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A Computational Approach to Packet Classification

IEEE-ACM TRANSACTIONS ON NETWORKING 2022年第3期30卷 1073-1087页

作者： Rashelbach, Alon Rottenstreich, Ori Silberstein, Mark Technion Israel Inst Technol IL-3200003 Haifa Israel Technion Israel Inst Technol Dept Comp Sci IL-3200003 Haifa Israel Technion Israel Inst Technol Dept Elect Engn IL-3200003 Haifa Israel Technion Israel Inst Technol Dept Elect & Comp Engn IL-3200003 Haifa Israel

Multi-field packet classification is a crucial component in modern software-defined data center networks. To achieve high throughput and low latency, state-of-the-art algorithms strive to fit the rule lookup data structures into on-die caches;however, they do not scale well with the number of rules. We present a novel approach, NuevoMatch, which improves the memory scaling of existing methods. A new data structure, Range Query Recursive Model Index (RQ-RMI), is the key component that enables NuevoMatch to replace most of the accesses to main memory with model inference computations. We describe an efficient training algorithm that guarantees the correctness of the RQ-RMI-based classification. The use of RQ-RMI allows the rules to be compressed into neural networks that fit into the hardware cache. Further, it takes advantage of the growing support for fast neural network processing in modern CPUs, such as wide vector instructions, achieving a latency of tens of nanoseconds per lookup. Our evaluation using 500K multi-field rules from the standard ClassBench benchmark shows a geometric mean compression factor of 4.9x, 8x, and 82x, and average performance improvement of 2.4x, 2.6x, and 1.6x in throughput compared to CutSplit, NeuroCuts, and TupleMerge, all state-of-the-art algorithms.

关键词： Decision trees Training Throughput Neural networks Indexing partitioning algorithms IEEE transactions Packet classification machine learning

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Community Detection Based on Fish School Effect

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IEEE ACCESS 2022年 10卷 48523-48538页

作者： Sun, Yanan Sun, Zejun Chang, Xinfeng Pan, Zhongqiang Luo, Limin Pingdingshan Univ Dept Network Ctr Pingdingshan 467000 Henan Peoples R China Pingdingshan Univ Sch Informat Engn Pingdingshan 467000 Henan Peoples R China

Community structure has a more intuitive, physical meaning than how it has traditionally been perceived;this representation plays an important role in networks and is accordingly becoming widely adapted. Consequently, community detection has attracted increasing attention. Although scholars have proposed many community detection methods from different perspectives, due to the complexity, diversity and dynamic characteristics of networks, efficient community detection in many real networks remains a challenge. Inspired by fish school effect in real life, this paper envisions networks as an ecosystem and proposes a novel dynamic model that aims to reveal the communities in a more intuitive way. Relying on the new model, we design a community detection algorithm, known as community detection based on fish school effect (CDFSE). CDFSE has plentiful desirable properties: high-quality community detection, parameters free and notable scalability. To evaluate the performance of CDFSE, this paper employs two widely utilized evaluation metrics and twelve representative algorithms to test the effectiveness of the algorithm in both synthetic and real-world networks. The experimental results show that in most cases, CDFSE is superior to the comparison methods in terms of the quality of community detection.

关键词： partitioning algorithms Fish Clustering algorithms Licenses Indexes Complex networks Time complexity Fish school effect community detection cluster complex network

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Link State Estimation Under Cyber-Physical Attacks: Theory and algorithms

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IEEE TRANSACTIONS ON SMART GRID 2022年第5期13卷 3760-3773页

作者： Huang, Yudi He, Ting Chaudhuri, Nilanjan Ray La Porta, Thomas F. Penn State Univ Sch Elect Engn & Comp Sci University Pk PA 16802 USA

