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Parallel Path Progression DAG Scheduling

IEEE TRANSACTIONS ON COMPUTERS 2023年第10期72卷 3002-3016页

作者： Ueter, Niklas Guenzel, Mario Von der Brueggen, Georg Chen, Jian-Jia TU Dortmund Univ Design Automat Embedded Syst Grp D-44227 Dortmund Germany

Increasing performance needs of modern cyber-physical systems leads to multiprocessor architectures being increasingly utilized. To efficiently exploit their potential parallelism in hard real-time systems, appropriate task models and scheduling algorithms that allow to provide timing guarantees are required. Such scheduling algorithms and the corresponding worst-case response time analyses usually suffer from resource over-provisioning due to pessimistic analyses based on worst-case assumptions. Hence, scheduling algorithms and analyses with high resource efficiency are required. A prominent fine-grained parallel task model is the directed-acyclic-graph (DAG) task model that is composed of precedence constrained subjobs. This paper studies the hierarchical real-time scheduling problem of sporadic arbitrary-deadline DAG tasks. We propose a parallel path progression scheduling property that is implemented with only two distinct subtask priorities, which allows to quantify the parallel execution of a user chosen collection of complete paths in the response time analysis. This novel approach significantly improves the state-of-the-art response time analyses for parallel DAG tasks for highly parallel DAG structures and can provably exhaust large core numbers. Two hierarchical scheduling algorithms are designed based on this property, extending the parallel path progression properties and improve the response time analysis for sporadic arbitrary-deadline DAG task sets.

关键词： Real-time DAG scheduling homogeneous multicore platforms hierarchical scheduling approximation algorithms

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Optimizing Resource Allocation With High-Reliability Constraint for Multicasting Automotive Messages in 5G NR C-V2X Networks

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IEEE Transactions on Vehicular Technology 2023年第4期72卷 4792-4804页

作者： Chen, Kuan-Lin Chen, Wei-Yu Hwang, Ren-Hung National Chung Cheng University Department of Computer Science and Informational Engineering Chiayi621 Taiwan Taipei115 Taiwan National Yang Ming Chiao Tung University College of Artificial Intelligence Tainan711 Taiwan

Cellular vehicle-to-everything (C-V2X) has been continuously evolving since Release 14 of the 3rd Generation Partnership Project (3GPP) for future autonomous vehicles. Apart from automotive safety, 5G NR further bring new capabilities to C-V2X for autonomous driving, such as real-time local update, and coordinated driving. These capabilities rely on the provision of low latency and high reliability from 5G NR. Among them, a basic demand is broadcasting or multicasting environment update messages, such as cooperative perception data, with high reliability and low latency from a Road Side Unit (RSU) or a base station (BS). In other words, broadcasting multiple types of automotive messages with high reliability and low latency is one of the key issues in 5G NR C-V2X. In this work, we consider how to select Modulation and Coding Scheme (MCS), RSU/BS, Forward Error Correction (FEC) code rate, to maximize the system utility, which is a function of message delivery reliability. We formulate the optimization problem as a nonlinear integer programming problem. Since the optimization problem is NP-hard, we propose an approximation algorithm, referred to as the Hyperbolic Successive Convex approximation (HSCA) algorithm, which uses the successive convex approximation to find the optimal solution. In our simulations, we compare the performance of HSCA with those of three algorithms respectively, including the baseline algorithm, the heuristic algorithm, and the optimal solution. Our simulation results show that HSCA outperforms the baseline and the heuristic algorithms and is very competitive to the optimal solution. © 1967-2012 IEEE.

关键词： approximation algorithms

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Sequential Diversification with Provable Guarantees 25

Sequential Diversification with Provable Guarantees

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18th International Conference on Web Search and Data Mining-WSDM

作者： Wang, Honglian Tu, Sijing Gionis, Aristides KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9798400713293

Diversification is a useful tool for exploring large collections of information items. It has been used to reduce redundancy and cover multiple perspectives in information-search settings. Diversification finds applications in many different domains, including presenting search results of information-retrieval systems and selecting suggestions for recommender systems. Interestingly, existing measures of diversity are defined over sets of items, rather than evaluating sequences of items. This design choice comes in contrast with commonly-used relevance measures, which are distinctly defined over sequences of items, taking into account the ranking of items. The importance of employing sequential measures is that information items are almost always presented in a sequential manner, and during their information-exploration activity users tend to prioritize items with higher ranking. In this paper, we study the problem of maximizing sequential diversity. This is a new measure of diversity, which accounts for the ranking of the items, and incorporates item relevance and user behavior. The overarching framework can be instantiated with different diversity measures, and here we consider the measures of sum diversity and coverage diversity. The problem was recently proposed by Coppolillo et al. [11], where they introduce empirical methods that work well in practice. Our paper is a theoretical treatment of the problem: we establish the problem hardness and present algorithms with constant approximation guarantees for both diversity measures we consider. Experimentally, we demonstrate that our methods are competitive against strong baselines.

