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A Machine Learning-Based Elastic Strategy for Operator parallelism in a Big Data Stream computing System 1

引用

12th international conference on Broadband Communications, networks, and Systems, BROADNETS 2021

作者： Li, Wei Sun, Dawei Gao, Shang Buyya, Rajkumar School of Information Engineering China University of Geosciences Beijing100083 China School of Information Technology Deakin University MelbourneVIC3216 Australia Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (数字)9783030934798

ISBN: (纸本)9783030934781

Elastic scaling in/out of operator parallelism degree is needed for processing real time dynamic data streams under low latency and high stability requirements. Usually the operator parallelism degree is set when a streaming application is submitted to a stream computing system and kept intact during runtime. this may substantially affect the performance of the system due to the fluctuation of input streams and availability of system resources. To address the problems brought by the static parallelism setting, we propose and implement a machine learning based elastic strategy for operator parallelism (named Me-Stream) in big data stream computing systems. the architecture of Me-Stream and its key models are introduced, including parallel bottleneck identification, parameter plan generation, parameter migration and conversion, and instances scheduling. Metrics of execution latency and process latency of the proposed scheduling strategy are evaluated on the widely used big data stream computing system Apache Storm. the experimental results demonstrate the efficiency and effectiveness of the proposed strategy. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Machine learning

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threat Detection-Oriented network Security Situation Assessment Method 1

引用

4th EAI international conference on Security and Privacy in New computing Environments, SPNCE 2021

作者： Yang, Hongyu Zhang, Zixin Zhang, Liang School of Safety Science and Engineering Civil Aviation University of China Tianjin300300 China School of Computer Science and Technology Civil Aviation University of China Tianjin300300 China School of Information University of Arizona TucsonAZ85721 United States

ISBN: (数字)9783030967918

ISBN: (纸本)9783030967901

To analyze the impact of network threats and accurately reflect the security situation of the network, we propose a threat detection-oriented network security situation assessment method. Firstly, a network threat detection model is designed. the model is composed of parallel feature extraction (PFE) with the sparse auto-encoder and an improved bi-directional gate recurrent (IBiGRU) with the attention mechanism. the PFE is established to extract the key information of different network threats and fuse the extracted features with the original information. Secondly, the PFE-IBiGRU is used to detect the threats in the network, and the occurrence number of each attack type and the false alarm reduction matrix are counted. Finally, according to the model detection results, combined with the proposed network security situation quantification method, the network security situation value is calculated. the experimental results show that our method is more accurate for identifying network attacks and can effectively and comprehensively evaluate the overall situation of network security. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Feature extraction

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Optimizing GNN on ARM Multi-Core Processors

Optimizing GNN on ARM Multi-Core Processors

引用

IEEE international conference on High Performance computing and Communications (HPCC)

作者： Chaorun Liu Huayou Su Yong Dou Kangkang Chen Yanjie Sun National University of Defense and Technology China

Graph Neural network (GNN) has shown great success in graph learning, including physics systems, protein interfaces, disease classification, molecular fingerprints, etc. Due to the complexity of the real-world tasks and the big graph datasets, current GNN models become increasingly bigger and more complicated to enhance the learning ability and prediction accuracy. In addition, GNN contains two main major components graph operation and neural network (NN) operation, both are executed alternately during processing. the interleaved complex processing of GNN poses a challenge for many computational platforms, especially for those without accelerators. Current optimization frameworks solely for deep learning or graph computing cannot achieve good performance on GNN. In this work, we first investigate the performance bottlenecks when handling GNN over multi-core processors. then, we perform a set of optimization strategies to leverage the capability of multi-core processors for GNN processing. Specifically, we build a set of microkernels for graph operations of GNN with assembly instructions that can exploit the capability of SIMD processing units within a core and implement a task allocator based on a greedy algorithm to balance the workloads between the cores of CPU. In addition, we use some strategies to optimize NN operation for GNN, according to the characteristics of NN operation in GNN. Experimental results show that the proposed methods can achieve a maximum of 2.88x and 4.08x performance improvements for various GNN models on Phytium 2000+ and Kunpeng 920 over the state-of-the-art GNN framework.

关键词： Proteins Program processors Multicore processing parallel programming Artificial neural networks Predictive models Graph neural networks

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ODCP: Optimizing Data Caching and Placement in Distributed File System Using Erasure Coding 17th

ODCP: Optimizing Data Caching and Placement in Distributed F...

引用

17th ifip WG 10.3 international conference on network and parallel computing, NPC 2020

作者： Wu, Shuhan Li, Yunchun Yang, Hailong Luan, Zerong Li, Wei School of Computer Science and Engineering Beihang University Beijing100191 China College of Life Sciences and Bioengineering Beijing University of Technology Beijing100083 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi214125 China

ISBN: (纸本)9783030794774

Many current distributed file systems use erasure-coding based data redundancy techniques to improve the reliability of data storage. Such techniques can significantly improve the effective storage utilization. However, there are several drawbacks to the above techniques. Firstly, they introduce non-negligible computation overhead for decoding. Secondly, traditional data caching and placement strategies become less effective in such cases. To solve the above drawbacks, this paper proposes a new data cache allocation mechanism based on simulated annealing and a new data placement strategy based on convex optimization, which effectively reduces data block transmission delay and decoding delay. We have implemented the proposed data placement strategy in the real-world distributed file system Alluxio, and evaluated the performance of our strategy. Experiment results show that our strategy can significantly reduce the file read delay compared to traditional data placement strategies. © 2021, ifip international Federation for Information Processing.

