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检索条件"任意字段=International Symposium on Databases in Parallel and Distributed Systems"

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Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks

Fast Convergence to Fairness for Reduced Long Flow Tail Late...

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international symposium on parallel and distributed Processing (IPDPS)

作者： John Snyder Alvin R. Lebeck Department of Computer Science Duke University

Many data-intensive applications, such as distributed deep learning and data analytics, require moving vast amounts of data between compute servers in a distributed system. To meet the demands of these applications, datacenters are adopting Remote Direct Memory Access (RDMA), which has higher bandwidth and lower latency than traditional kernel-based networking. To ensure high performance of RDMA networks, congestion control manages queue depth on switches, and historically focused on moderating queue depth to ensure small flows complete quickly. Unfortunately, one side-effect of many common decisions is that large flows are starved of bandwidth. This negatively impacts the flow completion time (FCT) of large, bandwidth-bound flows, which are integral to the performance of data-intensive applications. The FCT is particularly impacted at the tail, which is increasingly critical for predictable application performance. We identify the root causes of the poor performance for long flows and measure the impact. We then design mechanisms that improve long flow FCT without compromising small flow performance. Our evaluations show that these improvements reduce 99.9% tail FCT of long flows by over 2x.

关键词： distributed processing Protocols Additives distributed databases Tail Bandwidth Throughput

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Poisoning Attacks on Federated-learning based NIDS

Poisoning Attacks on Federated-learning based NIDS

引用

international symposium on Computing and Networking Workshops (CANDARW)

作者： Mariama Mbow Takeshi Takahashi Kouichi Sakurai Graduate School of Information Science and Electrical Engineering Kyushu University Japan National Institute of Information and Communications Technology Tokyo Japan Faculty of Information Science and Electrical Engineering Kyushu University Japan

ISBN: (数字)9798331505349

ISBN: (纸本)9798331505356

Federated learning (FL) offers a decentralized framework for training network intrusion detection systems (NIDS) across multiple entities while preserving data privacy. However, recent studies have shown that federated learning is vulnerable to poisoning attacks. This paper investigates poisoning attacks against FL-based NIDS. Existing attack methods on NIDS, such as label-flipping techniques, can be detected by advanced defense mechanisms based on statistical analysis. In this study, we propose a novel poisoning attack aimed at deceiving the global model into misclassifying targeted labels. Our approach utilizes GANs to generate effective yet stealthy poisoned data misclassified as benign, thus evading detection. Experimental results show the approach degrades the model’s performance, highlighting the need for more robust defense strategies.

关键词： Training Data privacy Federated learning Statistical analysis Conferences Network intrusion detection distributed databases Data models Servers Computer crime

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UpDown: Combining Scalable Address Translation with Locality Control

UpDown: Combining Scalable Address Translation with Locality...

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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the international Conference for

作者： Yuqing Wang Swann Perarnau Andrew A. Chien Department of Computer Science University of Chicago Chicago USA Argonne National Laboratory Chicago USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Global shared memories of petabytes are increasingly useful for applications, but traditional page-based techniques do not scale (limited reach), and scalable techniques such as segments fail to provide needed locality control. We propose a novel two-level translation scheme, UpDown, that provides compact, efficient access control to distributed segments of arbitrary size and data layout control, solving problems of reach and data locality. UpDown’s novel two-level structure separates access control from data layout, allowing applications to manage locality cheaply, without privileged *** evaluate UpDown against page-based systems, using big data computations. Our results show that UpDown is scalable and provides effective global data locality management. UpDown’s translation states are an average of ~620 billion times smaller total and ~130 billion times smaller local translation state. Simulations with synthetic traces show that the two-level translation scheme enables fast, user-level management of data locality in a scalable parallel machine.

关键词： Access control Translation High performance computing Conferences Computational modeling Layout distributed databases parallel machines Big Data Data models

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Mnemonic: A parallel Subgraph Matching System for Streaming Graphs

Mnemonic: A Parallel Subgraph Matching System for Streaming ...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Bibek Bhattarai Howie Huang George Washington University

Finding patterns in large highly connected datasets is critical for value discovery in business development and scientific research. This work focuses on the problem of subgraph matching on streaming graphs, which provides utility in a myriad of real-world applications ranging from social network analysis to cybersecurity. Each application poses a different set of control parameters, including the restrictions for a match, type of data stream, and search granularity. The problem-driven design of existing subgraph matching systems makes them challenging to apply for different problem domains. This paper presents Mnemonic, a programmable system that provides a high-level API and democratizes the development of a wide variety of subgraph matching solutions. Importantly, Mnemonic also delivers key data management capabilities and optimizations to support real-time processing on long-running, high-velocity multi-relational graph streams. The experiments demonstrate the versatility of Mnemonic, as it outperforms several state-of-the-art systems by up to two orders of magnitude.

