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DEISA: Dask-Enabled In Situ Analytics 28

DEISA: Dask-Enabled In Situ Analytics

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28th Annual IEEE International conference on High Performance Computing, Data, and Analytics (HiPC)

作者： Gueroudji, Amal Bigot, Julien Raffin, Bruno Univ Paris Saclay UVSQ CNRS CEAMaison Simulat F-91191 Gif Sur Yvette France Univ Grenoble Alpes Inria CNRS Grenoble INPLIG F-38000 Grenoble France

ISBN: (纸本)9781665410168

A widening performance gap is separating CPU performance and IO bandwidth on large scale systems. In some fields such as weather forecast and nuclear fusion, numerical models generate such amounts of data that classical post hoc processing is not feasible anymore due to the limits in both storage capacity and IO performance. In situ approaches are attractive to bypass disk accesses in these cases and fully leverage the UPC platform. They are however often complex to set up and can require to re-develop parallel versions of the analysis from scratch. In this paper we propose a hybrid model that is well suited for in situ workflows that combine regular simulations and irregular analytics. Our model couples the bulk synchronous parallel paradigm for simulation with a distributed task-based one for analysis. This reduces complexity and leverages the best of each of these two powerful paradigms. We validate the model with a prototype, called DEISA, that supports coupling MPI parallel codes with analyses written using Dask. This implementation requires minimal modifications of both the simulation and analysis codes compared to their post hoc counterpart. It give access to an already existing rich ecosystem to be used in situ such as the parallel versions of Numpy, Pandas and scikit-learn. Experiments in configurations up to 1024 cores show that DEISA can improve the simulation wallclock time (excluding analysis) by a factor up to 3 and the total experiment (including analysis) *** cost by a factor of up to 5 compared to parallel post hoc with plain Dask while requiring the modification of only two lines of python code, three of YAML, and none at all in a C simulation code already instrumented with PDI Data Interface.

关键词： In situ processing code coupling task-based programming MPI Dask

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An Online Detection System for LDoS attack based on XGBoost

An Online Detection System for LDoS attack Based on XGBoost

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

作者： Jialun Deng Li Cheng HaiWen Yuan Kuanlei Zheng Xuan Li Quli Li College of Electrical Information Wuhan Institute of Technology Wuhan China

Low-rate-Denial-of-Service (LDoS) attack poses a significant threat to network service quality and even has the potential to cause network paralysis. Therefore, it is urgent to conduct research on LDoS. However, the deployment of extension modules for online attack detection in traditional networks is a formidable undertaking due to the limited flexibility and scalability inherent in conventional network equipment. Software-Defined networking (SDN) offers a viable solution to this challenge. SDN represents an innovative network architecture that delivers both flexibility and programmability by segregating the network Control Plane from the Data forwarding Plane, thereby enabling real-time detection of LDoS attack. In this paper, we implemented a real-time LDoS attack detection system on SDN, with Extreme Gradient Boosting (XGBoost) serving as the primary method for identifying LDoS attack. Furthermore, we employed RYU as the SDN controller, utilized the Mininet simulation software to create network topologies, and employed attack code to simulate a real LDoS attack environment. The XGBoost classifier is applied to detect LDoS attack in the real-time network traffic collected by the OpenFlow protocol, and its performance is compared with that of several other classifiers. Experimental results conclusively demonstrated that XGBoost exhibited the most optimal detection performance, achieving accuracy and F1 scores of 97.43% and 98.37%,respectively.

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Accelerating Actor-based Applications with parallel Patterns 27

Accelerating Actor-based Applications with Parallel Patterns

引用

27th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Rinaldi, Luca Torquati, Massimo Mencagli, Gabriele Danelutto, Marco Menga, Tullio Univ Pisa Dept Comp Sci Pisa Italy ATS Adv Technol Solut SpA Milan Italy

ISBN: (纸本)9781728116440

parallel programmers mandate high-level parallel programming tools allowing to reduce the effort of the efficient parallelization of their applications. parallel programming leveraging parallel patterns has recently received renovated attention thanks to their clear functional and parallel semantics. In this work, we propose a synergy between the well-known Actors-based programming model and the pattern-based parallelization methodology. We present our preliminary results in that direction, discussing and assessing the implementation of the Map parallel pattern by using an Actor-based software accelerator abstraction that seamlessly integrates within the C++ Actor Framework (ICAF). The results obtained on the Intel Xeon Phi KNL platform demonstrate good performance figures achieved with negligible programming efforts.

