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Mobility-Aware Utility Maximization in Digital Twin-Enabled Serverless Edge Computing

IEEE TRANSACTIONS ON COMPUTERS 2024年第7期73卷 1837-1851页

作者： Li, Jing Guo, Song Liang, Weifa Wang, Jianping Chen, Quan Xu, Wenchao Wei, Kang Jia, Xiaohua City Univ Hong Kong Dept Comp Sci Hong Kong 999077 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong 999077 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong 999077 Peoples R China Guangdong Univ Technol Sch Comp Guangzhou 510006 Peoples R China

Driven by data and models, the digital twin technique presents a new concept of optimizing system design, process monitoring, decision-making and more, through performing comprehensive virtual-reality interaction and continuous mapping. By introducing serverless computing to Mobile Edge Computing (MEC) environments, the emerging serverless edge computing paradigm facilitates the communication-efficient digital twin services, and promises agile, fine-grained and cost-efficient provisioning of limited edge resources, where serverless functions are implemented by containers in cloudlets (edge servers). However, the nonnegligible cold start delay of containers deteriorates the responsiveness of digital twin services dramatically and the perceived user service experience. In this paper, we investigate delay-sensitive query service provisioning in digital twin-empowered serverless edge computing by considering user mobility. With digital twins of users deployed in the remote cloud, referred to as primary digital twins, we deploy their digital twin replicas based on serverless functions in cloudlets to mitigate the query service delay while enhancing user service satisfaction that is expressed as a utility function. We study two optimization problems with the aim of maximizing the accumulative utility gain: the digital twin replica placement problem per time slot, and the dynamic digital twin replica placement problem over a finite time horizon. We first formulate an Integer Linear Program (ILP) solution for the digital twin replica placement problem when the problem size is small;otherwise, we propose an approximation algorithm for the problem with a provable approximation ratio. We then design an online algorithm for the dynamic digital twin replica placement problem, and a performance-guaranteed online algorithm for a special case of the problem by assuming each user issues a query at each time slot. Finally, we evaluate the performance of the proposed algorithms for pl

关键词： Digital twins Heuristic algorithms approximation algorithms Delays Edge computing Containers Computers Digital twin serverless computing mobile edge computing user mobility approximation algorithm online algorithm user service satisfaction delay-sensitive service utility maximization resource allocation and optimization

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Piecewise-Constant Representation and Sampling of Bandlimited Signals on Graphs

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2024年 10卷 332-346页

作者： Yang, Guangrui Zhang, Qing Yang, Lihua South China Agr Univ Coll Math & Informat Dept Math Guangzhou 510642 Peoples R China Jiangsu Univ Sch Math Sci Zhenjiang 212013 Peoples R China Sun Yat Sen Univ Sch Math Guangzhou 510275 Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou 510275 Peoples R China

Signal representations on graphs are at the heart of most graph signal processing techniques, allowing for targeted signal models for tasks such as denoising, compression, sampling, reconstruction and detection. This paper studies the piecewise-constant representation of bandlimited graph signals, thereby establishing the relationship between the bandlimited graph signal and the piecewise-constant graph signal. For this purpose, we first introduce the concept of epsilon-level piecewise-constant representation for a general signal space. Then, using a distance matrix, a single-layer piecewise-constant representation algorithm is proposed to find an epsilon-level piecewise-constant representation for bandlimited graph signals. On this basis, we further propose a multi-layer piecewise-constant representation algorithm, which can find a node partition with as few pieces as possible to represent bandlimited graph signals piecewise within a preset error bound. Finally, as an application, we apply the node partition obtained by the multi-layer algorithm to establish a sampling theory for bandlimited signals, which does not need to compute the eigendecomposition of a variation operator in both sampling and signal reconstruction. Numerical experiments show that the proposed algorithms have good performance.