关键词： Diversification Ranking algorithms approximation algorithms

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Byzantine-Resilient Decentralized Policy Evaluation With Linear Function approximation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 3839-3853页

作者： Wu, Zhaoxian Shen, Han Chen, Tianyi Ling, Qing Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou 510006 Guangdong Peoples R China Pazhou Lab Guangzhou 510300 Guangdong Peoples R China Rensselaer Polytech Inst Dept Elect Comp & Syst Engn Troy NY 12180 USA

In this paper, we consider the policy evaluation problem in reinforcement learning with agents on a decentralized and directed network. In order to evaluate the quality of a fixed policy in this decentralized setting, one option is for agents to run decentralized temporal-difference (TD) collaboratively. To account for the practical scenarios where the state and action spaces are large and malicious attacks emerge, we focus on the decentralized TD learning with linear function approximation in the presence of malicious agents (often termed as Byzantine agents). We propose a trimmed mean-based Byzantine-resilient decentralized TD algorithm to perform policy evaluation in this setting. We establish the finite-time convergence rate, as well as the asymptotic learning error that depends on the number of Byzantine agents. Numerical experiments corroborate the robustness of the proposed algorithm.

关键词： Signal processing algorithms Convergence Function approximation Optimization approximation algorithms Task analysis Mathematical model Policy evaluation multi-agent reinforcement learning temporal-difference learning Byzantine attacks

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Approximability of Edge-Vertex Domination in Unit Disk Graphs 2nd

Approximability of Edge-Vertex Domination in Unit Disk Gra...

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2nd International Conference on Applied algorithms, ICAA 2025

作者： Singireddy, Vishwanath R. Basappa, Manjanna Geethanjali College of Engineering and Technology Cheeryala Keesara Hyderabad501301 India National Institute of Technology Karnataka Surathkal Mangaluru575025 India

ISBN: (纸本)9783031845420

Given an undirected graph G=(V,E), a vertex v∈V is edge-vertex (ev) dominated by an edge e∈E if v is either incident to e or incident to an adjacent edge of e. A set Sev⊆E is an edge-vertex dominating set (referred to as ev-dominating set and in short as EVDS) of G if every vertex of G is ev-dominated by at least one edge of Sev. The minimum cardinality of an ev-dominating set is the ev-domination number. The edge-vertex dominating set problem is to find a minimum ev-domination number. The EVDS problem finds applications in handling security issues in the communication network by identifying a minimum number of critical edges to implement additional security measures to safeguard key components of the network. In this paper, we prove that this problem admits a polynomial-time approximation scheme in unit disk graphs. We also give a simple 5-factor linear-time approximation algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： approximation algorithms

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Parity-Constrained k-Supplier Problem 18th

Parity-Constrained k-Supplier Problem

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18th International Joint Conference on Theoretical Computer Science-Frontier of Algorithmic Wisdom, IJTCS-FAW 2024

作者： Xia, Xinlan Han, Lu Mei, Lili School of Science Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9789819777518

In this paper, we propose and study the parity-constrained k-supplier (PAR k-supplier) problem, generalizing the classical (unconstrained) k-supplier problem. In the PAR k-supplier problem, we are given a set of facilities and a set of clients in a metric space with distances. An integer k is also given. Each facility is associated with an odd or even parity requirement. The goal is to open at most k facilities and assign each client to an opened facility, such that the number of clients assigned to a facility meets its parity requirement, and the maximum distance of any client to its assigned facility is minimized. As our main contribution, we provide a constant-factor approximation algorithm for the PAR k-supplier problem with a ratio of 9. Our algorithm proceeds by first ignoring all parity requirements and constructing an (unconstrained) k-supplier instance. Next, we design an algorithm to solve the constructed instance and obtain a solution that may have some invalid facilities. An invalid facility is an opened facility whose parity requirement is violated. Last, to obtain a feasible solution, we match up all the invalid facilities and make reassignments according to each invalid pair. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： approximation algorithms

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Efficient approximation of Two-Terminal Networks Reliability Polynomials Using Cubic Splines

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IEEE TRANSACTIONS ON RELIABILITY 2021年第3期70卷 1193-1203页

作者： Cristescu, Gabriela Dragoi, Vlad-Florin Aurel Vlaicu Univ Arad Dept Math & Comp Sci Arad 310032 Romania

In this article, two new techniques of approximation of the reliability of a two-terminal network are developed based on the constructive theory of functions and related methods. Two methods of generating an approximation cubic spline are used: Lagrange-type interpolation procedures and Bernstein approximation operator. A possibility of minimizing the total error of approximation, based on keeping some properties invariant, is described in case of a large class of pairs of dual two-terminal networks. Simulations are included, showing that the error of approximation is negligible in case of some special initial data.