关键词： Computational modeling Message systems Software C++ languages parallel programming Semantics

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19th IEEE International Symposium on parallel and distributed processing with Applications, 11th IEEE International conference on Big Data and Cloud Computing, 14th IEEE International conference on Social Computing and networking and 11th IEEE International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

19th IEEE International Symposium on Parallel and Distribute...

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19th IEEE International Symposium on parallel and distributed processing with Applications, 11th IEEE International conference on Big Data and Cloud Computing, 14th IEEE International conference on Social Computing and networking and 11th IEEE International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

ISBN: (纸本)9781665435741

The proceedings contain 222 papers. The topics discussed include: DRL-deploy: adaptive service function chains deployment with deep reinforcement learning;accuracy vs. efficiency: achieving both through hardware-aware quantization and reconfigurable architecture with mixed precision;cmss: collaborative modeling of safety and security requirements for network protocols;FGPA: fine-grained pipelined acceleration for depthwise separable CNN in resource constraint scenarios;Dyacon: JointCloud dynamic access control model of data security based on verifiable credentials;understanding the runtime overheads of deep learning inference on edge devices;and alleviating imbalance in synchronous distributed training of deep neural networks.

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Traceable Ring Signature Scheme in Internet of Vehicles based on Lattice Cryptography

Traceable Ring Signature Scheme in Internet of Vehicles Base...

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

作者： Wei Liao Lansheng Han Peng Chen Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

Lattice cryptography, as a recognized Cryptosystem that can resist quantum computation, has great potential for development. Lattice based signature scheme is currently a research focus. In this paper, the traceable ring signature scheme based on lattice cryptography in the Internet of Vehicles (IoV) environment is studied. Firstly, users’ public and private keys are generated on the lattice, which effectively reduces the key size and time cost. Secondly, the scheme also achieves traceability by adding additional information. The scheme satisfies strong unforgeability, traceability, and anonymity. In addition, the traceable ring signature scheme based on lattice cryptography is applied in the IoV environment to effectively protect user privacy. Model communication can not only protect user privacy, but also trace the signer when necessary, achieving a balance between security and privacy. The simulation results show that the average delay of IoV of our scheme is better than that of similar schemes. The research in this paper improves the security of data transmission in the IoV environment, enables it to adapt to the complex and changeable new network environment, and effectively guarantees the information security of vehicles and owners.

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50th International conference on parallel processing Workshop, ICPP 2021 - Proceedings

50th International Conference on Parallel Processing Worksho...

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50th International conference on parallel processing Workshop, ICPP 2021

ISBN: (纸本)9781450384414

The proceedings contain 38 papers. The topics discussed include: automated arrhythmia detection using Hilbert-Huang transform based convolutional neural network;self-stabilization with selfish agents;new evacuation guidance using augmented reality for emergency rescue evacuation support system (ERESS);analysis on nursing care activity related stress level for reduction of caregiving workload;warp-centric k-nearest neighbor graphs construction on GPU;explaining the classification performance of supervised and semi-supervised methods for automated sparse matrix format selection;an intelligent parallel distributed streaming framework for near real-time science sensors and high resolution medical images;and intra- and inter- layer transformation to reduce memory traffic for CNN computation.

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Proceedings - 20th euromicro International conference on parallel, distributed and network-based processing, PDP 2012

Proceedings - 20th Euromicro International Conference on Par...

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20th euromicro International conference on parallel, distributed and network-based processing, PDP 2012

ISBN: (纸本)9780769546339

The proceedings contain 87 papers. The topics discussed include: context map for navigating the physical world;robust and tuneable family of gossiping algorithms;efficiency-aware Jobs allocation for e-Science environments;performance evaluations of a BSP algorithm for state space construction of security protocols;a dynamic deadlock detection/resolution algorithm with linear message complexity;a dynamic distributed algorithm for read write locks;locality-aware dynamic mapping for multithreaded applications;interaction list compression in large parallel particle simulations on multicore systems;a federated data zone for the arts and humanities;bit rate reduction video transcoding with distributed computing;LAMBDA-the LSDF execution framework for data intensive applications;and dynamic serialization: improving energy consumption in eager-eager.

