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28th International Conference on Neural Information Processing, ICONIP 2021

作者： An, Shuqi Chen, Shuyu Yuan, Xiaohan Yuwen, Lu Mei, Sha School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9783030923099

The Corona Virus Disease 2019 (COVID-19) has widely spread over the world and comes up with new challenges to the research community. Accurately predicting the number of new infections is essential for optimizing available resources and slowing the progression of such diseases. Long short-term memory network (LSTM) is a typical method for COVID-19 prediction in deep learning, but it is difficult to extract potentially important features in time series effectively. Thus, we proposed a Bidirectional LSTM (BiLSTM) model based on the attention mechanism (ATT) and used the Sparrow Search Algorithm (SSA) for parameter tuning, to predict the daily new cases of COVID-19. We capture the information in the past and future through the BiLSTM network and apply the attention mechanism to assign different weights to the hidden state of BiLSTM, enhance the ability of the model to learn vital information, and use the SSA to optimize the critical parameters of the model for matching the characteristics of COVID-19 data, enhance the interpretability of the model parameters. This study is based on daily confirmed cases collected from six countries: Egypt, Ireland, Iran, Japan, Russia, and the UK. The experimental results show that our proposed model has the best predictive performance among all the comparison models. © 2021, Springer Nature Switzerland AG.

关键词： Long short-term memory

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Distributed Energy Management for Carbon Neutral data Centers 22

Distributed Energy Management for Carbon Neutral Data Center...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Liu, Chuyi Wan, Jianxiong Li, Leixiao Ren, Guanyu Wang, Xiaolei Inner Mongolia University of Technology Huhhot China Inner Mongolia Autonomous Region Engineering Technology Research Center of Big Data Based Software Service Huhhot China Research Center of Large-Scale Energy Storage Technologies Huhhot China

ISBN: (纸本)9798331509712

With the continuous expansion of data centers, their carbon emission becomes a serious issue. A number of studies are committing to reduce the carbon emission of data centers. Carbon trading is a promising emission reduction technique which is, however, seldom applied to data centers. To bridge this gap, we propose a carbon-neutral architecture DC2 for distributed data centers, where each data center consists of three subsystems, i.e., energy subsystem for energy supply, thermal subsystem for data center cooling and the carbon subsystem for carbon trading. Then, we formulate the energy management problem as a Decentralized Partially Observable Markov Decision Processe (Dec-POMDP) and develop a distributed solution framework using Multi-Agent Deep Deterministic Policy Gradient (MAD-DPG). Finally, simulations on real-world data shows that the DC2 provides a cost saving of 8.16%, and applying MADDPG saves 10.04% of the overall cost compared to Independent MADDPG. © 2024 IEEE.

关键词： Carbon sequestration

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Understanding the Implementation Issues When Using Deep Learning Frameworks

SSRN

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SSRN 2023年

作者： Liu, Chao Cai, Runfeng Zhou, Yiqun Chen, Xin Hu, Haibo Yan, Meng School of Big Data and Software Engineering Chongqing University Chongqing401331 China

Context: Deep Learning (DL) frameworks like TensorFlow can help developers implement DL applications (e.g., computer vision) faster and easier. When using DL frameworks, developers encountered a large number of questions and posted them on Stack Overflow (SO). Objective: The goal of this paper is to conduct a comprehensive empirical study on the SO questions, summarize the implementation issues, and suggest future opportunities. Method: This paper focuses on three DL frameworks (i.e., TensorFlow, PyTorch, and Theano), groups 2,401 relevant SO questions into various implementation issues, and constructs a taxonomy. We also analyze the popularity and difficulty of these issues under the taxonomy. Results: For the identified various implementation issues, we constructed a taxonomy consisting of seven major categories with 64 subcategories. Our analysis reveals that 91.7% of questions are related to the implementation categories of data processing, model setting, model training, and model prediction. Developers frequently address the remaining three categories (i.e., Model evaluation, runtime environment, and visualization), where runtime environment is the most difficult category. Based on empirical findings, we provide some suggestions for future research. Conclusion: In this paper, we summarized the issues of DL implementation and proposed corresponding opportunities for future study. We expect this paper to help developers and researchers understand these issues and design better tools to improve the productivity of DL implementation. © 2023, The Authors. All rights reserved.