关键词： Partitioning algorithms Vectors Signal processing Information processing Signal processing algorithms approximation algorithms Laplace equations Bandlimited graph signal graph signal processing piecewise-constant graph signal piecewise-constant representation sampling

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Budget-Constrained Ego Network Extraction With Maximized Willingness

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第12期36卷 7692-7707页

作者： Hsu, Bay-Yuan Lu, Chia-Hsun Chang, Ming-Yi Tseng, Chih-Ying Shen, Chih-Ya Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu Taiwan Natl Tsing Hua Univ Dept Comp Sci Hsinchu Taiwan Fu Jen Catholic Univ Dept Sociol New Taipei City Taiwan

Many large-scale machine learning approaches and graph algorithms are proposed recently to address a variety of problems in online social networks (OSNs). To evaluate and validate these algorithms and models, the data of ego-centric networks (ego networks) are widely adopted. Therefore, effectively extracting large-scale ego networks from OSNs becomes an important issue, particularly when privacy policies become increasingly strict nowadays. In this paper, we study the problem of extracting ego network data by considering jointly the user willingness, crawling cost, and structure of the network. We formulate a new research problem, named Structure and Willingness Aware Ego Network Extraction (SWAN) and analyze its NP-hardness. We first propose a (1-1/e) -approximation algorithm, named Tristar-Optimized Ego Network Identification with Maximum Willingness (TOMW) . In addition to the deterministic approximation algorithm, we also propose to automatically learn an effective heuristic approach with machine learning, to avoid the huge efforts for human to devise a good algorithm. The learning approach is named Willingness-maximized and Structure-aware Ego Network Extraction with Reinforcement Learning (WSRL) , in which we propose a novel constrastive learning strategy, named Contrastive Learning with Performance-boosting Graph Augmentation . We recruited 1,810 real-world participants and conducted an evaluation study to validate our problem formulation and proposed approaches. Moreover, experimental results on real social network datasets show that the proposed approaches outperform the other baselines significantly.

关键词： Costs Machine learning algorithms Data mining approximation algorithms Social networking (online) Recruitment Data models Willingness approximation algorithm crawling ego networks online social networks

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Heterogeneous Hypernetwork Representation Learning With Hyperedge Fusion

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第6期11卷 7646-7657页

作者： Wang, Keke Zhu, Yu Wang, Xiaoying Huang, Jianqiang Cao, Tengfei Qinghai Univ Dept Comp Technol & Applicat Xining 810000 Peoples R China

Most of the existing hypernetwork representation learning methods fail to fully consider the hyperedges, leading to the untapped potential of information contained within the hyperedges. To address this issue, this article proposes a heterogeneous hypernetwork representation learning method with hyperedge fusion abbreviated as HRHF. First, this method incorporates the hyperedges into random walk node sequences by means of incidence graph to enhance tuple relationships, i.e., the hyperedges among the nodes. Second, under the condition of the above random walk node sequences, the cognitive structure model, cognitive set model, and cognitive hyperedge model are jointly optimized to comprehensively consider pairwise relationships and tuple relationships among the nodes to learn high-quality node representation vectors. The experimental results demonstrate that, for the link prediction task, this method outperforms other optimal baseline methods, i.e., hyper-path- based random walks + hyper-gram (HPHG) by 0.99% points on the drug dataset, is comparable to the other optimal method, i.e., Event2vec on the global positioning system (GPS) dataset, is close to the performance of other optimal methods, i.e., hyper-path-based random walks + skip-gram (HPSG) on the MovieLens, and is close to the performance of other optimal methods, i.e., Hyper2vec on the WordNet dataset. For the hypernetwork reconstruction task, this method achieves superior average performance on the drug and GPS datasets compared with other baseline methods.

关键词： Vectors Representation learning Linear programming Task analysis approximation algorithms Convolution Marine vehicles Hyperedge hypernetwork reconstruction hypernetwork representation link prediction random walk

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A (5/3+Ε)-approximation for Tricolored Non-Crossing Euclidean TSP 32

A (5/3+Ε)-Approximation for Tricolored Non-Crossing Euclide...