Effective defense against cyber-physical attacks in power grids requires accurate damage assessment. While some solutions have been proposed to recover the line states within the attacked area, existing solutions are limited by the assumption of a connected post-attack grid and the lack of verifiable performance guarantees. To fill this gap, we study the recovery of line states under a cyber-physical attack that disconnects lines while blocking information from the attacked area. In contrast to existing solutions assuming a connected post-attack grid or known post-attack power injections, we consider a more challenging scenario where the attack may partition the grid into islands, which causes unknown changes in power injections. To address this problem, under the DC model we (i) propose a linear programming-based algorithm to recover the line states within the attacked area under unknown post-attack power injections, (ii) characterize the accuracy of the proposed recovery algorithm under certain conditions, and (iii) develop efficient algorithms to verify the recovery results using observable information. Then, we extend the DC-based algorithms to AC model. Our numerical evaluations demonstrate that the proposed recovery algorithm is highly accurate in localizing failed lines, most of which can be successfully verified by the proposed verification algorithms.

关键词： Topology Cyberattack Transmission line matrix methods State estimation Phasor measurement units Smart grids partitioning algorithms Power grid state estimation cyber-physical attack failure localization verifiable condition

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Generalized Assignment for Multirobot Systems via Distributed Branch-and-Price

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IEEE TRANSACTIONS ON ROBOTICS 2022年第3期38卷 1990-2001页

作者： Testa, Andrea Notarstefano, Giuseppe Univ Bologna Dept Elect Elect & Informat Engn I-40136 Bologna Italy

In this article, we consider a network of agents that has to self-assign a set of tasks while respecting resource constraints. One possible formulation is the generalized assignment problem, where the goal is to find a maximum payoff while satisfying capability constraints. We propose a purely distributed branch-and-price algorithm to solve this problem in a cooperative fashion. Inspired by classical (centralized) branch-and-price schemes, in the proposed algorithm, each agent locally solves small linear programs, generates columns by solving simple knapsack problems, and communicates to its neighbors a fixed number of basic columns. We prove finite-time convergence of the algorithm to an optimal solution of the problem. Then, we apply the proposed scheme to a generalized assignment scenario, in which a team of robots has to serve a set of tasks. We implement the proposed algorithm in a Robot Operating System testbed and provide experiments for a team of heterogeneous robots solving the assignment problem.

关键词： Task analysis Robots Costs Robot kinematics Robot sensing systems partitioning algorithms Resource management Cooperating Robots distributed robot systems optimization and optimal control planning scheduling and coordination

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Noisy Adaptive Group Testing via Noisy Binary Search

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第5期68卷 3340-3353页

作者： Teo, Bernard Scarlett, Jonathan Natl Univ Singapore Dept Comp Sci Singapore 117418 Singapore Natl Univ Singapore Dept Math Singapore 117418 Singapore Natl Univ Singapore Inst Data Sci Singapore 117418 Singapore

The group testing problem consists of determining a small set of defective items from a larger set of items based on a number of possibly-noisy tests, and has numerous practical applications. One of the defining features of group testing is whether the tests are adaptive (i.e., a given test can be chosen based on all previous outcomes) or non-adaptive (i.e., all tests must be chosen in advance). In this paper, building on the success of binary splitting techniques in noiseless group testing (Hwang, 1972), we introduce noisy group testing algorithms that apply noisy binary search as a subroutine. We provide three variations of this approach with increasing complexity, culminating in an algorithm that succeeds using a number of tests that matches the best known previously (Scarlett, 2019), while overcoming fundamental practical limitations of the existing approach, and more precisely capturing the dependence of the number of tests on the error probability. We provide numerical experiments demonstrating that adaptive group testing strategies based on noisy binary search can be highly effective in practice, using significantly fewer tests compared to state-of-the-art non-adaptive strategies.

