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An Effective Cloud Workflow Scheduling Approach Combining PSO and Idle Time Slot-Aware Rules

IEEE/CAA Journal of Automatica Sinica 2021年第5期8卷 1079-1094页

作者： Yun Wang Xingquan Zuo the School of Computing Science Beijing University of Posts and TelecommunicationsBeijing 100876China the School of Information Engineering Nanchang Institute of TechnologyNanchang 330099China the Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijing 100876China

Workflow scheduling is a key issue and remains a challenging problem in cloud *** with the large number of virtual machine(VM)types offered by cloud providers,cloud users need to choose the most appropriate VM type for each *** task scheduling sequences exist in a workflow *** task scheduling sequences have a significant impact on the scheduling *** is not easy to determine the most appropriate set of VM types for tasks and the best task scheduling ***,the idle time slots on VM instances should be used fully to increase resources'utilization and save the execution cost of a *** paper considers these three aspects simultaneously and proposes a cloud workflow scheduling approach which combines particle swarm optimization(PSO)and idle time slot-aware rules,to minimize the execution cost of a workflow application under a deadline constraint.A new particle encoding is devised to represent the VM type required by each task and the scheduling sequence of *** idle time slot-aware decoding procedure is proposed to decode a particle into a scheduling *** handle tasks'invalid priorities caused by the randomness of PSO,a repair method is used to repair those priorities to produce valid task scheduling *** proposed approach is compared with state-of-the-art cloud workflow scheduling *** show that the proposed approach outperforms the comparative algorithms in terms of both of the execution cost and the success rate in meeting the deadline.

关键词： Cloud computing idle time slot particle swarm optimization task scheduling sequence workflow scheduling

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Compositional Concept-Based Neuron-Level Interpretability for Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Jiang, Zeyu Huang, Hai Zuo, Xingquan School of Computer Science Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Services Ministry of Education Beijing China

Deep reinforcement learning (DRL), through learning policies or values represented by neural networks, has successfully addressed many complex control problems. However, the neural networks introduced by DRL lack interpretability and transparency. Current DRL interpretability methods largely treat neural networks as black boxes, with few approaches delving into the internal mechanisms of policy/value networks. This limitation undermines trust in both the neural network models that represent policies and the explanations derived from them. In this work, we propose a novel concept-based interpretability method that provides fine-grained explanations of DRL models at the neuron level. Our method formalizes atomic concepts as binary functions over the state space and constructs complex concepts through logical operations. By analyzing the correspondence between neuron activations and concept functions, we establish interpretable explanations for individual neurons in policy/value networks. Experimental results on both continuous control tasks and discrete decision-making environments demonstrate that our method can effectively identify meaningful concepts that align with human understanding while faithfully reflecting the network’s decision-making logic. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Communication Encryption Scheme of 5G Power Trading Private Network Based on Dynamic key 2

Communication Encryption Scheme of 5G Power Trading Private ...

引用

2nd Asia Conference on Electrical, Power and Computer Engineering, EPCE 2023

作者： Zhou, Rui Song, Yiyang Xu, Ying Harbin Institute of Technology Electrical Engineering and Automation School Harbin China Guangdong Power Exchange Center Information Data Department Guangzhou China Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9798350310818

Security has been the primary concern for the 5G power trading private network, which involves a large amount of power trading information. The communication must be encrypted to prevent the leakage of important information. The communication encryption scheme for 5G power trading private network should meet the characteristics of small communication overhead, low storage space requirement and strong resistance to attack. Based on these characteristics, the dynamic key-based encryption scheme is designed. To ensure the security and reduce the computation overhead, the optimized symmetric encryption algorithm is used to update the shared key. And the dynamic key generation method is improved by adding random numbers to solve the problem that the correct transmission of the data link does not have true randomness. The encryption mode used can generate dynamic keys directly and synchronously at both ends of the communication, reducing the overhead of key exchange and eliminating the need for trusted third parties. The algorithm is also analyzed for security and efficiency and compared with other algorithms. The experimental results show that the improved algorithm enhances security with guaranteed efficiency. © 2023 IEEE.

