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distributed Partial Simulation for Graph Pattern Matching

COMPUTER JOURNAL 2024年第1期67卷 110-126页

作者： Aouar, Aissam Yahiaoui, Said Sadeg, Lamia Nouali-Taboudjemat, Nadia Beghdad Bey, Kadda Ecole Mil Polytech BP 17 Algiers 16111 Algeria CERIST Res Ctr Sci & Tech Informat Algiers 16030 Algeria

Pattern matching in big graphs is important for different modern applications. Recently, this problem was defined in terms of multiple extensions of graph simulation, to reduce complexity and capture more meaningful results. These results were achieved through the relaxation of commonly used constraint in subgraph isomorphism pattern matching. Nevertheless, these graph simulation variant models are still too strict to provide results in many cases, especially when analyzed graphs contain anomalies and incomplete information. To deal with this issue, we introduce a new graph pattern matching (GPM) method, called partial simulation, capable of retrieving matches despite missing parts of the pattern graph, such as vertices and/or edges. Furthermore, considering the number and inequality of the outputs, we define a relevance function to compute a value expressing how each match vertex respects the pattern graph. Similarly, we define partial dual simulation GPM that returns vertices that satisfy a part of the dual simulation constraints and assigns a relevance value to them. Additionally, we provide distributed scalable algorithms to evaluate the proposed partial simulation methods based on the distributed vertex-centric programming paradigm. Finally, our experiments on real-world data graphs demonstrate the effectiveness of the proposed models and the efficiency of their associated algorithms.

关键词： big graphs graph pattern matching graph simulation distributed computing vertex-centric programming

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TDMiL: Tiny distributed Machine Learning for Microcontroller-Based Interconnected Devices

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IEEE ACCESS 2024年 12卷 167810-167826页

作者： Gulati, Mayank Zandberg, Koen Huang, Zhaolan Wunder, Gerhard Adjih, Cedric Baccelli, Emmanuel Free Univ Berlin Inst Comp Sci D-14195 Berlin Germany Inria F-91120 Palaiseau France

More and more, edge devices embark Artificial Neuron Networks. In this context, a trend is to simultaneously decentralize their training as much as possible while shrinking their resource requirements, both for inference and training-tasks that are typically intensive in terms of data, memory, and computation. At the edge's extremity, a specific challenge arises concerning the inclusion of microcontroller-based devices typically deployed in the IoT. So far, no general framework has been provided for that. Such devices not only have extremely challenging resource constraints (weak CPUs, slow network connections, memory budgets measured in kilobytes) but also exhibit high polymorphism, leading to large variability in computational performance among these devices. In this paper, we design and implement TDMiL, a versatile framework for distributed training, and transfer learning. TDMiL interconnects and combines logical components including CoAPerator (a central aggregator) and various tiny embedded software runtimes that are specifically tailored for networks comprising heterogeneous, resource-constrained devices built on diverse types of microcontrollers. We report on experiments conducted with the TDMiL framework, which we use to comparatively evaluate several schemes devised to address computational variability among distributed learning microcontroller-based devices, i.e., stragglers. Additionally, we release the code of our implementation of TDMiL as an open-source project, which is compatible with common commercial off-the-shelf IoT hardware and a well-known open-access IoT testbed.

关键词： Protocols Training Internet of Things Microcontrollers Machine learning Data models Tiny machine learning Distance learning Computer aided instruction Transfer learning distributed computing Federated learning distributed learning federated learning (FL) Internet of Things (IoT) machine learning microcontrollers TinyML-as-a-Service (TMLaaS)

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Modular Polynomial Codes for Secure and Robust distributed Matrix Multiplication

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第6期70卷 4396-4413页

作者： Karpuk, David Tajeddine, Razane WithSecure Corp W Intelligence Helsinki 00180 Finland Aalto Univ Dept Math & Syst Anal Espoo 02150 Finland Univ Helsinki Dept Comp Sci Helsinki 00014 Finland

We present Modular Polynomial (MP) Codes for Secure distributed Matrix Multiplication (SDMM). The construction is based on the observation that one can decode certain proper subsets of the coefficients of a polynomial with fewer evaluations than is necessary to interpolate the entire polynomial. We also present Generalized Gap Additive Secure Polynomial (GGASP) codes. Both MP and GGASP codes are shown experimentally to perform favorably in terms of recovery threshold when compared to other polynomials codes for SDMM which use the grid partition. Both MP and GGASP codes achieve the recovery threshold of Entangled Polynomial Codes for robustness against stragglers, but MP codes can decode below this recovery threshold depending on the set of worker nodes which fails. The decoding complexity of MP codes is shown to be lower than other approaches in the literature, due to the user not being tasked with interpolating an entire polynomial.

