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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yinggen Xu Wei Chen Shaoqi Wang Xiaobo Zhou Changjun Jiang The Key Lab of Embedded System and Service Computing Tongji University Shanghai China Department of Computer Science University of Colorado Colorado Springs Colorado USA

ISBN: (纸本)9781538641293

Modern datacenter schedulers apply a static policy to partition resources among different tasks. The amount of allocated resource won't get changed during a task's lifetime. However, we found that resource usage during a task's runtime demonstrates high dynamics and it only reaches full usage at few moments. Therefore, the static allocation policy doesn't exploit the dynamic nature of resource usage, leading to low system resource utilization. To address this hard problem, a recently proposed task-consolidation approach packs as many tasks as possible on the same node based on real-time resource demands. However, this approach may cause resource over-allocation and harm application performance. In this paper, we propose and develop ECS, an elastic container based scheduler that leverages resource usage variation within the task lifetime to exploit the potential utilization and parallelism. The key idea is to proactively select and shift tasks backward so that the inherent paralleled tasks can be identified without over-allocation. We formulate the scheduling scheme as an online optimization problem and solves it using a resource leveling algorithm. We have implemented ECS in Apache Yarn and performed evaluations with various MapReduce benchmarks in a cluster. Experimental results show that ECS can efficiently utilize resource and achieves up to 29% reduction on average job completion time while increasing CPU utilization by 25%, compared to stock Yarn.

关键词： Task analysis Containers Dynamic scheduling Resource management Benchmark testing Parallel processing Runtime

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FedDDB: Clustered Federated Learning based on Data Distribution Difference 22

FedDDB: Clustered Federated Learning based on Data Distribut...

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Proceedings of the 2022 5th International Conference on Algorithms, computing and Artificial Intelligence

作者： Chengyu You Zihao Lu Junli Wang Chungang Yan Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education China and National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University China

ISBN: (纸本)9781450398336

Clustered federated learning is a federated learning method based on multi-task learning. It groups similar clients into the same clusters and shares model parameters to solve the problem that the joint model is trapped in local optima on Non-IID data. Most of the existing clustered federated learning methods are based on the difference of model parameters for clients clustering. During the client model training process, the model parameters are biased and the clustering result is affected due to insufficient samples and missing eigenvalues in the dataset. In this paper, we develop a clustered federated learning method based on data distribution difference (FedDDB) in the dataset level. The method in this paper focuses on the distribution of label probability and eigenvalues, analyzes the difference of data distribution difference between clients and measures the distance between datasets which is used for client clustering. Every cluster will be trained independently and in parallel on the cluster center model. At the beginning of each round of training, the client clustering process needs to be repeated. We conduct relevant experiments and demonstrate the effectiveness of our method.

关键词： Clustering method

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Base Station Network Traffic Prediction Approach Based on LMA -DeepAR 6

Base Station Network Traffic Prediction Approach Based on LM...

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6th IEEE International Conference on Computer and Communication systems, ICCCS 2021

作者： Zhang, Jiachen Zuo, Xingquan Xu, Mingying Han, Jing Zhang, Baisheng Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing Uinversity of Posts and Telecommunications Beijing China Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing Uinversity of Posts and Telecommunications Beijing China Network Interface Virtualization Shanghai System Design Deptment Zhongxing Telecommunication Equipment Corporation Shanghai China

ISBN: (纸本)9780738126043

Accurate network traffic prediction of base station cell is very vital for the expansion and reduction of wireless devices in base station cell. The burst and uncertainty of base station cell network traffic makes the network traffic nonlinear and nonstationary, which brings challenges to the long-term prediction of network traffic. In this paper, the traffic model LMA-DeepAR for base station network is established based on DeepAR. Acordding to the distribution characteristics of network traffic, this paper proposes an artificial feature sequence calculation method based on local moving average (LMA). The feature sequence is input into DeepAR as covariant, which makes the statistical characteristics of network traffic near a period of time in the past be considered when updating parameters, and the interference of non-stationary network traffic on model training will be reduced. Experimental results show that the proposed prediction approach (LMA-DeepAR) outperforms other methods in the overall long-term prediction performance and stability of multi cell network traffic. © 2021 IEEE.

关键词： Base stations

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Behavioral Authentication for Security and Safety

arXiv

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arXiv 2023年

作者： Wang, Cheng Tang, Hao Zhu, Hangyu Zheng, Junhan Jiang, Changjun Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China

The issues of both system security and safety can be dissected integrally from the perspective of behavioral appropriateness. That is, a system is secure or safe can be judged by whether the behavior of certain agent(s) is appropriate or not. Specifically, a so-called appropriate behavior involves the right agent performing the right actions at the right time under certain conditions. Then, according to different levels of appropriateness and degrees of custodies, behavioral authentication can be graded into three levels, i.e., the authentication of behavioral Identity, Conformity, and Benignity. In a broad sense, for the security and safety issue, behavioral authentication is not only an innovative and promising method due to its inherent advantages but also a critical and fundamental problem due to the ubiquity of behavior generation and the necessity of behavior regulation in any system. By this classification, this review provides a comprehensive examination of the background and preliminaries of behavioral authentication. It further summarizes existing research based on their respective focus areas and characteristics. The challenges confronted by current behavioral authentication methods are analyzed, and potential research directions are discussed to promote the diversified and integrated development of behavioral authentication. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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A Privacy Preserving Algorithm Based on R-constrained Dummy Trajectory in Mobile Social Network

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Procedia Computer Science 2018年 129卷 420-425页

作者： Lina Ni Yanfeng Yuan Xiao Wang Jiguo Yu Jinquan Zhang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China School of Information Science and Engineering Qufu Normal University Shandong China

Recently the research of location privacy preserving has become a hot spot. Location privacy preserving involves not only a single location privacy but also the trajectory privacy where the mobile users in different locations publish the consecutive query requests. In this paper, we consider the problem of trajectory privacy preserving in MSN. Particularly, we propose privacy preserving algorithms based on R-constraint dummy trajectory (RcDT). By constraining the generating range R of the dummy positions, the generated dummy positions are within a certain range near the real locations. Furthermore, the dummy trajectories which have higher similarity to the real trajectories are generated via constraining both the single location exposure risk and the trajectory exposure risk.