关键词： Convex optimization

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Performance Investigations of DFMA-PON Systems Using a parallel IM/PM Transmitter for Dispersion Compensation 9

Performance Investigations of DFMA-PON Systems Using a Paral...

引用

9th international conference on Intelligent computing and Wireless Optical Communications (ICWOC)

作者： Bao, Hainan Gong, Xiaoxue Zhang, Qihan Hu, Ting Li, Rui Hou, Weigang Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China

ISBN: (纸本)9781665435949

In the digital filter multiple access (DFMA) passive optical network (PON) systems based on direct detection (DD), due to the existence of fiber dispersion, the power fading phenomenon occurs when using a single photo detector (PD) to make optical signals transform into electrical signals, which affects the transmission capacities of the system. therefore, so as to ensure the effective transmission capacities of the DFMA-PON system, this paper proposes a scheme that uses a parallel intensity modulation (IM)/phase modulation (PM) structure at the transmitter. According to the frequency responses of IM signal and PM signal are complementary in the same frequency range, we can use one way of modulation to compensate the other one which was damaged by dispersion. So the sub channels generated by the digital orthogonal filters in the DFMA-PON are allocated to the corresponding IM or PM modulation method. the numerical simulation results illustrate that the system transmission rate can reach 66.67Gb/s when bit error rate(BER) is lower than 3. 8 x 10(-3) after a 25km standard single mode fiber(SSMF) transmission, it proves that the scheme proposed in this paper can effectively avoid the impact of power fading due to the chromatic dispersion on the system.

关键词： digital filter multiple access (DFMA) direct detection (DD) power fading intensity modulation (IM) phase modulation (PM)

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Location-Based Service Recommendation for Cold-Start in Mobile Edge computing 17th

Location-Based Service Recommendation for Cold-Start in Mobi...

引用

17th ifip WG 10.3 international conference on network and parallel computing, NPC 2020

作者： Yu, Mengshan Fan, Guisheng Yu, Huiqun Chen, Liang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030794774

With the rapid development of the 5G and Internet of things (IoT), mobile edge computing has gained considerable popularity in academic and industrial field, which provides physical resources closer to end users. Service recommendation in such a distributed environment is a hot issue. However, the cold-start problem for service recommendation in mobile edge computing is still urgent to be solved. In this paper, we propose a service recommendation method based on collaborative filtering (CF) and user location, by comprehensively considering the characteristic of services at the edge and mobility of users. In detail, we first synthesize the service characteristics of each dimension through multidimensional weighting method. We further introduce the idea of Inverse_CF_Rec to the traditional CF and then predict the lost QoS value to solve the problem of sparse data. Finally, a recommendation algorithm based on predicted QoS value and user geographic location is proposed to recommend appropriate services to users. the experimental results show that our multidimensional inverse similarity recommendation algorithm based on collaborative filtering (MDICF) outperforms Inverse_CF_Rec in terms of the accuracy of recommendation. © 2021, ifip international Federation for Information Processing.

关键词： Location based services

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FNotify: A Low-Latency and Scalable Publish/Subscribe System using RDMA

FNotify: A Low-Latency and Scalable Publish/Subscribe System...

引用

IEEE international conference on High Performance computing and Communications (HPCC)

作者： Yusen Xu Sijie Shen Mingcong Han Rong Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University Shanghai China

Publish/subscribe systems are widely used as messaging middleware in various scenarios, such as online business, financial trading, and social media. However, the network overhead intro-duced by the pub/sub system can severely impact application performance. Especially in the latency-sensitive applications of the data center, optimizing the latency of publishing or delivering messages of the pub/sub system is particularly important. RDMA network has gained popularity in relevant applications due to its low latency, high bandwidth and ability to bypass the CPU. Never-theless, it is still challenging to design and optimize the pub/sub system using RDMA. through an in-depth analysis of RDMA features, we design and implement FNotify, a high-performance RDMA-accelerated pub/sub system. FNotify optimizes latency and CPU load using in-memory message management and one-sided RDMA primitives for message collection. FNotify also proposes a topic-based partitioning scheme to achieve good scal-ability. We conducted a comprehensive evaluation and analysis on an 8-node RDMA cluster to verify the validity of our design by comparing the performance of FNotify with Apache Kafka (a state-of-the-art pub/sub system) in a typical pub/sub application scenario and various micro-benchmarks. Experimental results show that FNotify improves overall performance by $1.5\times$ to $3.9\times$ and reduces end-to-end latency by more than $20\times$ compared to Apache Kafka.