关键词： Taxonomies

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End-to-End QoS Aggregation and Container Allocation for Complex Microservice Flows 16th

End-to-End QoS Aggregation and Container Allocation for Comp...

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Zhou, Min Wu, Yingbo Wu, Jie School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9783030675394

Microservice is increasingly seen as a rapidly developing architectural style that uses containerization technology to deploy, update, and scale independently and quickly. A complex microservice flow that is composed of a set of microservices can be characterized by a complex request execution path spanning multiple microservices. It is essential to aggregate quality of service (QoS) of individual microservice to provide overall QoS metrics for a complex microservice flow. Besides, leveraging the cost and performance of a complex microservice flow to find an optimal end-to-end container allocation solution with QoS guarantee is also a challenge. In this paper, we define an end-to-end QoS aggregation model for the complex microservice flow, and formulate the end-to-end container allocation problem of microservice flow as a nonlinear optimization problem, and propose an ONSGA2-DE algorithm to solve this problem. We comprehensively evaluate our modeling method and optimization algorithms on the open-source microservice benchmark Sock Shop. The results of experiments show that our method can effectively assist in the QoS management and container allocation of complex microservice flow. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Quality of service

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Multi-D3QN: A Multi-strategy Deep Reinforcement Learning for Service Composition in Cloud Manufacturing 17th

Multi-D3QN: A Multi-strategy Deep Reinforcement Learning for...

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17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Zeng, Jun Yao, Juan Yu, Yang Wu, Yingbo School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9783030926373

Service composition is an indispensable technology in the cloud manufacturing process to ensure the smooth execution of tasks. To implement effective and accurate service composition strategies, many researchers choose to use Meta-heuristics algorithms with strong optimization capabilities. However, as users’ demand of personalized products increasing, dynamic service composition is essential. Meta-heuristics algorithms lack dynamic adaptability, so they are not suitable for solving complex and dynamic service composition problems. Deep Reinforcement Learning (DRL) algorithm is difficult to reach a stable state, when the hyper-parameters and rewards in the algorithm are not properly designed. To solve these problems, we propose a Multi-strategy Deep Reinforcement Learning (DRL) algorithm, named Multi-D3QN, which combines the basic DQN algorithm, the dueling architecture, the double estimator and the prioritized replay mechanism. Meanwhile, we add some strategies such as instant reward, the ɛ-greedy policy and a heuristic strategy to ensure better performance of the algorithm in dynamic environment. Experiments show that our proposed method not only adapt to the dynamic environment, but also obtain a better solution. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Quality of service

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A Complex Dynamic Weight Assignment Method for Conflict Management in Complex Evidence Theory

A Complex Dynamic Weight Assignment Method for Conflict Mana...

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2021 IEEE International Conference on Unmanned Systems, ICUS 2021

作者： Li, Yuting Xiao, Fuyuan School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9780738146577

Under the framework of complex plane, complex mass function (CMF) is a useful tool in modeling uncertainty complex-value-based information. However, how to evaluate complex evidences and to assign weight of the CMF have not been solved yet. Therefore, an information quality of complex evidences and a complex dynamic weight allocation (CDWA) method are proposed in this paper. Based on CDWA, a novel weighted fusion method for complex evidences is presented. Besides, the practicability and feasibility of the proposed method are demonstrated by the numerical example and application in medical diagnosis. © 2021 IEEE.