关键词： Efficiency

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Blockchain Storage Method Based on Erasure Code 8

Blockchain Storage Method Based on Erasure Code

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8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Meng, Fanyao Li, Jin Gao, Jiaqi Liu, Junjie Ru, Junpeng Lu, Yueming School of Cyberspace Security Beijing University of Posts and Telecommunications Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications The State Key Laboratory of Information Photonics and Optical Communications Beijing China

ISBN: (纸本)9798350331035

Blockchain, as an emerging distributed database, effectively addresses the issue of centralized storage in IoT data, where storage capacity cannot match the explosive growth in devices and data scale, as well as the contradictions arising from centralized data management concerning data privacy and security concerns. To alleviate the problem of excessive pressure on single-point storage and ensure data security, a blockchain data storage method based on erasure codes is proposed. This method involves constructing mathematical functions that describe the data to split the original block data into multiple fragments and add redundant slices. These fragments are then encoded and stored in different locations using a circular hash space with the addition of virtual nodes to ensure load balancing among nodes and reduce situations where a single node stores too many encoded data blocks, effectively enhancing the storage space utilization efficiency of the distributed storage database. The blockchain storage method stores encoded data digest information such as storage location, creation time, and hashes, allowing for the tracing of the origin of encoded data blocks. In case of accidental loss or malicious tampering, this enables effective recovery and ensures the integrity and availability of data in the network. Experimental results indicate that compared to traditional blockchain approaches, this method effectively reduces the storage pressure on nodes and exhibits a certain degree of disaster recovery capability. © 2023 IEEE.

关键词： Blockchain Consistent Hashing Erasure Codes Internet of Things

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RL-MSA: a Reinforcement Learning-based Multi-line bus Scheduling Approach

arXiv

引用

arXiv 2024年

作者： Liu, Yingzhuo Zuo, Xingquan School of Computer Science Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China

Multiple Line Bus Scheduling Problem (MLBSP) is vital to save operational cost of bus company and guarantee service quality for passengers. Existing approaches typically generate a bus scheduling scheme in an offline manner and then schedule buses according to the scheme. In practice, uncertain events such as traffic congestion occur frequently, which may make the pre-determined bus scheduling scheme infeasible. In this paper, MLBSP is modeled as a Markov Decision Process (MDP). A Reinforcement Learning-based Multi-line bus Scheduling Approach (RL-MSA) is proposed for bus scheduling at both the offline and online phases. At the offline phase, deadhead decision is integrated into bus selection decision for the first time to simplify the learning problem. At the online phase, deadhead decision is made through a time window mechanism based on the policy learned at the offline phase. We develop several new and useful state features including the features for control points, bus lines and buses. A bus priority screening mechanism is invented to construct bus-related features. Considering the interests of both the bus company and passengers, a reward function combining the final reward and the step-wise reward is devised. Experiments at the offline phase demonstrate that the number of buses used of RL-MSA is decreased compared with offline optimization approaches. At the online phase, RL-MSA can cover all departure times in a timetable (i.e., service quality) without increasing the number of buses used (i.e., operational cost). Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Multi-Agent Trust Evaluation Model based on Reinforcement Learning 8

Multi-Agent Trust Evaluation Model based on Reinforcement Le...

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8th International Conference on Dependable Systems and Their Applications, DSA 2021

作者： Haoran, Jia Yuyu, Yuan Qiang, Han Pengqian, Zhao Ting, Guo Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781665443913

The establishment of trust between agents is an important part of Multi-agent Reinforcement Learning (MARL). Recent studies on multi-agents have paid more attention to the game relationship between agents, ignoring trust is the basis of cooperation among agents. In this article, we propose a trust evaluation model in MARL to evaluate the trustworthiness of the target agent. The trust evaluation of the target agent is composed of direct trust and reputation. Direct trust is obtained from the historical interaction of agents. Reputation is to evaluate the impact of an agent's behavior by counterfactual action of a high-ranking agent. In addition, this paper also proposes a trust-based reward mechanism in MARL, which divides agent rewards into environmental rewards and trust rewards. Trust rewards are obtained through trust evaluation values, which drive the agents to reach a cooperative state and obtain higher long-term rewards. In this paper, we set up experiments in harvest game to verify that the trust evaluation model can effectively promote the cooperation of agents and avoid the problem of agents facing social dilemmas. © 2021 IEEE.

关键词： Reinforcement learning

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A Novel Evolutionary Neural Network-based Approach for Online Bus Scheduling

A Novel Evolutionary Neural Network-based Approach for Onlin...

引用

IEEE International Conference on Networking, Sensing and Control

作者： Wenyang Tian Feiyang Wang Xingquan Zuo School of Computer Science Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Bus scheduling is vital to ensure service quality and save operational costs. Current studies typically consider the problem as an optimization problem and solve it using exact or heuristic approaches. In this paper, we consider the problem as a sequential decision-making problem and propose an Evolutionary Neural Network-based Approach (ENNA) for it. A neural network is used to make a decision at each departure time in the bus timetable. It selects which bus should depart at that specific time. By this means, a complete bus scheduling scheme is generated by a series of decisions made by the neural network. The structure and weights of the neural network are optimized by a genetic algorithm. To meet the real-world operational needs of bus duty types and improve bus utilization, we design a bus duty type transition rule that dynamically decides the duty type of buses. Experimental results demonstrate that the proposed ENNA outperforms manual scheduling schemes and can generate bus scheduling schemes with fewer buses.