关键词： Testing Noise measurement Decoding Adaptation models Upper bound Search problems partitioning algorithms Group testing sparsity adaptive algorithms binary splitting noisy binary search

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Memento: Making Sliding Windows Efficient for Heavy Hitters

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IEEE-ACM TRANSACTIONS ON NETWORKING 2022年第4期30卷 1440-1453页

作者： Ben Basat, Ran Einziger, Gil Keslassy, Isaac Orda, Ariel Vargaftik, Shay Waisbard, Erez UCL Dept Comp Sci London NW1 2AE England Ben Gurion Univ Negev Dept Comp Sci IL-8410501 Beer Sheva Israel Technion Israel Inst Technol Dept Elect & Comp Engn IL-3200003 Haifa Israel Nokia Bell Labs IL-4439246 Kefar Sava Israel

Cloud operators require timely identification of Heavy Hitters (HH) and Hierarchical Heavy Hitters (HHH) for applications such as load balancing, traffic engineering, and attack mitigation. However, existing techniques are slow in detecting new heavy hitters. In this paper, we present the case for identifying heavy hitters through sliding windows. Sliding windows are quicker and more accurate to detect new heavy hitters than current interval-based methods, but to date had no practical algorithms. Accordingly, we introduce, design, and analyze the Memento family of sliding window algorithms for the HH and HHH problems in the single-device and network-wide settings. We use extensive evaluations to show that our single-device solutions are orders of magnitude faster than existing sliding window techniques and comparable in speed to state-of-the-art non-windowed sampling based technique. Furthermore, we exemplify our network-wide HHH detection capabilities on a realistic testbed. To that end, we implemented Memento as an open-source extension to the popular HAProxy cloud load-balancer. In our evaluations, using an HTTP flood by 50 subnets, our network-wide approach detected the new subnets faster and blackuced the number of undetected flood requests by up to 37x compablack to the alternatives.

关键词： Frequency estimation Load management Computer crime Windows Time measurement partitioning algorithms IEEE transactions Communication technology communication systems computer networks internet

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A Two-Stage Approach for Constructing Type-2 Information Granules

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第4期52卷 2214-2224页

作者： Zhu, Xiubin Pedrycz, Witold Li, Zhiwu Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada King Abdulaziz Univ Fac Engn Jeddah 21589 Saudi Arabia Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China

In this article, we are concerned with the formation of type-2 information granules in a two-stage approach. We present a comprehensive algorithmic framework which gives rise to information granules of a higher type (type-2, to be specific) such that the key structure of the local granular data, their topologies, and their diversities become fully reflected and quantified. In contrast to traditional collaborative clustering where local structures (information granules) are obtained by running algorithms on the local datasets and communicating findings across sites, we propose a way of characterizing granular data (formed) by forming a suite of higher type information granules to reveal an overall structure of a collection of locally available datasets. Information granules built at the lower level on a basis of local sources of data are weighted by the number of data they represent while the information granules formed at the higher level of hierarchy are more abstract and general, thus facilitating a formation of a hierarchical description of data realized at different levels of detail. The construction of information granules is completed by resorting to fuzzy clustering algorithms (more specifically, the well-known Fuzzy C-Means). In the formation of information granules, we follow the fundamental principle of granular computing, viz., the principle of justifiable granularity. Experimental studies concerning selected publicly available machine-learning datasets are reported.

关键词： Collaboration Clustering algorithms Prototypes partitioning algorithms Clustering methods Cybernetics Topology Collaborative clustering granular computing (GrC) principle of justifiable granularity type-2 information granules

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A Framework of Mahalanobis-Distance Metric With Supervised Learning for Clustering Multipath Components in MIMO Channel Analysis

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2022年第6期70卷 4069-4081页

作者： Chen, Yi Han, Chong He, Jia Wang, Guangjian Shanghai Jiao Tong Univ Terahertz Wireless Commun TWC Lab Shanghai 200240 Peoples R China Huawei Technol Co Ltd Shenzhen 518129 Peoples R China

As multipath components (MPCs) are experimentally observed to appear in clusters, cluster-based channel models have been focused in the wireless channel study. However, most of the MPC clustering algorithms for multi-in-multiout (MIMO) channels with delay and angle information of MPCs are based on the distance metric that quantifies the similarity of two MPCs and determines the preferred cluster shape, greatly impacting MPC clustering quality. In this article, a general framework of Mahalanobis-distance metric is proposed for MPC clustering in the MIMO channel analysis, without user-specified parameters. Remarkably, the popular multipath component distance (MCD) is proven to be a special case of the proposed distance metric framework. Furthermore, two machine learning algorithms, namely, weak-supervised Mahalanobis metric for clustering and supervised large margin nearest neighbor, are introduced to learn the distance metric. To evaluate the effectiveness, a modified channel model is proposed based on the Third Generation Partnership Project (3GPP) spatial channel model (SCM) to generate clustered MPCs with delay and angular information since the original 3GPP SCM is incapable to evaluate clustering quality. Experiment results show that the proposed distance metric can significantly improve the clustering quality of existing clustering algorithms, while the learning phase requires considerably limited efforts of labeling MPCs.

