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International Conference on parallel and distributed Systems (ICPADS)

作者： Yutian Lei Qingyong Deng Saiqin Long Shaohui Liu Sangyoon Oh The Key Laboratory of Hunan Province for Internet of Things and Information Security The School of Computer Science Science and Technology on Parallel and Distributed Processing Laboratory Xiangtan University Xiangtan China The Artificial Intelligence Center PengCheng Laboratory shenzhen China Ajou University Suwon-si Republic of Korea

The following topics are dealt with: cloud computing; learning (artificial intelligence); resource allocation; security of data; mobile computing; neural nets; optimisation; parallel processing; scheduling; and storag... 详细信息

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

关键词： Deep learning Schedules Multicore processing Logic gates Throughput Acceleration Field programmable gate arrays

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Versionized process based on non-volatile random-access memory for fine-grained fault tolerance

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Frontiers of Information technology & Electronic Engineering 2018年第2期19卷 192-205页

作者： Wen-zhe ZHANG Kai LU Xiao-ping WANG Science and Technology on Parallel and Distributed Processing Laboratory College of ComputerNational University of Defense Technology

Non-volatile random-access memory（NVRAM） technology is maturing rapidly and its byte-persistence feature allows the design of new and efficient fault tolerance mechanisms. In this paper we propose the versionized process（Ver P）, a new process model based on NVRAM that is natively non-volatile and fault tolerant. We introduce an intermediate software layer that allows us to run a process directly on NVRAM and to put all the process states into NVRAM, and then propose a mechanism to versionize all the process data. Each piece of the process data is given a special version number, which increases with the modification of that piece of data. The version number can effectively help us trace the modification of any data and recover it to a consistent state after a system *** with traditional checkpoint methods, our work can achieve fine-grained fault tolerance at very little cost.

关键词： Non-volatile memory Byte-persistence Versionized process Version number

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RLLL: Accurate Relative Localization of RFID Tags with Low Latency

RLLL: Accurate Relative Localization of RFID Tags with Low L...

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International Workshop on Quality of Service

作者： Xuan Liu Quan Yang Shigeng Zhang Bin Xiao The Science and Technology on Parallel and Distributed Processing Laboratory (PDL) P. R. China College of Computer Science and Electronic Engineering Hunan University P. R. China School of Computer Science and Engineering Central South University P. R. China The Hong Kong Polytechnic University Hong Kong

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

Radio frequency identification (RFID) has been widely used in many smart applications. In many scenarios, it is essential to know the ordering of a set of RFID tags. For example, to quickly detect misplaced books in smart libraries, we need to know the relative ordering of the tags attached to the books. Although several relative RFID localization algorithms have been proposed, they usually suffer from large localization latency and cannot support applications that require real-time detection of tag (product) positions like automatic manufacturing on an assembly line. Moreover, existing approaches face significant degradation in ordering accuracy when the tags are close to each other. In this paper, we propose RLLL, an accurate Relative Localization algorithm for RFID tags with Low Latency. RLLL reduces localization latency by proposing a novel geometry-based approach to identifying the V-zone in the phase reading sequence of each tag. Moreover, RLLL uses only the data in the V-zone to calculate relative positions of tags and thus avoids the negative effects of low-quality data collected when the tag is far from the antenna. Experimental results with commercial RFID devices show that RLLL achieves an ordering accuracy of higher than 0.986 with latency less than 0.8 seconds even when the tags are spaced only 7 mm from adjacent tags, in which case the state-of-the-art solutions only achieve ordering accuracy of lower than 0.8 with localization latency larger than 3 seconds.

关键词： RFID tags Manufacturing Antenna arrays Quality of service Libraries

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Locality-sensitive sketching for resilient network flow monitoring

arXiv

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arXiv 2019年

作者： Fu, Yongquan Li, Dongsheng Shen, Siqi Zhang, Yiming Chen, Kai Science and Technology Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology SING Lab Hong Kong University of Science and Technology

