Toward service selection for workflow reconfiguration:An interface-based computing solution

Gao, Honghao; Huang, Wanqiu; Yang, Xiaoxian; Duan, Yucong; Yin, Yuyu

doi:10.1016/j.future.2018.04.064

Cited by 108 publications

(58 citation statements)

References 39 publications

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“…Study of workflow scheduling using CGA is another future investigation. And we can also mine or forecast its potential relationships [32][33][34]. In addition, the method of task scheduling can consider many other parameters, such as the use of memory, peak of the demand, and overloads [10].…”

Section: Discussionmentioning

confidence: 99%

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Wang

Pang

et al. 2020

Scientific Programming

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Task scheduling plays a critical role in the performance of the edge-cloud collaborative. Whether the task is executed in the cloud and how it is scheduled in the cloud is an important issue. On the basis of satisfying the delay, this paper will schedule tasks on edge devices or cloud and present a task scheduling algorithm for tasks that need to be transferred to the cloud based on the catastrophic genetic algorithm (CGA) to achieve global optimum. e algorithm quantifies the total task completion time and the penalty factor as a fitness function. By improving the roulette selection strategy, optimizing mutation and crossover operator, and introducing cataclysm strategy, the search scope is expanded. Furthermore, the premature problem of the evolutionary algorithm is effectively alleviated. e experimental results show that the algorithm can address the optimal local issue while significantly shortening the task completion time on the basis of satisfying tasks delays.

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Section: Discussionmentioning

confidence: 99%

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Wang

Pang

et al. 2020

Scientific Programming

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“…GR is an important application problem in many social activities and industries, such as online shopping, music sharing and group travelling. GR belongs to social services [10], so it inevitably emphasizes the service quality (QoS) [11], and privacy preservation and dynamicity [12]. Service recommendation research has a long history, along with personalized recommendation technology, such as collaborative filtering (CF) [13], matrix decomposition (MF) [14] and deep learning (DL) [15] have been widely studied and the research on group recommendation is still very limited.…”

Section: Group Recommendationmentioning

confidence: 99%

Group recommendation based on hybrid trust metric

2020

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Group recommendation is a special service type which has the ability to satisfy a group's common interest and find the preferred items for group users. Deep mining of trust relationship between group members can contribute to the improvement of accuracy during group recommendation. Most of the existing trust-based group recommendation methods pay little attention to the diversity of trust sources, resulting in poor recommendation accuracy. To address the problem above, this paper proposes a group recommendation method based on a hybrid trust metric (GR-HTM). Firstly, GR-HTM creates an attribute trust matrix and a social trust matrix based on user attributes and social relationships, respectively. Secondly, GR-HTM accomplishes a hybrid trust matrix based on the integration of these two matrices with the employment of the Tanimoto coefficient. Finally, GR-HTM calculates weights for each item in the hybrid trust matrix based on weighted-meanlist and proceeds to group recommendation with a given trust threshold. Simulation experiments demonstrate that the proposed GR-HTM has better performance for group recommendation in accuracy and effectiveness. ARTICLE HISTORY

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“…This solution can produce faster results without an operational network connection but must cope with the lack of device resources and therefore requires the right software and hardware solutions. In terms of mobile services, Honghao Gao, Wanqiu Huang and others have researched and innovated service patterns and workflows [31,32]. In the development of deep learning, scientists have optimized model compression and neural network structure, especially for the CNN.…”

Section: Related Workmentioning

confidence: 99%

A New Deep Learning-Based Handwritten Character Recognition System on Mobile Computing Devices

Xia

2019

Mobile Netw Appl

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Deep learning (DL) is a hot topic in current pattern recognition and machine learning. DL has unprecedented potential to solve many complex machine learning problems and is clearly attractive in the framework of mobile devices. The availability of powerful pattern recognition tools creates tremendous opportunities for next-generation smart applications. A convolutional neural network (CNN) enables data-driven learning and extraction of highly representative, hierarchical image features from appropriate training data. However, for some data sets, the CNN classification method needs adjustments in its structure and parameters. Mobile computing has certain requirements for running time and network weight of the neural network. In this paper, we first design an image processing module for a mobile device based on the characteristics of a CNN. Then, we describe how to use the mobile to collect data, process the data, and construct the data set. Finally, considering the computing environment and data characteristics of mobile devices, we propose a lightweight network structure for optical character recognition (OCR) on specific data sets. The proposed method using a CNN has been validated by comparison with the results of existing methods, used for optical character recognition.

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Toward service selection for workflow reconfiguration:An interface-based computing solution

Cited by 108 publications

References 39 publications

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Group recommendation based on hybrid trust metric

A New Deep Learning-Based Handwritten Character Recognition System on Mobile Computing Devices

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