关键词： Reliability Computer network reliability Telecommunication network reliability Splines (mathematics) Reliability theory approximation algorithms Integrated circuit reliability Bernstein polynomial cubic spline approximation interpolation reliability polynomial two-terminal network (2TN)

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Joint Embedding Learning and Low-Rank approximation: A Framework for Incomplete Multiview Learning

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第3期51卷 1690-1703页

作者： Tao, Hong Hou, Chenping Yi, Dongyun Zhu, Jubo Hu, Dewen Natl Univ Def Technol Coll Liberal Arts & Sci Changsha 410073 Peoples R China Natl Univ Def Technol Coll Mechatron & Automat Changsha 410073 Peoples R China

In real-world applications, not all instances in the multiview data are fully represented. To deal with incomplete data, incomplete multiview learning (IML) rises. In this article, we propose the joint embedding learning and low-rank approximation (JELLA) framework for IML. The JELLA framework approximates the incomplete data by a set of low-rank matrices and learns a full and common embedding by linear transformation. Several existing IML methods can be unified as special cases of the framework. More interestingly, some linear transformation-based complete multiview methods can be adapted to IML directly with the guidance of the framework. Thus, the JELLA framework improves the efficiency of processing incomplete multiview data, and bridges the gap between complete multiview learning and IML. Moreover, the JELLA framework can provide guidance for developing new algorithms. For illustration, within the framework, we propose the IML with the block-diagonal representation (IML-BDR) method. Assuming that the sampled examples have an approximate linear subspace structure, IML-BDR uses the block-diagonal structure prior to learning the full embedding, which would lead to more correct clustering. A convergent alternating iterative algorithm with the successive over-relaxation optimization technique is devised for optimization. The experimental results on various datasets demonstrate the effectiveness of IML-BDR.

关键词： Clustering algorithms Optimization Bridges Cameras Cybernetics approximation algorithms Iterative methods Block-diagonal representation (BDR) embedding learning incomplete multiview learning (IML) low-rank approximation

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POLYNOMIAL TIME approximation SCHEMES FOR THE TRAVELING REPAIRMAN AND OTHER MINIMUM LATENCY PROBLEMS

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SIAM JOURNAL ON COMPUTING 2021年第5期50卷 1580-1602页

作者： Sitters, Rene Vrije Univ Dept Econometr & Operat Res NL-1081 HV Amsterdam Netherlands Ctr Wiskunde & Informat CWI NL-1090 GB Amsterdam Netherlands

We give a polynomial time approximation scheme for the weighted traveling repairman problem (TRP) in the Euclidean plane, on trees, and on planar graphs. This improves upon the quasi-polynomial time approximation schemes for the unweighted TRP in the Euclidean plane and trees and on the 3.59-approximation for planar graphs. The algorithms are based on a new decomposition technique that reduces the approximation of weighted TRP to instances for which we may restrict ourselves to solutions that are the concatenation of only a constant number of traveling salesman problem paths. A similar reduction applies to many other problems with an average completion time objective. To illustrate the strength of this approach, we apply the same technique to the well-studied scheduling problem of minimizing total weighted completion time under precedence constraints, 1 vertical bar prec vertical bar Sigma w(j)C(j), and present a polynomial time approximation scheme for the case of interval order precedence constraints. This improves on the known 3/2-approximation for this problem.

关键词： minimum latency tour traveling repairman approximation algorithms interval orders total completion time

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Dual-Objective Personalized Federated Service System With Partially-Labeled Data Over Wireless Networks

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IEEE Transactions on Services Computing 2023年第5期16卷 3265-3279页

作者： Ching, Cheng-Wei Chang, Jia-Ming Kuo, Jian-Jhih Wang, Chih-Yu Ucsc Department of Computer Science and Engineering Santa CruzCA95064 United States Academia Sinica Research Center for Information Technology Innovation Taipei City11529 Taiwan National Chung Cheng University Department of Computer Science and Information Engineering Chiayi62102 Taiwan Advanced Institute of Manufacturing with Hightech Innovations National Chung Cheng University Chiayi62102 Taiwan

Federated learning (FL) emerges to mitigate the privacy concerns in machine learning-based services and applications, and personalized federated learning (PFL) evolves to alleviate the issue of data heterogeneity. However, FL and PFL usually rest on two assumptions: the users' data is well-labeled, or the personalized goals align with sufficient local data. Unfortunately, the two assumptions may not hold in most cases, where data labeling is costly, or most users have no sufficient local data to satisfy their personalized needs. To this end, we first formulate the problem, DoLP, that studies the issue of insufficient and partially-labeled data on FL-based services. DoLP aims to maximize two service objectives: 1) personalized classification objective and 2) the personalized labeling objective for each user within the constraint of training time over wireless networks. Then, we propose a PFL-based service system DoFed-SPP to solve DoLP. The DoFed-SPP's novelty is two-fold. First, we devise an inference-based first-order approximation metric, similarity ratio, to identify the similarity between users' local data. Second, we design an approximation algorithm to determine the appropriate size and set of users for uploading in each round. Extensive experiments show DoFed-SPP outperforms the state-of-the-art in final accuracy and time-to-accuracy performance on CIFAR10/100 and DBPedia. © 2008-2012 IEEE.

关键词： approximation algorithms

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