overcome the defect of BP algorithm, using a combination of Ant Colony optimization algorithm and BP neural network method, a mathematical model of Ant Colony neural network was established, enables Ant Colony neural network training, and verify the validity of the method. And concluded: using ant Colony neural network identification of concrete defects, the identification of the location more effective than on size.

Simulation on Task Scheduling for Multiprocessors Based on Improved Neural Network

2014 ◽  
Vol 513-517 ◽  
pp. 2293-2296
Author(s):  
Xiao Fang Li
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
The Real ◽  
Time Performance ◽  
Task Scheduling Algorithm ◽  
Time Requirements ◽  
Neural Network Algorithm ◽  
Feasible Solutions

This paper mainly discusses task scheduling for multiprocessors. Application requires higher performance of the multiprocessors task scheduling systems. The traditional algorithms majorly consider the accuracy and neglect the real-time performance. In order to improve the real-time performance while maintaining the accuracy, the paper proposes a task scheduling algorithm (GA-ACO) for multiprocessors based on improved neural network. It first builds mathematical models for task scheduling of multiprocessor systems, and then introduces genetic algorithms to quickly find feasible solutions. The simulation results show that the improved neural network algorithm not only has the global optimization ability of genetic algorithm, but also has both local search and the positive feedback capabilities of neural networks; compared with single optimization algorithm, it can quickly find the task scheduling solutions to meet real-time requirements, accelerate the speed of execution of the task, furthermore achieve reasonable, effective task allocation and scheduling for multi-processor.

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 Improved Health Care System in Fog Computing and WSN

2019 ◽  
Vol 9 (1) ◽  
pp. 4269-4271
Keyword(s):  
Health Care ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
The Novel ◽  
Distributed Network ◽  
Task Scheduling Algorithm ◽  
The One ◽  
Advanced Computing ◽  
Present World ◽  
The Internet Of Things

Fog computing is most widely used in many applications. This is the most advanced computing of the various services in the cloud. Fog is considered as another layer that is a distributed network and is similarly having an association with cloud computing and the internet of things (IoT). Health care is the one of the dominating domain in present world. The healthcare with IoT has some of the drawbacks such as limited storage, computing and accessing. To improve the performance the task scheduling algorithm is implemented. To overcome this, in this paper, the novel healthcare system with fog computing and WSN is implemented. Results show the performance of the proposed system.

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