关键词： distributed processing Social networking (online) Real-time systems Distance measurement Computer security Pattern matching Optimization

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HINT: Designing Cache-Efficient MPI_Alltoall using Hybrid Memory Copy Ordering and Non-Temporal Instructions

HINT: Designing Cache-Efficient MPI_Alltoall using Hybrid Me...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Bharath Ramesh Nick Contini Nawras Alnaasan Kaushik Kandadi Suresh Mustafa Abduljabbar Aamir Shafi Hari Subramoni Dhabaleswar K. D K Panda Department of Computer Science and Engineering The Ohio State University Columbus USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Modern multi/many-core processors in HPC systems have hundreds of cores with deep memory hierarchies. HPC applications run at high core counts often experience contention between processes/threads on shared resources such as caches, leading to degraded performance. This is especially true for dense collective patterns, such as MPI_Alltoall, that have many concurrent memory transactions. The ordering of memory copies during the MPI_Alltoall operation can significantly affect performance as cache-efficient access patterns could potentially reduce cache misses. However, the correct access pattern depends on various factors, including cache associativity, cache sizes, coherence protocols, and memory layouts. This paper first identifies sources of bottlenecks in performing memory operations in an Alltoall. We propose different orderings for the memory copies in Alltoall operations and study their effectiveness for various message sizes. We overcome bandwidth bottlenecks related to repeated bus requests in the cache by proposing a hybrid memory copy scheme that combines regular temporal and non-temporal stores. Then, we implement an Alltoall algorithm that dynamically picks between memory orders based on their performance for different message sizes/number of processes. To the best of our knowledge, this is the first work that explores a combination of dynamic memory copy orders and non-temporal instructions for optimizing MPI_Alltoall operations. Our proposed solutions reduce the latency versus state-of-the-art solutions by up to 10x at the micro-benchmark level and 22.2% for the CPU time per loop in distributed Fast Fourier Transforms (FFTs) using P3DFFT.

关键词： distributed processing Protocols Program processors Fast Fourier transforms Heuristic algorithms Layout Coherence

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RPCRS: Human Activity Recognition Using Millimeter Wave Radar

RPCRS: Human Activity Recognition Using Millimeter Wave Rada...

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international Conference on parallel and distributed systems (ICPADS)

作者： Tingpei Huang Guoyong Liu Shibao Li Jianhang Liu College of Computer Science and Technology China University of Petroleum (East China) Qingdao China

Millimeter wave radar-based human activity recognition (HAR) technology has received much attention as a research hot-spot in recent years. Previous researches have demonstrated the feasibility of using millimeter wave radar for HAR. While existing work has achieved excellent performance in ideal environments, its application in life is still limited due to the intensive data collection required, the additional training needed to adapt to new domains (i.e., environments, people, and locations), and the high computational complexity associated with voxelization. To solve the above problems, we propose the radar point cloud recognition system RPCRS, which is capable of accurately recognizing human activities from noisy environments, has promising recognition performance for new users, environments and locations, and significantly reduces the computational overhead during system training. Firstly, RPCRS use the velocity information of the clustered point cloud data to extract the human activity subjects from the noisy background. Then, the size of the extracted non-uniform point cloud data is unified by removing or adding the number of point clouds. Secondly, in order to enhance the robustness of the system and reduce the data collection effort, we designed a data enhancement framework based on correlation between point cloud data and human activity changes. Finally, a lightweight neural network based on a multilayer perceptron (MLP) is used to classify the raw point cloud data of human activities, which reduces the computational complexity and memory requirements associated with voxelization. We evaluate our system with 5 different activities, which attains average accuracy of 95.40%. In addition, we evaluate the performance of the system in a new environment and with new users, which obtains an average accuracy of 94.53% and 95.08%, respectively.

关键词： Point cloud compression Training Correlation Shape distributed databases Millimeter wave radar Data collection

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DLion: Decentralized distributed Deep Learning in Micro-Clouds 21

DLion: Decentralized Distributed Deep Learning in Micro-Clou...

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30th international symposium on High-Performance parallel and distributed Computing (HPDC)

作者： Hong, Rankyung Chandra, Abhishek Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9781450382175

Deep learning (DL) is a popular technique for building models from large quantities of data such as pictures, videos, messages generated from edges devices at rapid pace all over the world. It is often infeasible to migrate large quantities of data from the edges to centralized data center(s) over WANs for training due to privacy, cost, and performance reasons. At the same time, training large DL models on edge devices is infeasible due to their limited resources. An attractive alternative for DL training distributed data is to use micro-clouds-small-scale clouds deployed near edge devices in multiple locations. However, micro-clouds present the challenges of both computation and network resource heterogeneity as well as dynamism. In this paper, we introduce DLion, a new and generic decentralized distributed DL system designed to address the key challenges in micro-cloud environments, in order to reduce overall training time and improve model accuracy. We present three key techniques in DLion: (1) Weighted dynamic batching to maximize data parallelism for dealing with heterogeneous and dynamic compute capacity, (2) Per-link prioritized gradient exchange to reduce communication overhead for model updates based on available network capacity, and (3) Direct knowledge transfer to improve model accuracy by merging the best performing model parameters. We build a prototype of DLion on top of TensorFlow and show that DLion achieves up to 4.2x speedup in an Amazon GPU cluster, and up to 2x speed up and 26% higher model accuracy in a CPU cluster over four state-of-the-art distributed DL systems.