关键词： Data privacy data security distributed computing polynomials interpolation

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Multi-Memristor Based distributed Decision Tree Circuit for Cybersecurity Applications

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第8期71卷 3526-3537页

作者： Zhang, Lei Riem, Joseph Chen, Jingdi Mackay, Henry Lan, Tian Bastian, Nathaniel D. Adam, Gina C. George Washington Univ Elect & Comp Engn Dept Washington DC 20052 USA US Mil Acad Army Cyber Inst Dept Elect Engn & Comp Sci West Point NY 10996 USA

Cybersecurity at the edge requires fast computing in energy-constrained environments. Decision trees can provide an explainable solution for network intrusion detection with high detection accuracy at the packet level. However, their hardware implementation needs to support efficient real-time operation. In this paper, we propose a spatially distributed decision tree for network intrusion detection, using memristor-based chiplet leaves. Each chiplet processes an input by comparing it to a predefined boundary stored in the memristor cell and provides a binary output to select one of the interconnected leaves on the lower level, with an estimated power consumption in a 130nm node design of 389 $\mu$ W. The delay is 2.5 $\mu$ s for one inference decision. This chiplet approach is reconfigurable and in line with the natural architecture of decision trees. It also supports the prototyping with known good dies, overcoming the non-idealities challenge prevalent in memristor technologies. Our memristor-based decision trees show high intrusion detection accuracy of 82%, 84%, and 73% on the benchmark UNSW, CIC-IDS, and ACI-IoT datasets respectively, considering 6-bit device precision in one memristor vs. three memristor per boundary configurations. This distributed approach opens the way to utilizing memristor technology despite device defects for applications in need of local real-time computing.

关键词： Memristors Decision trees Circuits Computer architecture Network intrusion detection Resistance Real-time systems decision tree memristor multi-device distributed computing

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A Fast Learned Key-Value Store for Concurrent and distributed Systems

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第6期36卷 2301-2315页

作者： Li, Pengfei Hua, Yu Jia, Jingnan Zuo, Pengfei Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China

Efficient key-value (KV) store becomes important for concurrent and distributed systems to deliver high performance. The promising learned indexes leverage deep-learning models to complement existing KV stores and obtain significant performance improvements. However, existing schemes show limited scalability in concurrent systems due to containing high dependency among data. The practical system performance decreases when inserting a large amount of new data due to triggering frequent and inefficient retraining operations. Moreover, existing learned indexes become inefficient in distributed systems, since different machines incur high overheads to guarantee the data consistency when the index structures dynamically change. To address these problems in concurrent and distributed systems, we propose a fine-grained learned index scheme with high scalability, called FineStore, which constructs independent models with a flattened data structure under the trained data array to concurrently process the requests with low overheads. FineStore processes the new requests in-place with the support of non-blocking retraining, hence adapting to the new distributions without blocking the systems. In the distributed systems, different machines efficiently leverage the extended RCU barrier to guarantee the data consistency. We evaluate FineStore via YCSB and real-world datasets, and extensive experimental results demonstrate that FineStore improves the performance respectively by up to 1.8x and 2.5x than state-of-the-art XIndex and Masstree. We have released the open-source codes of FineStore for public use in GitHub.

关键词： Computers and information processing computer architecture data structures distributed computing

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Pattern Models: A Dynamic Epistemic Logic For distributed Systems

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COMPUTER JOURNAL 2024年第7期67卷 2421-2440页

作者： Castaneda, Armando van Ditmarsch, Hans Rosenblueth, David A. Velazquez, Diego A. Univ Nacl Autonoma Mexico Inst Matemat Ciudad Univ Mexico City 04510 Mexico Univ Toulouse Inst Rech Informat Toulouse Ctr Natl Rech Sci 118 Route Narbonne F-31062 Toulouse 9 France Univ Nacl Autonoma Mexico Inst Invest Matemat Aplicadas & Sistemas Ciudad Univ Mexico City 04510 Mexico Univ Nacl Autonoma Mexico Posgrad Ciencia & Ingn Comp Ciudad Univ Mexico City 04510 Mexico

We introduce pattern models, a dynamic epistemic logic for analyzing distributed systems. First, we present a version of pattern models where the full-information protocol, widely studied in distributed computability, is static in the product definition of pattern models. Next, we parametrize such a logic so as to add the capability to model dynamics of arbitrary deterministic protocols. We thus give a systematic construction of pattern models for a large variety of distributed-computing models called dynamic-network models. Using pattern models, the epistemic dynamics of a proper subclass of dynamic-network models called oblivious can be described using a static pattern model, hence using constant space. For this case, we present a sufficient unsolvability condition for the consensus task that can be easily verified analyzing the structure of the initial epistemic model and the pattern model for a given oblivious dynamic-network model.