Network monitoring is vital in modern clouds and data center networks for traffic engineering, network diagnosis, network intrusion detection, which need diverse traffic statistics ranging fromflow size distributions to heavy hitters. To cope with increasing network rates and massive traffic volumes, sketch based approximate measurement has been extensively studied to trade the accuracy for memory and computation cost, which unfortunately, is sensitive to hash collisions. In addition, deploying the sketch involves fine-grained performance control and instrumentation. This paper presents a locality-sensitive sketch (LSS) to be resilient to hash collisions. LSS proactively minimizes the estimation error due to hash collisions with an autoencoder based optimization model, and reduces the estimation variance by keeping similar network flows to the same bucket array. To illustrate the feasibility of the sketch, we develop a disaggregatedmonitoring application that supports non-intrusive sketching deployment and native network-wide analysis. Testbed shows that the framework adapts to line rates and provides accurate query results. Real-world trace-driven simulations show that LSS remains stable performance underwide ranges of parameters and dramatically outperforms state-of-the-art sketching structures, with over 103 to 105 times reduction in relative errors for per-flow queries as the ratio of the number of buckets to the number of network flows reduces from 10% to 0.1%. Copyright © 2019, The Authors. All rights reserved.

关键词： Intrusion detection

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DCIGAN: A distributed Class-Incremental Learning Method Based on Generative Adversarial Networks

DCIGAN: A Distributed Class-Incremental Learning Method Base...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Hongtao Guan Yijie Wang Xingkong Ma Yongmou Li Science and Technology on Parallel and Distributed Laboratory College of Computer National University of Defense Technology Changsha Hunan P.R.China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Class incremental learning has received wide attention due to its better adaptability to the changing characteristics of online learning. However, data cannot be shared between organizations in the data isolated islands scenario. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIGAN. It uses GAN generators to store the information of past data and continuously update GAN parameters with new data. In particular, we proposed CIGAN to ensure that the distribution of generated pseudo data is as close as possible to the real data on a single node, which guarantees the accuracy of class-incremental learning. Furthermore, we propose GF, a generators fusion method to integrate local generators of multi-nodes into a new global generator. To evaluate the performance of DCIGAN, we conduct experiments on six datasets under various parameter settings on both two and multi nodes distributed scenarios. Extensive experiments confirm that DCIGAN outperform the general baselines and achieve classification accuracy closes to the method of data aggregating.

关键词： Generators Generative adversarial networks distributed databases Diseases Servers Hospitals Training

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HUNTER: AI based holistic resource management for sustainable cloud computing

arXiv

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arXiv 2021年

作者： Tuli, Shreshth Gill, Sukhpal Singh Xu, Minxian Garraghan, Peter Bahsoon, Rami Prof, Scharam Dustdar Sakellariou, Rizos Rana, Omer Buyya, Rajkumar Casale, Giuliano Jennings, Nicholas R. Department of Computing Imperial College London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China School of Computing and Communications Lancaster University United Kingdom School of Computer Science University of Birmingham United Kingdom Distributed Systems Group Vienna University of Technology Austria Department of Computer Science University of Manchester United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energy-efficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively. © 2021, CC BY.

关键词： Energy efficiency

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ECDT: Exploiting Correlation Diversity for Knowledge Transfer in Partial Domain Adaptation

ECDT: Exploiting Correlation Diversity for Knowledge Transfe...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Shichang He Xuan Liu Xinning Chen Ying Huang College of Computer Science and Electronic Engineering Hunan University P.R. China The Science and Technology on Paraller and Distributed Processing Laboratory Tsinghua University P.R. China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Domain adaptation aims to transfer knowledge across different domains and bridge the gap between them. While traditional knowledge transfer considers identical domain, a more realistic scenario is to transfer from a larger and more diverse source domain to a smaller target domain, which is referred to as partial domain adaptation (PDA). However, matching the whole source domain to the target domain for PDA might produce negative transfer. Samples in the shared classes should be carefully selected to mitigate negative transfer in PDA. We observe that the correlations between different target domain samples and source domain samples are diverse: classes are not equally correlated and moreover, different samples have different correlation strengthes even when they are in the same class. In this study, we propose ECDT, a novel PDA method that Exploits the Correlation Diversity for knowledge Transfer between different domains. We propose a novel method to estimate target domain label space that utilizes the label distribution and feature distribution of target samples, based on which outlier source classes can be filtered out and their negative effects on transfer can be mitigated. Moreover, ECDT combines class-level correlation and instance-level correlation to quantity sample-level transferability in domain adversarial network. Experimental results on three commonly used cross-domain object data sets show that ECDT is superior to previous partial domain adaptation methods.