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32nd Annual European Symposium on algorithms, ESA 2024

作者： Baligács, Júlia Disser, Yann Feldmann, Andreas Emil Zych-Pawlewicz, Anna Technische Universität Darmstadt Germany University of Sheffield United Kingdom University of Warsaw Poland

ISBN: (纸本)9783959773386

In the Tricolored Euclidean Traveling Salesperson problem, we are given k = 3 sets of points in the plane and are looking for disjoint tours, each covering one of the sets. Arora (1998) famously gave a PTAS based on "patching" for the case k = 1 and, recently, Dross et al. (2023) generalized this result to k = 2. Our contribution is a (5/3 + Ε)-approximation algorithm for k = 3 that further generalizes Arora's approach. It is believed that patching is generally no longer possible for more than two tours. We circumvent this issue by either applying a conditional patching scheme for three tours or using an alternative approach based on a weighted solution for k = 2. © Júlia Baligács, Yann Disser, Andreas Emil Feldmann, and Anna Zych-Pawlewicz;licensed under Creative Commons License CC-BY 4.0.

关键词： approximation algorithms

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Network Shortest Path Interdiction Problem Based on Generalized Set Coverage

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第2期11卷 2191-2203页

作者： Ma, Chuang Zuo, Tao Zhang, Hai-Feng Anhui Univ Sch Internet Hefei 230601 Peoples R China Anhui Univ Sch Math Sci Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China

In the network shortest path interdiction problem, an evader attempts to find the shortest path between the origin and the destination in a network, while an interdictor attempts to maximize the length of this shortest path by interdicting network arcs with limited resources. Therefore, the problem can be formulated as a bi-level programming problem. Existing methods for solving this problem have either low accuracy or slow convergence speed. To address this, in this article, we propose a new algorithm to overcome the above challenges by transforming the problem into an iterative generalized set coverage problem and then solving it by using zero-one linear programming. At each step of the iteration, we obtain a better solution than at the previous step by setting a fixed parameter to interdict a dynamic set regarding the possible interdiction paths. We rigorously prove that the iterative algorithm can converge to the optimal solution. Additionally, the convergence speed is significantly faster than those of baseline methods. For the fixed parameter setting problem, we also propose a parameter adaptive algorithm to further accelerate the convergence speed. Finally, the excellent performance of the proposed algorithms is verified in randomly generated networks and real networks.

关键词： Programming Signal processing algorithms approximation algorithms Wireless sensor networks Optimization Sensors Linear programming Generalized set coverage network shortest path interdiction problem parameter adaptive algorithm zero-one linear programming

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A Proximal Newton Adaptive Importance Sampler

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1545-1549页

作者： Elvira, Victor Chouzenoux, Emilie Akyildiz, O. Deniz Univ Edinburgh Sch Math Edinburgh EH8 9YL Scotland Univ Paris Saclay Inria Saclay OPIS F-91190 Saclay France Imperial Coll London Dept Math London SW7 2AZ England

Adaptive importance sampling (AIS) algorithms are a rising methodology in signal processing, statistics, and machine learning. An effective adaptation of the proposals is key for the success of AIS. Recent works have shown that gradient information about the involved target density can greatly boost performance, but its applicability is restricted to differentiable targets. In this letter, we propose a proximal Newton adaptive importance sampler for the estimation of expectations with respect to non-smooth target distributions. We implement a scaled Newton proximal gradient method to adapt the proposal distributions, enabling efficient and optimized moves even when the target distribution lacks differentiability. We show the good performance of the algorithm in two scenarios: one with convex constraints and another with non-smooth sparse priors.

关键词： Proposals Monte Carlo methods Signal processing algorithms Artificial intelligence Bayes methods Convergence Signal processing Inference algorithms Gradient methods approximation algorithms Adaptive importance sampling proximal methods Newton algorithm preconditioning

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An LMI-Based Robust Nonlinear Adaptive Observer for Disturbed Regression Models

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第6期69卷 4035-4041页

作者： Rios, Hector de Loza, Alejandra Ferreira Efimov, Denis Franco, Roberto Tecnol Nacl Mexico IT La Laguna Div Estudios Posgrad & Invest Torreon 27000 Mexico CONAHCYT Investigadoras & Investigadores Mexico Ciudad De Mexico 03940 Mexico Inst Politecn Nacl CITEDI Tijuana 22435 Mexico Univ Lille Inria CNRS F-59000 Lille France Informat Technol Mech & Opt Univ Dept Control Syst & Informat St Petersburg 197101 Russia

This article deals with the problem of time-varying parameter identification in dynamical regression models affected by disturbances. The disturbances comprise time-dependent external perturbations and nonlinear unmodeled dynamics. With this aim in mind, we propose a robust nonlinear adaptive observer. The algorithm ensures the asymptotic convergence of the parameter identification error to an acceptably small region around the origin in the presence of disturbances. The synthesis of the adaptive observer is given in terms of linear matrix inequalities, providing a constructive design method. An academic example and a low inertia power system illustrate the robustness and the applicability of the proposed adaptive observer for the time-varying parameter identification problem.