关键词： Data centers Publishing Social networking (online) Scalability Semantics Systems architecture Low latency communication

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Compiler-Assisted Operator Template Library for DNN Accelerators 17th

Compiler-Assisted Operator Template Library for DNN Accelera...

引用

17th ifip WG 10.3 international conference on network and parallel computing, NPC 2020

作者： Li, Jiansong Cao, Wei Dong, Xiao Li, Guangli Wang, Xueying Liu, Lei Feng, Xiaobing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030794774

Despite many dedicated accelerators are gaining popularity for their performance and energy efficiency in the deep neural network (DNN) domain, high-level programming support for these accelerators remains thin. In contrast to existing researches targeting the whole DNNs, we choose to dive into details and review this problem from a finer-grained level, operators. Due to performance concerns, operator programmers may have to take hand-written assembly as their first choice, which is error-prone and involves many programming chores. To alleviate this problem, we propose TOpLib, a compiler-assisted template library. By providing a unified user-view abstraction, TOpLib allows programmers to express computational kernels with high-level tensor primitives, which will be automatically lowered into low-level intrinsic primitives via expression templates. Moreover, considering memory management is performance critical and the optimization strategy of expression template is limited to enumeration based rewriting rules, we implement TOpLib with a compiler-assisted approach. We address the memory reuse challenges into the compiler, which allows TOpLib to make full use of on-chip buffers and result in better performance. Experiments over 55 typical DNN operators demonstrate that TOpLib can generate scalable code with performance faster than or on par with hand-written assembly versions. © 2021, ifip international Federation for Information Processing.

关键词： Deep neural networks

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CH-Go: Online Go System Based on Chunk Data Storage

CH-Go: Online Go System Based on Chunk Data Storage

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Hui Lu Chuan Li Yiming Yang Cheng Li Ashraful Islam Computer School Sichuan University Chengdu China School of Computing National University of Singapore Singapore Singapore

the training and running of an online Go system require the support of effective data management systems to deal with vast data, such as the initial Go game records, the feature data set obtained by representation learning, the experience data set of self-play, the randomly sampled Monte Carlo tree, and so on. Previous work has rarely mentioned this problem, but the ability and efficiency of data management systems determine the accuracy and speed of the Go system. To tackle this issue, we propose an online Go game system based on the chunk data storage method (CH-Go), which processes the format of 160k Go game data released by Kiseido Go Server (KGS) and designs a Go encoder with 11 planes, a parallel processor and generator for better memory performance. Specifically, we store the data in chunks, take the chunk size of 1024 as a batch, and save the features and labels of each chunk as binary files. then a small set of data is randomly sampled each time for the neural network training, which is accessed by batch through yield method. the training part of the prototype includes three modules: supervised learning module, reinforcement learning module, and an online module. Firstly, we apply Zobrist-guided hash coding to speed up the Go board construction. then we train a supervised learning policy network to initialize the self-play for generation of experience data with 160k Go game data released by KGS. Finally, we conduct reinforcement learning based on REINFORCE algorithm. Experiments show that the training accuracy of CH-Go in the sampled 150 games is 99.14%, and the accuracy in the test set is as high as 98.82 %. Under the condition of limited local computing power and time, we have achieved a better level of intelligence. Given the current situation that classical systems such as GOLAXY are not free and open, CH-Go has realized and maintained complete Internet openness.

关键词： Training Monte Carlo methods Smart cities Supervised learning Neural networks Games Reinforcement learning

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4th ifip TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2021

4th IFIP TC 12 International Conference on Computational Int...

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4th ifip TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2021

ISBN: (纸本)9783030925994

the proceedings contain 20 papers. the special focus in this conference is on Computational Intelligence in Data Science. the topics include: Predicting Customer Churn in Banking Based on Data Mining Techniques;early Prediction of Diabetes Disease Based on Data Mining Techniques;an Application-Driven IoT Based Rooftop Farming Model for Urban Agriculture;enhanced Ant Colony Optimization Algorithm for Optimizing Load Balancing in Cloud computing Platform;Captioning of Image Conceptually Using BI-LSTM Technique;analysis of Land Cover Type Using Landsat-8 Data;rule Based Combined Tagger for Marathi Text;preface;a Scalable Data Pipeline for Realtime Geofencing Using Apache Pulsar;evaluating Candidate Answers Based on Derivative Lexical Similarity and Space Padding for the Arabic Language;ontology Model for Spatio-Temporal Contexts in Smart Home Environments;automatic Detection of Buildings Using High Resolution Images for Medium Density Regions;A Liver Segmentation Algorithm with Interactive Error Correction for Abdominal CT Images: A Preliminary Study;pixel Based Adversarial Attacks on Convolutional Neural network Models;continual Learning for Classification Problems: A Survey;Detection of Flooded Regions from Satellite Images Using Modified UNET;Blockchain Based End-To-End Tracking System for COVID Patients;decentralized Application Using Ethereum Blockchain on Performance Analysis Considering E-Voting System.

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