关键词： Correlation Handheld computers Tagging Sensors Task analysis Knowledge transfer

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Exploring frame segmentation networks for temporal action localization

arXiv

引用

arXiv 2019年

作者： Yang, Ke Shen, Xiaolong Qiao, Peng Li, Shijie Li, Dongsheng Dou, Yong National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

Temporal action localization is an important task of computer vision. Though many methods have been proposed, it still remains an open question how to predict the temporal location of action segments precisely. Most state-of-the-art works train action classifiers on video segments pre-determined by action proposal. However, recent work found that a desirable model should move beyond segment-level and make dense predictions at a fine granularity in time to determine precise temporal boundaries. In this paper, we propose a Frame Segmentation Network (FSN) that places a temporal CNN on top of the 2D spatial CNNs. Spatial CNNs are responsible for abstracting semantics in spatial dimension while temporal CNN is responsible for introducing temporal context information and performing dense predictions. The proposed FSN can make dense predictions at frame-level for a video clip using both spatial and temporal context information. FSN is trained in an end-to-end manner, so the model can be optimized in spatial and temporal domain jointly. We also adapt FSN to use it in weakly supervised scenario (WFSN), where only video level labels are provided when training. Experiment results on public dataset show that FSN achieves superior performance in both frame-level action localization and temporal action localization. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Untrusted Hardware Causes Double-Fetch Problems in the I/O Memory

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Journal of computer science & technology 2018年第3期33卷 587-602页

作者： Kai Lu Peng-Fei Wang Gen Li Xu Zhou College of Computer National University of Defense Technology Changsha 410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China Collaborative Innovation Center of High-Performance Computing National University of Defense Technology Changsha 410073 China

The double fetch problem occurs when the data is maliciously changed between two kernel reads of the supposedly same data, which can cause serious security problems in the kernel. Previous research focused on the double fetches between the kernel and user applications. In this paper, we present the first dedicated study of the double fetch problem between the kernel and peripheral devices （aka. the hardware double fetch）. Operating systems communicate with peripheral devices by reading from and writing to the device mapped I/O （input and output） memory. Owing to the lack of effective validation of the attached hardware, compromised hardware could flip the data between two reads of the same I/O memory address, causing a double fetch problem. We propose a static pattern-matching approach to identify the hardware double fetches from the Linux kernel. Our approach can analyze the entire kernel without relying on the corresponding hardware. The results are categorized and each category is analyzed using case studies to discuss the possibility of causing bugs. We also find four previously unknown double-fetch vulnerabilities, which have been confirmed and fixed after reporting them to the maintainers.

关键词： hardware double fetch double-fetch bug I/O memory peripheral device double-fetch vulnerability

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Rev Rec: A two-layer reviewer recommendation algorithm in pull-based development model

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Journal of Central South University 2018年第5期25卷 1129-1143页

作者：杨程张迅晖曾令斌范强王涛余跃尹刚王怀民 National Laboratory for Parallel and Distributed Processing College of ComputerNational University of Defense Technology

Code review is an important process to reduce code defects and improve software quality. In social coding communities like GitHub, as everyone can submit Pull-Requests, code review plays a more important role than ever before, and the process is quite time-consuming. Therefore, finding and recommending proper reviewers for the emerging Pull-Requests becomes a vital task. However, most of the current studies mainly focus on recommending reviewers by checking whether they will participate or not without differentiating the participation types. In this paper, we develop a two-layer reviewer recommendation model to recommend reviewers for Pull-Requests （PRs） in GitHub projects from the technical and managerial perspectives. For the first layer, we recommend suitable developers to review the target PRs based on a hybrid recommendation method. For the second layer, after getting the recommendation results from the first layer, we specify whether the target developer will technically or managerially participate in the reviewing process. We conducted experiments on two popular projects in GitHub, and tested the approach using PRs created between February 2016 and February 2017. The results show that the first layer of our recommendation model performs better than the previous work, and the second layer can effectively differentiate the types of participation.

关键词： Pull-Request code reviewer recommendation GitHub open source community

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