关键词： Heuristic algorithms Perturbation methods Observers Convergence Adaptation models Linear matrix inequalities approximation algorithms Adaptive control parameter identification regression models

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CLIPPER+: A Fast Maximal Clique Algorithm for Robust Global Registration

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第4期9卷 3562-3569页

作者： Fathian, Kaveh Summers, Tyler Colorado Sch Mines Dept Comp Sci Golden CO 80401 USA Univ Texas Dallas Dept Mech Engn Richardson TX 75080 USA

We present CLIPPER+, an algorithm for finding maximal cliques in unweighted graphs for outlier-robust global registration. The registration problem can be formulated as a graph and solved by finding its maximum clique. This formulation leads to extreme robustness to outliers;however, finding the maximum clique is an NP-hard problem, and therefore approximation is required in practice for large-size problems. The performance of an approximation algorithm is evaluated by its computational complexity (the lower the runtime, the better) and solution accuracy (how close the solution is to the maximum clique). Accordingly, the main contribution of CLIPPER+ is outperforming the state-of-the-art in accuracy while maintaining a relatively low runtime. CLIPPER+ builds on prior work (CLIPPER Lusk et al. (2021) and PMC Rossi et al. (2015)) and prunes the graph by removing vertices that have a small core number and cannot be a part of the maximum clique. This will result in a smaller graph, on which the maximum clique can be estimated considerably faster. We evaluate the performance of CLIPPER+ on standard graph benchmarks, as well as synthetic and real-world point cloud registration problems. These evaluations demonstrate that CLIPPER+ has the highest accuracy and can register point clouds in scenarios where over 99% of associations are outliers.

关键词： Point cloud compression Runtime approximation algorithms Three-dimensional displays Simultaneous localization and mapping Robustness Registers Localization SLAM RGB-D perception

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Minimax approximation of Sign Function by Composite Polynomial for Homomorphic Comparison

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2022年第6期19卷 3711-3727页

作者： Lee, Eunsang Lee, Joon-Woo No, Jong-Seon Kim, Young-Sik Seoul Natl Univ Dept Elect & Comp Engn INMC Seoul 08826 South Korea Chosun Univ Dept Informat & Commun Engn Gwangju 61452 South Korea

The comparison operation for two numbers is one of the most frequently used operations in several applications, including deep learning. As such, lots of research has been conducted with the goal of efficiently evaluating the comparison operation in homomorphic encryption schemes. Recently, Cheon et al. (Asiacrypt 2020) proposed new comparison methods that approximated the sign function on homomorphically encrypted data using composite polynomials and proved that these methods had optimal asymptotic complexity. In this article, we propose a practically optimal method that approximates the sign function using compositions of minimax approximation polynomials. We prove that this approximation method is optimal with respect to depth consumption and the number of non-scalar multiplications. In addition, we propose a polynomial-time algorithm that determines the optimal composition of minimax approximation polynomials for the proposed homomorphic comparison operation using dynamic programming. The numerical analysis demonstrates that when minimizing runtime, the proposed comparison operation reduces the runtime by approximately 45 percent on average when compared to the previous algorithm. Likewise, when minimizing depth consumption, the proposed algorithm reduces the runtime by approximately 41 percent on average. In addition, when high precision in the comparison operation is required, the previous algorithm does not achieve 128-bit security, while the proposed algorithm does due to its small depth consumption.

关键词： approximation algorithms Runtime Encryption Sorting Deep learning Heuristic algorithms Upper bound Cheon-Kim-Kim-Song (CKKS) scheme fully homomorphic encryption homomorphic comparison operation minimax approximation polynomial Remez algorithm sign function

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