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A robust fixed path-based routing scheme for protecting the source location privacy in WSNs 17

A robust fixed path-based routing scheme for protecting the ...

引用

17th IEEE International conference on Mobility, Sensing and networking (MSN)

作者： Hu, Lingling Liu, Liang Liu, Yulei Zhai, Wenbin Wang, Xinmeng Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665406680

With the development of wireless sensor networks (WSNs), WSNs have been widely used in various fields such as animal habitat detection, military surveillance, etc. This paper focuses on protecting the source location privacy (SLP) in WSNs. Existing algorithms perform poorly in non-uniform networks which are common in reality. In order to address the performance degradation problem of existing algorithms in non-uniform networks, this paper proposes a robust fixed path-based random routing scheme (RFRR), which guarantees the path diversity with certainty in non-uniform networks. In RFRR, the data packets are sent by selecting a routing path that is highly differentiated from each other, which effectively protects SLP and resists the backtracking attack. The experimental results show that RFRR increases the difficulty of the backtracking attack while safekeeping the balance between security and energy consumption.

关键词： wireless sensor network location privacy protection routing protocol fixed path

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Focus on Inherent Attributes for Temporal Knowledge Graph Completion

Focus on Inherent Attributes for Temporal Knowledge Graph Co...

引用

International Joint conference on Neural networks (IJCNN)

作者： Chen, Kai Li, Chenchen Li, Aiping Gao, Jingsheng Ma, Sixia Natl Univ Def Technol Coll Comp Changsha Peoples R China

ISBN: (纸本)9780738133669

In the last few years, the availability of temporal knowledge graphs has stimulated extensive research in temporal knowledge graph completion (TKGC) and temporal knowledge graph embedding (TKGE), where temporal information is added to static knowledge graphs that have been widely applied previously. However, most existing methods, such as current state-of-the-art DE-SimplE and TeRo, learn embeddings of temporalevolving attributes, overlooking the inherent attributes inside entities, where some essential and inherent features are included. In this paper, we introduce a novel method utilizing Inherent Attributes with a Graph Attention network (IAGAT) for TKGC. Our IAGAT extracts inherent attributes from sufficient features corresponding to various facts at different time stamps, to obtain the inherent embeddings. And we take advantages of previous rotation based methods to obtain the temporal-evolving embeddings. Through extensive experiments and sufficient comparisons, we demonstrate our model outperforms the current state-of-the-art models on link prediction task. Furthermore, we evaluate and prove the necessity of the inherent attributes in performance improvement, and study how our model functions in extracting inherent features.

关键词： Knowledge engineering distributed processing Neural networks Predictive models Feature extraction Task analysis Research and development

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Semi-asynchronous Federated Learning Optimized for NON-IID Data Communication based on Tensor Decomposition

Semi-asynchronous Federated Learning Optimized for NON-IID D...

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

作者： Qidong Wu Xuemei Fu Xiangli Yang Ruonan Zhao Chengqian Wu Tinghua Zhang School of Computer Science and Technology Hainan University Haikou China School of Information and Communication Engineering Hainan University Haikou China

Federated Learning (FL) is a new distributed machine learning framework that enables reliable collaborative training without collecting users’ private data, it can successfully address the issue of data silos. However, FL has had trouble scaling to statistically varied data and large-scale models because of its frequent communication and average aggregation procedures. In this paper, we propose a Semi-asynchronous Stochastic Controlled Averaging based on Tensor Decomposition for Federated Learning (SATD-SCAFFOLD), in which we perform tensor decomposition on the client model to reduce communication costs. In addition, we design a semi-asynchronous aggregation approach for the server, which can prevent server from wasting too much time waiting for data from late clients in the case of an unstable network, and the overall model still has excellent performance. Thorough experiments demonstrate that our proposed SATDSCAFFOLD algorithm can reduce both the communication cost and convergence time while maintaining good performance.

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