关键词： Privacy preserving mobile social network trajectory privacy location-based service R-constrained dummy trajectory

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A MapReduce based parallel SVM for large scale spam filtering

A MapReduce based parallel SVM for large scale spam filterin...

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2011 8th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Caruana, Godwin Li, Maozhen Qi, Man School of Engineering and Design Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Department of Computing Canterbury Christ Church University Canterbury Kent CT1 1QU United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University China

ISBN: (纸本)9781612841816

Spam continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) based techniques have been proposed for spam classification. However, SVM training is a computationally intensive process. This paper presents a parallel SVM algorithm for scalable spam filtering. By distributing, processing and optimizing the subsets of the training data across multiple participating nodes, the distributed SVM reduces the training time significantly. Ontology based concepts are also employed to minimize the impact of accuracy degradation when distributing the training data amongst the SVM classifiers. © 2011 IEEE.

关键词： Support vector machines

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On the Expressive Power of Behavior Structure

arXiv

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arXiv 2023年

作者： Wang, Cheng Zhu, Hangyu Lin, Yuhang Jiang, Changjun Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Efforts toward a comprehensive description of behavior have indeed facilitated the development of representation-based approaches that utilize deep learning to capture behavioral information. As behavior complexity increases, the expressive power of these models reaches a bottleneck. We coin the term "behavioral molecular structure" and propose a new model called the Behavioral Molecular Structure (BMS). The model characterizes behaviors at the atomic level, analogizes behavioral attributes to atoms, and concretizes interrelations at the granularity of atoms using graphs. Here, we design three different downstream tasks to test the performance of the BMS model on public datasets. Additionally, we provide a preliminary theoretical analysis demonstrating that the BMS can offer effective expressiveness for complex behaviors. Copyright © 2023, The Authors. All rights reserved.

关键词： Atoms

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Parallelizing abnormal event detection in crowded scenes with GPU 12

Parallelizing abnormal event detection in crowded scenes wit...

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12th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2015

作者： Yavari, Mohammadreza Li, Maozhen Li, Siguang Qi, Man Department of Electronic and Computer Engineering Brunel University London UxbridgeUB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai200092 China Department of Computing Canterbury Christ Church University KentCT1 1QU United Kingdom

ISBN: (纸本)9781467376822

Analyzing human activities in surveillance videos a challenging task due to the high volume of data that needs to be processed in a timely manner. This paper presents a GPU based Gaussian Mixture Model (GMM) to detect abnormal activities in crowded scenes. GMM is a fully unsupervised method that predicts abnormal crowd behaviors based on the processing of normal crowd behaviors. Specifically, we use crowd distribution and GMM to estimate the speed and to predict the behaviors of the crowd. The performance of the parallel GMM is evaluated from the aspects of computation efficiency and accuracy in terms of area under the curve. © 2015 IEEE.

关键词： Graphics processing unit

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Covariance free incremental principal component analysis with exact mean update

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Journal of Computational Information systems 2013年第16期9卷 6703-6710页

作者： Zeng, Xueqiang Li, Guozheng The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Computer Center Nanchang University Nanchang 330029 China Department of Control Science and Engineering Tongji University Shanghai 201804 China

Incremental feature extraction is an essential data preprocessing technique for large-scale and streaming data mining. Among various covariance matrix-free Incremental Principal Component Analysis (IPCA) methods, Candid Covariance-free Incremental Principal Component Analysis (CCIPCA) is a state-of-the art algorithm. Since the training samples are required to be centred, CCIPCA applies an approximate centric alignment on the input data, where only the current sample is correctly centred and all historical data are not updated properly. In this paper, we propose a novel centred incremental principal component analysis algorithm with exact historical mean update, where not only the current sample is centred, but also all historical data are updated by the current mean correctly. Compared to CCIPCA, the proposed method converges more quickly, and the performance improvement is especially obvious when the data's inherent covariance is not stable. The experiments on real streaming dataset show that the proposed method is much superior to CCIPCA in convergence speed. © 2013 Binary Information Press.

关键词： Principal component analysis

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Improved asymptotic multicast throughput for random extended networks

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Computer Communications 2010年第18期33卷 2195-2202页

作者： Cheng Wang Changjun Jiang Xiang-Yang Li Jiujun Cheng Department of Computer Science and Technology Tongji University Shanghai China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science Illinois Institute of Technology Chicago IL 60616 USA

We study the asymptotic throughput for random extended networks , where n ad hoc nodes are randomly deployed in a square region R ( n ) = 0 , n 2 . We directly consider the multicast throughput to unify the unicast and broadcast throughput, and design a new multicast scheme under the generalized physical model based on the so-called secondary highways system . Taking account of all possible cases of n s = ω (1) and 1 ⩽ n d ⩽ n − 1, we derive the achievable multicast throughput, where n s and n d denote the number of sessions and the number of destinations of each session. We prove that for some cases in terms of n s and n d , our scheme achieves better throughput than the existing schemes.

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