关键词： Network topology Decision making Artificial neural networks Transforms Dynamic scheduling Real-time systems Topology Sensors Optimization Genetic algorithms

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An Abnormal Behavior Detection Method Based on User Behavior Correlation Feature Sequence Modeling 9

An Abnormal Behavior Detection Method Based on User Behavior...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Zhou, Yuting Sun, Lijuan Wu, Jingchen Gao, Yutong Wu, Xu Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service China Beijing University of Posts and Telecommunications School of Cyberspace Security BUPT Beijing China Beihang University Intellectual Property Information Service Center BHU Beijing China Minzu University of China School of Information Engineering MUC Beijing China

ISBN: (纸本)9798350391367

The healthcare industry is one of the industries most affected by insider threats. At present, the mainstream abnormal behavior detection of internal threats has the problems of relatively single reference dimension of user behavior data and lack of correlation analysis between data of different dimensions. Therefore, this paper proposes an abnormal behavior detection method that integrates user behavior correlation feature sequence modeling (UBCFSM). This method fully considers the multidimension and correlation of user behavior data, and uses the time convolutional network to learn behavior sequence features and the bidirectional long and short term memory network to learn behavior habit features of the convolutional attention mechanism. In addition, the deep neural network is introduced to fuse the deviations between the predicted results of different models and the actual results, detect abnormal results, and improve the generalization ability and detection accuracy of the model. Finally, experiments were carried out on the CERT public data set, and the AUC score of 97.1% was obtained. Compared with other abnormal behavior detection models, the proposed model has achieved significant improvement, showing its effectiveness and competitiveness. © 2024 IEEE.

关键词： Convolutional neural networks

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Graph Neural Network Based User Group Comments Interaction Behavior Prediction on Microblog Websites 6

Graph Neural Network Based User Group Comments Interaction B...

引用

6th IEEE International Conference on Data Science in Cyberspace, DSC 2021

作者： Chen, Yisen Wu, Xu Xie, Xiaqing Wu, Jingchen Qiu, Lirong Sun, Lijuan Beijing University Of Posts And Telecommunications Ministry Of Education Key Laboratory Of Trustworthy Distributed Computing And Service Beijing China

ISBN: (纸本)9781665418157

The interactive behavior of comments within the user group on microblog website is bidirectional and dynamic, reflecting the level of familiarity among users. Predicting the future comment interaction behavior within the user group is of great significance for commercial recommendation and crime fighting. Related research groups regard it as a temporal link prediction problem, assume the evolution is smooth or there is no evolution at all. Meanwhile, the feature in those studies is too unitary, leading to low link prediction performance. In this paper, we propose a method called User Group Comment Interaction Behaviour Prediction (UGCIBP), which combines structure extraction layer and evolution extraction layer to do dynamic graph representation learning to acheive the modeling of user group historical comment interaction graphs, and build the features of communication weight, interest similarity, and common degree of activity between users on microblog website. Then, we combine dynamic graph representation learning and the constructed features to do link prediction, finally, the purpose of predicting comment interaction is acheived. Experiments are conducted on the public datasets Enron and Twitter datasets to evaluate the method. The results show that the AUC scores of this method are 4.74% and 9.95% higher than existing methods, respectively, which proves the effectiveness of the UGCIBP method. © 2021 IEEE.

关键词： Websites

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OptimizeLog: Log Anomaly Detection and Localization based on Optimized Log Parsing in distributed Systems

OptimizeLog: Log Anomaly Detection and Localization based on...

引用

International Conference on Computer and Communications (ICCC)

作者： Yanjie Sun Yali Gao Xiaoyong Li Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing China

Modern distributed systems generate interleaved logs when performing parallel operations, and these logs become an important basis for anomaly detection and localization. To achieve more robust and accurate log anomaly detection and localization, we propose a comprehensive model, namely OptimizeLog. We built a dictionary- and length-based log parser that works stably in most log systems without parameter tuning. We significantly improve anomaly detection accuracy by introducing ALBERT-based semantic embeddings, count embeddings, and time embeddings, combined with an attentionbased Bi-GRU model. By constructing component instance forest and tree-based depth-first traversal algorithm, abnormal location in distributed systems is realized. Experimental results show that our log parsing method is 4% more accurate than other log parsers. Compared with other advanced methods such as DeepLog, LogAnomaly, and LogRobust, OptimizeLog improves the accuracy of anomaly detection by 5%, while enabling instancelevel anomaly localization on real datasets.

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