关键词： Edge computing Deep learning Micro-clouds Resource allocation

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Toward Profiling IoT Processes for Remote Service Attestation

Toward Profiling IoT Processes for Remote Service Attestatio...

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international symposium on parallel and distributed Computing

作者： William A. Johnson John Housley Sheikh Ghafoor Stacy Prowell Cyber and Computer Sciences The Citadel Charleston SC USA Computer Science Tennessee Tech University Cookeville TN USA Computational Sciences Oak Ridge National Lab Oak Ridge TN USA

ISBN: (数字)9798350369199

ISBN: (纸本)9798350369205

The Internet of Things (IoT) is ubiquitous in modern life and is being used very widely in industrial control systems, smart grids, home appliances and many more. IoT devices are used to get information from sensors, process information, and send signals to actuators and controllers. In general these devices form a distributed computing network while in operation. Malware in IoT or any embedded devices is a potential security threat. Detecting malware in such a setting while in operation is non-trivial, because these low power devices may not have the computational ability to perform traditional security operations. Additionally, an infected device may cause other machines to misbehave by interfering with the data they receive. Remote Attestation is a security service designed to detect an infection in a device well before the malware detonates. Recent works have turned their attention to service attestation, or attesting the service that a network provides, rather than the individual devices themselves. Traditional remote attestation schemes use cryptographic hashing algorithms as evidence, but this approach generates exponentially more hashes as heterogeneous IoT devices are added to the network and their jobs’ complexity increases. In this work, we propose an approach to collect the contents of executable virtual memory from an IoT device. We develop a protocol based on our approach that can build a profile of a process running on an IoT device, such evidence can be analyzed automatically with high granularity. We validate our protocol by testing on both a personal computer, and a real-world Industrial IoT device under process injection attacks. Our results show that our protocol will be able to detect small changes to process memory over time, and that an injection as small as one word can be detected and read.

关键词： Protocols Scalability Production Malware Complexity theory Smart grids Internet of Things Cryptography distributed computing Testing

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HRaft: Adaptive Erasure Coded Data Maintenance for Consensus in distributed Networks

HRaft: Adaptive Erasure Coded Data Maintenance for Consensus...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Yulei Jia Guangping Xu Chi Wan Sung Salwa Mostafa Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China College of Engineering Mathematics and Physical Sciences University of Exeter Exeter UK

distributed data services usually rely on consensus protocols like Paxos and Raft to provide fault-tolerance and data consistency across global and local-distributed data centers. Erasure coding replication has appealing storage and network cost saving compared with full copy replication, which helps consensus protocols achieve low latency, high fault tolerance, and high throughput for data access. Applying erasure coding in consensus protocols directly will degrade the liveness level when the number of failure servers reaches a certain level. To address the challenge, CRaft just stores full copy replication instead of erasure coding replication when the number of failed servers reaches a certain threshold. In such situation, CRaft will be downgraded sharply to the same storage and network costs as Raft. To overcome the shortcoming of CRaft, we propose a protocol, called HRaft, which can adapt the placement of data blocks in order to always have enough blocks to recover the stored value when servers fail. By replenishing some coded blocks in healthy servers instead of full copy replication, it can avoid switching to the full replication when a certain threshold on the number of failures is reached. We designed and implemented a key-value (KV) storage prototype to validate the proposed protocol and evaluate its performance. The experimental results show HRaft can significantly reduce storage and network costs and improve write performance while keeping the liveness level compared to CRaft.

关键词： Fault tolerance Costs System performance Fault tolerant systems distributed databases Switches Throughput

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A Communication-Induced Checkpointing Algorithm for Consistent-Transaction in distributed Database systems 1

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8th international symposium on Security in Computing and Communications, SSCC 2020

作者： Mansouri, Houssem Pathan, Al-Sakib Khan Laboratory of Networks and Distributed Systems Computer Science Department Faculty of Sciences Ferhat Abbas Setif University 1 Setif Algeria Department of Computer Science and Engineering Independent University Dhaka Bangladesh

ISBN: (数字)9789811604225

ISBN: (纸本)9789811604218

For better protection of distributed systems, two well-known techniques are: checkpointing and rollback recovery. While failure protection is often considered a separate issue, it is crucial for establishing better secure distributed systems. This article is dedicated to the proposal of a new checkpointing algorithm for saving a consistent-transaction state in distributed databases ensuring that database management systems are able, after a failure, to recover the state of the database. The proposed communication-induced algorithm does not hamper the normal transaction processing and saves a global consistent-transaction state that records only the fully completed transactions. Analysis and experimental results of our proposal show that the proposed scheme saves a minimum number of forced checkpoints and has some performance gains compared to the alternative approaches. © 2021, Springer Nature Singapore Pte Ltd.

关键词： distributed database systems

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