DoSRA: A Decentralized Approach to Online Edge Task Scheduling and Resource Allocation

Peng, Qinglan; Wu, Chunrong; Xia, Yunni; Ma, Yong; Wang, Xu; Jiang, Ning

doi:10.1109/jiot.2021.3107431

Cited by 30 publications

(8 citation statements)

References 55 publications

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“…In formula (10), F f represents the objective function; Q p represents the resource utilization domain of multi-objective tasks of edge computing; p represents firefly cluster; k represents the multi-objective task resources of edge computing, and represents the resource allocation model as shown in Figure 4.…”

Section: Multi Objective Task Resource Allocation In Edge Computingmentioning

confidence: 99%

“…First, it proposes a edge computing architecture and task processing model based on node cluster partition, characterises task processing characteristics and task coupling relationship, then establishes a two-level optimization model for terminal deployment and task allocation, analyzes the coupling relationship between the two-level models, proposes an improved K-means method to partition node clusters, and uses the implicit enumeration method with boundary conditions to solve the two-level model to achieve task allocation and scheduling. A decentralised online edge task scheduling and resource allocation method is proposed in reference [10]. This method uses the unloading response time weighted by task priority as the evaluation indicator to design real-time task scheduling strategies, edge resource allocation strategies, and runtime task migration strategies.…”

Section: Introductionmentioning

confidence: 99%

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Multi objective task resource allocation method based on hierarchical Bayesian adaptive sparsity for edge computing in low voltage stations

Liu

Bofeng

2023

IET Cyber-Phy Sys Theory & Ap

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In order to achieve more efficient and optimised resource scheduling, this research carried out a multi‐objective task resource allocation method for low‐voltage station edge computing based on hierarchical Bayesian adaptive sparsity. Based on hierarchical Bayesian adaptive sparsity, the multi‐objective task resource allocation technical framework for edge computing in low‐voltage stations is established, which is composed of end pipe edge cloud; After collecting real‐time operation data of power distribution equipment, substation terminals, transmission terminals, etc. in the architecture end, it is transmitted to the data middle platform and service middle platform of the Internet of Things management platform in the cloud through the edge Internet of Things agent; Set and solve the constraint conditions, and build a multi type flexible load hierarchical optimal allocation model; The abnormal area topology identification sub module of multi‐objective task resource of low‐voltage station area edge computing is used to identify the abnormal area topology of the current low‐voltage station area; Taking it as input, the multi‐objective task resources of edge computing are allocated, and the multi‐objective task resources allocation method of edge computing in low pressure platform area is realized under the differential evolution algorithm. The experimental results show that the proposed method has good convergence effect, strong distribution ability, relatively gentle increase in energy consumption, and the calculated results are basically consistent with the actual values, with good effectiveness.

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Section: Multi Objective Task Resource Allocation In Edge Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi objective task resource allocation method based on hierarchical Bayesian adaptive sparsity for edge computing in low voltage stations

Liu

Bofeng

2023

IET Cyber-Phy Sys Theory & Ap

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“…Throughout the traditional statistical techniques, for the quantitative stock market, multi-factor or multi-feature stock selection strategy is the most widely used stock selection model in quantitative stock investment (Zhang et al, 2018). The basic principle is based on mathematical and statistical methods, testing the validity of a series of factors related to stock prices, combining multiple valid features to establish a quantitative model to model stocks, and selecting the best performance according to the corresponding principles for an excess return of the stock portfolio (Peng et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

A Financial Deep Learning Framework

Liao

Hu³

et al. 2022

International Journal of Web Services Research

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Prediction of stock price movement is regarded as a challenging task of financial time series prediction. Due to the complexity and massive financial market data, the research of deep learning approaches for predicting the future price is very difficult. This study attempted to develop a novel framework, named 13f-LSTM, where the AutoRegressive Integrated Moving Average (ARIMA), for the first time, as one of the technical features, Fourier transforms for trend analysis and Long-Short Term Memory (LSTM), including its variants, to forecast the future’s closing prices. Thirteen historical and technical features of stock were selected as inputs of the proposed 13f-LSTM model. Three typical stock market indices in the real world and their corresponding closing prices in 30 trading days are chosen to examine the performance and predictive accuracy of it. The experimental results show that the 13f-LSTM model outperforms other proposed models in both profitability performance and predictive accuracy.

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“…In the articles studying the behavior of Mobileedge Computing networks, some restrictions on the fixed structure of the Network are removed, the combinatorial complexity of load planning and the heterogeneity of nodes are taken into account [13]. In [14], researchers propose intelligent planning.…”

Section: моделирование систем и процессовmentioning

confidence: 99%

Model of a distributed information system solving tasks with the required probability

Gryzunov

2022

ICS

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Introduction: Distributed in space-time Networks: IIoT and IoT, fog and edge computing tend to penetrate into all spheres of human activity. Enterprises, government, law enforcement agencies, etc. depend on the quality of those technologies. Purpose: To determine the composition of the Network that provides the required uptime probability. Methods: According to the concept of structural and functional synthesis, a distributed Network is presented as an unstable queuing system in which servicing devices are connected and disconnected at an arbitrary point in time. A simulation model of the Network has been built. Results: The state of the Network depends on the number of devices and tasks, their performance and lifetimes. The model does not use these quantities themselves, but their ratios. The values of the uptime probability of the Network are calculated for all possible combinations of ratios. The confidence interval has been calculated with a confidence level of 0.95. From the data obtained, it is clear: 1) what should be the minimum composition of the Network in order to provide the required probability; 2) what probability the current composition of the Network can provide; 3) what flow of tasks is admissible in order to solve tasks with the required probability. It is shown that the dependence of the mean tasks residence time on the Network on the composition of the Network has two inflection points. Using information about these points, the Network Management System forms pools of devices or increases the number of devices. Discussion: It is assumed that the Net has a fully connected structure. Consequently, for practical application, it is necessary: to expand the model with an adjacency matrix describing the connections between nodes, and hence the paths of propagation of tasks over the Network or consider that each node is a relay and is capable of transmitting the task to any other node on the Network. Overhead costs arising from this are taken into account by adjusting the original data. Practical relevance: The results allow minimizing costs in the design and operation of distributed systems, maximizing the likelihood of system uptime under given constraints for resource.

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DoSRA: A Decentralized Approach to Online Edge Task Scheduling and Resource Allocation

Cited by 30 publications

References 55 publications

Multi objective task resource allocation method based on hierarchical Bayesian adaptive sparsity for edge computing in low voltage stations

Multi objective task resource allocation method based on hierarchical Bayesian adaptive sparsity for edge computing in low voltage stations

A Financial Deep Learning Framework

Model of a distributed information system solving tasks with the required probability

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