关键词： dynamic epistemic logic distributed computing consensus dynamic-network models

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Exploration of distributed self-supervised training optimization strategies in visual tasks

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 2667-2675页

作者： Zhang, Xi Wang, Bo Chen, Jiangqi Wang, Jin Chen, Xia China Elect Power Res Inst Co Ltd Beijing 100192 Peoples R China State Grid Jinan Power Supply Co Jinan 250000 Shandong Peoples R China

In response to the growing demand for handling unlabeled data in visual tasks, this paper introduces a novel federated self-supervised learning model (FedSSL), which employs a federated learning framework to conduct distributed model training across multiple decentralized datasets. This model effectively harnesses the unlabeled data that is discreetly distributed among various terminals, thereby collaboratively training high-performance models. Moreover, comparative experiments conducted under standard experimental parameters and on general datasets demonstrate the model's efficacy. FedSSL not only reduces the computational complexity of the model but also enhances classification accuracy.

关键词： visual tasks distributed computing self-supervised federated learning

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distributed Finite-Time Observer for Multiple Line Outages Detection in Power Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第3期54卷 1768-1778页

作者： Chen, Yu Liu, Zhi-Wei Wen, Guanghui Wang, Yan-Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Southeast Univ Sch Math Dept Syst Sci Nanjing 210096 Peoples R China

Fast detection of power line outages is critical for maintaining the stable operation of the power system. The aim of this article is to address the real time detection problem of multiple line outages in power systems. To effectively tackle the high-computational complexity issues associated with traditional approaches, we propose a multiple line outages detection algorithm that utilizes a distributed finite-time observer. The proposed method utilizes only local measurements and information from neighboring buses to update local observer for each bus. The proposed observer is mathematically proven to converge in a finite time, ensuring rapid detection of multiple line outages. Finally, simulation results demonstrate the effectiveness and rapidity of the proposed detection algorithm.

关键词： distributed computing finite-time observer multiple line outages detection topology identification

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Toward Secure and Robust Federated Distillation in distributed Cloud: Challenges and Design Issues

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IEEE NETWORK 2024年第4期38卷 151-157页

作者： Wang, Xiaodong Guan, Zhitao Wu, Longfei Gai, Keke North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Fayetteville State Univ Dept Math & Comp Sci Fayetteville NC 28301 USA Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China

Federated learning (FL) offers a promising solution for effectively leveraging the data scattered across the distributed cloud system. Despite its potential, the huge communication overhead greatly burdens the distributed cloud system. Federated distillation (FD) is a novel distributed learning technique with low communication cost, in which the clients communicate only the model logits rather than the model parameters. However, FD faces challenges related to data heterogeneity and security. Additionally, the conventional aggregation method in FD is vulnerable to malicious uploads. In this article, we discuss the limitations of FL and the challenges of FD in the context of distributed cloud system. To address these issues, we propose a blockchain-based framework to achieve secure and robust FD. Specifically, we develop a pre-training data preparation method to reduce data distribution heterogeneity and an aggregation method to enhance the robustness of the aggregation process. Moreover, a committee/workers selection strategy is devised to optimize the task allocation among clients. Experimental evaluations are conducted to evaluate the effectiveness of the proposed framework.

关键词： Training Data models Servers Cloud computing Blockchains Computational modeling Security Federated learning distributed computing

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HERP: Hierarchical and distributed clouds oriented efficient resource scheduling algorithm with stochastic requirements

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 258卷

作者： Wei, Wei Li, Haoyi Zhang, Qinghui Henan Univ Technol KeyLab Grain Informat Proc & Control Minist Educ Zhengzhou 450001 Peoples R China Henan Univ Technol Henan Key Lab Grain Storage Informat Intelligent P Zhengzhou 450001 Peoples R China Henan Univ Technol Henan Grain Big Data Anal & Applicat Engn Res Ctr Zhengzhou 450001 Peoples R China

With the development of cloud technology, hierarchical and distributed cross-cloud architecture is gradually replacing traditional centralized architecture, for example, used in edge (or fog) computing. Due to the fluctuation of resource requirements, if a node does not have sufficient resources to process requests, the same or higher-level nodes can share their resources by offloading or redirecting requests to themselves, at the possible cost of reduced service quality. However, it is difficult to effectively optimize the sharing effect based on mean requirements. We formulate the multilevel problem with horizontal and vertical resource sharing using stochastic models, identify the optimal structures with embedded subproblems, and obtain the approximation solution in an efficient dynamic programming manner. In the problem setting with a wide range of different parameters, the proposed algorithm can outperform existing mean and heuristic algorithms in all scenarios to improve the total satisfied requirements by up to 26%, and can be hundreds of times faster than these heuristic algorithms.

关键词： Hierarchical and distributed clouds Resource scheduling Stochasticity Edge computing distributed computing

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