关键词： Measurement Clustering algorithms Delays MIMO communication Channel models partitioning algorithms Machine learning algorithms Machine learning multi-in-multiout (MIMO) channel modeling multipath component (MPC) clustering

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Subarray Partition algorithms for RIS-Aided MIMO Communications

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IEEE INTERNET OF THINGS JOURNAL 2022年第17期9卷 16196-16208页

作者： Dai, Hui Shen, Wenqian Ding, Li Gong, Shiqi An, Jianping Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China

In order to reduce computational complexity and hardware cost for reconfigurable intelligent surface (RIS)-aided multiple-input-multiple-output (MIMO) systems, in this article, the subarray partition algorithm designs at RIS are investigated. Without instantaneous channel state information (CSI) of the RIS-related links, the subarray partition algorithms aim at minimizing the number of subarrays while keeping a minimum sum rate requirement. In nature, the subarray partition optimization problem is a combinatorial optimization and NP-hard because of many discrete optimization variables. Three kinds of subarray partition algorithms are proposed. The first one is named as a fixed pattern subarray partition algorithm, in which subarray is arranged in a predefined manner. This algorithm is easy to implement but its performance is far from optimal. To reap the benefits of RIS as much as possible, two dynamic pattern subarray partition algorithms are given as well. The first dynamic pattern algorithm is the greedy dynamic pattern subarray partition algorithm that is more complicated than the fixed pattern one but benefits much better performance. To reduce complexity, the relaxation-based dynamic pattern algorithm is given, which has almost the same performance as the greedy dynamic algorithm but has a much lower complexity. At the end of the whole work, numerical results are given to access the performance of the proposed algorithms.

关键词： partitioning algorithms Heuristic algorithms Optimization MIMO communication Complexity theory Channel estimation Phased arrays Multiple-input-multiple-output (MIMO) reflection coefficient optimization reconfigurable intelligent surface (RIS) subarray partition

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Edge Repartitioning via Structure-Aware Group Migration

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2022年第3期9卷 751-760页

作者： Li, He Yuan, Hang Huang, Jianbin Ma, Xiaoke Cui, Jiangtao Yoo, Jaesoo Xidian Univ Sch Comp Sci & Technol Xian 710126 Peoples R China Chungbuk Natl Univ Dept Informat & Commun Engn Cheongju 361763 South Korea

Graph partitioning is a mandatory step in distributed graph computing systems. Some existing systems use edge partitioning methods to partition static graphs. However, the structure of the real-world graphs changes dynamically, which leads to unnecessary vertex replicas and load imbalance, reducing the performance of graph computation. In this article, we focus on improving the lower partitioning quality caused by the dynamics of the graph structure. We propose an edge repartitioning algorithm via structure-aware group migration (SAGM-ER). We define a special structure edge group (EG) consisting of multiple edges, which can reduce vertex replicas by migrating to other partitions. In repartitioning, we search for EGs in parallel by a method based on a structure-aware priority and then migrate EGs to reduce vertex replicas. Compared to the state of the art, SAGM-ER can reduce more vertex replicas. We implement SAGM-ER on Powergraph, which reduces the redundant replicas by 63.33%, thus reducing executing time and communication costs in graph computation by 33.72% and 37.51%, respectively.

关键词： partitioning algorithms Heuristic algorithms Load management Resource management Spread spectrum communication Social networking (online) Real-time systems Dynamic graph edge group (EG) edge repartitioning graph computation structure-aware priority

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