关键词： Diagnosis

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A Novel Class Imbalance-oriented Polynomial Neural Network Algorithm for Disease Diagnosis

A Novel Class Imbalance-oriented Polynomial Neural Network A...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Yuan, Xiaohan Chen, Shuyu Sun, Chuan Yuwen, Lu Chongqing University School of Big Data Software Engineering Chongqing China

ISBN: (纸本)9781665401265

The class imbalance problem is common in disease diagnosis, which significantly damages the artificial intelligence-based diagnostic models and causes enormous cost about disease misclassification. To alleviate the class imbalance problem, we propose a novel class imbalance-oriented polynomial neural network (CIPNN) algorithm, which incorporates the data sampling and the classifier ensemble. Specifically, we utilize the nearest neighbor algorithm to identify a critical area from the given medical dataset (original dataset), which forms a critical area dataset. Then, we apply the synthetic minority over-sampling technique to generate a balanced dataset based on the original medical dataset. Further, we obtain the ensemble by combining multiple polynomial neural network classifiers, which are learned from the bootstrap samples sampled from the balanced dataset, the critical area dataset, and the original medical dataset, respectively. The experiments conducted on nine imbalanced medical datasets demonstrate that the proposed method can effectively alleviate the class imbalance problem. © 2021 IEEE.

关键词： Polynomials

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Deep Reinforcement Learning for Joint Service Placement and Request Scheduling in Mobile Edge Computing Networks 28

Deep Reinforcement Learning for Joint Service Placement and ...

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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Deng, Yuxuan Li, Xiuhua Sun, Chuan Hao, Jinlong Wang, Xiaofei Leung, Victor C. M. School of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Changan Automobile Software Technology Co. Ltd Chongqing China TKLAN College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350300482

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-Tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-Term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS. © 2023 IEEE.

关键词： Reinforcement learning

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Class-wise Adaptive Logits Distillation with Meta-Learning

Class-wise Adaptive Logits Distillation with Meta-Learning

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiao Huang Wu Chen Wei Zhou College of Computer and Information Science College of Software Southwest University Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Knowledge distillation has become a crucial technique for transferring intricate knowledge from a teacher model to a smaller student model. While logit-based knowledge distillation has shown promise, existing methods often overlook the efficient distillation of logits. In this paper, we introduce a novel approach called Class-wise Adaptive Logits Distillation (CALD) based on meta-learning. Our method leverages a meta-network to generate class-adaptive weights, delivering both explicit and implicit knowledge adaptively. By training the meta-network to assign higher weights to specific classes crucial for the student model’s learning from the teacher model, our approach enhances the knowledge transfer process. Experimental results on CIFAR-100 and ImageNet datasets demonstrate that CALD surpasses state-of-the-art knowledge distillation methods, achieving enhanced accuracy and efficiency in transferring knowledge from teacher to student models.

关键词： Metalearning Training Adaptation models Accuracy Signal processing Acoustics Speech processing Knowledge transfer

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Multi-Server Cooperative Video Caching Strategy Based on Deep Reinforcement Learning in Cloud-Edge Computing 10

Multi-Server Cooperative Video Caching Strategy Based on Dee...

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10th International Conference on big data Computing and Communications, bigCOM 2024

作者： Li, Guanyu Mao, Yingchi Peng, Xinxin Zheng, Haotian Wang, Zibo College of Computer Science and Software Engineering Hohai University Nanjing China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Huaneng Hydro Lancang Xiaowan Hydropower Plant Dali China

ISBN: (纸本)9798331509538

The soaring number of mobile devices has led the innovation of the edge caching in edge computing, which relieves the heavy pressure of cloud computing. However, with the escalating popularity of short video services, the prevalence of video content in edge server caches has seen a marked increase. Previous work ignored the impact of edge server performance on video caching and failed to automatically adjust the optimal video bit rate in response to users' demand. To improve users' evaluation for watching videos on devices with different display sizes, we propose a novel edge caching strategy called Gate Self-attention Recurrent Network-Multi Agent Reinforcement Learning(GSRN-MARL). GSRN-MARL utilizes leverages Gate Recurrent Unit(GRU) and attention mechanisms to predict cache content based on user historical request information and device details, consequently improving edge server caching performance and users' evaluation of video. Experiment demonstrates that GSRN-MARL surpasses LRU, DRL, and MAAC across two critical metrics: video cache hit rate and users' QoE (Quality of Experience). © 2024 IEEE.

关键词： Deep reinforcement learning

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