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A Multi-Market Trading Framework for Low-Latency Service Provision at the Edge of Networks

IEEE TRANSACTIONS ON SERVICES COMPUTING 2023年第1期16卷 27-39页

作者： Shih, Yuan-Yao Pang, Ai-Chun He, Tian Chiu, Te-Chuan Natl Chung Cheng Univ Dept Commun Engn Chiayi 62102 Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei 10617 Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 10617 Taiwan Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA Acad Sin Res Ctr Informat Technol Innovat CITI Taipei 115024 Taiwan

Along with the Ultra-Reliable and Low-Latency Communications (URLLC) of 5G, edge computing enables many potential low-latency IoT applications and has recently gained widespread attention. Addressing edge computing's economic issues is critical as we need to motivate edge devices as resource providers to devote their computing resources to the service. There are multiple users and resource providers in most edge computing service scenarios. The communication delays between users and providers are not the same since their physical distances are different. However, most existing works on edge computing regarding network economics adopt single market models that do not consider the influence of communication delay. Some researchers advocate using multi-market models to overcome this issue by grouping users who connect to the same access point or base station and letting each host its auction for resource trading. However, due to the high cost of deploying 5G ultra-dense small cells, barely a network operator can reach full network coverage. Users cannot rely on a single network, but these models do not allow resource trading between different groups. Thus, this article proposes a novel multi-market trading (MMT) framework to address these shortcomings. The framework combines the double auction at each group and a market selection game to enable the resource providers to participate in multiple auctions and analyze their choice of markets. We present the framework's detailed design and propose a theory-based learning algorithm to approximate each provider's optimal strategies. Through extensive simulations using real-world datasets of vehicular networks, we show that the proposed framework can improve the social welfare by $14.45\%$14.45% and $36.74\%$36.74%, respectively, compared with classic multi-market and single market models.

关键词： Performance evaluation 5G mobile communication Computational modeling Biological system modeling Games Ultra reliable low latency communication approximation algorithms Double auction edge computing multi-market fictitious play

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Brief Announcement: Towards Proportionate Fair Assignment 26th

Brief Announcement: Towards Proportionate Fair Assignment

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26th International Symposium on Stabilization, Safety, and Security of Distributed Systems

作者： Schieber, Baruch New Jersey Inst Technol Newark NJ 07102 USA

ISBN: (纸本)9783031744976;9783031744983

The well known assignment problem finds an optimal assignment of tasks to agents. Optimal assignment is applicable to the placement of virtual machines in cloud systems to optimize resource allocation and ensure efficient operation. It is also applicable to fault-tolerant computation to ensure continued operation in case of component failures. In some instances the assignment needs to satisfy extraneous fairness constraints. For example, in a multi-tenant cloud environment the assignment has to guarantee equitable treatment of customers. We initiate the study of proportionate fair assignment. In the multi-tenant setting such an assignment ensures proportionate representation of the customers over all servers. We show that even the simple case of computing an assignment that ensures equal representation of two customers is hard. On the positive side, we present a 1/2-approximation algorithm for computing an assignment that ensures equal representation of two customers.

关键词： Assignment Perfect matching Proportionate fairness Cloud system approximation algorithms

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Improved algorithms for Minimum-Membership Geometric Set Cover 10th

Improved Algorithms for Minimum-Membership Geometric Set Cov...

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10th International Conference on algorithms and Discrete Applied Mathematics (CALDAM)

作者： Govindarajan, Sathish Sarkar, Siddhartha Indian Inst Sci Bengaluru India

ISBN: (纸本)9783031522123;9783031522130

Bandyapadhyay et al. introduced the generalized minimum-membership geometric set cover (GMMGSC) problem [SoCG, 2023], which is defined as follows: We are given two sets P and P' of points in R-2, n = max(|P|, | P'|), and a set S of m axis-parallel unit squares. The goal is to find a subset S* subset of S that covers all the points in P while minimizing memb(P', S*), where memb(P', S*) = max(p is an element of P)' |{s is an element of S* : p is an element of s}|. We study GMMGSC problem and give a 16-approximation algorithm that runs in O(m(2) logm+ m(2)n) time. Our result is a significant improvement to the 144-approximation given by Bandyapadhyay et al. that runs in (O) over tilde (nm) time. GMMGSC problem is a generalization of another well-studied problem called Minimum Ply Geometric Set Cover (MPGSC), in which the goal is to minimize the ply of S*, where the ply is the maximum cardinality of a subset of the unit squares that have a non-empty intersection. The best-known result for the MPGSC problem is an 8-approximation algorithm by Durocher et al. that runs in O(n+ m(8)k(4) log k + m8 log m log k) time, where k is the optimal ply value [WALCOM, 2023].

关键词： Computational Geometry Minimum-Membership Geometric Set Cover Minimum Ply Covering approximation algorithms

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Path Planning of Randomly Scattering Waypoints for Wafer Probing Based on Deep Attention Mechanism

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第1期53卷 529-541页

作者： Shi, Haobin Li, Jingchen Liang, Meng Hwang, Maxwell Hwang, Kao-Shing Hsu, Yun-Yu Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Zhejiang Univ Sch Med Hangzhou 310000 Peoples R China Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 80424 Taiwan

Wafer probing is a critical process employed to measure the yield of wafer fabrication. The primary object of wafer probing is to find the defect grain on the wafer. After a full coverage check, there are always some suspected grains existing for further inspection. However, this second probing result could be affected by the shape of the probe card and the setting actions (path planning) of operators for grains randomly scattering on the wafer. Good grains can be damaged by reprobe actions, which decrease production performance and customer trust. In general, it also requires manpower to perform reprobing, which dramatically deteriorates the throughput of production. This article has studied this problem, and an adaptive coverage path planning (CPP) method for randomly scattering grains using an attention interface is proposed. The proposed randomly scattering waypoints method uses deep reinforcement learning (DRL) for automatic real-time path planning of the second detection. A soft attention interface accelerates the process with a less overlapped check. The experimental results demonstrate the efficiency of the proposed method in terms of less overlapping and steps, and this method learns a better CPP strategy for wafer probing than programmed paths and other RL-based methods.

关键词： Path planning Artificial neural networks Testing Semiconductor device modeling Probes approximation algorithms Task analysis Attention mechanism (AM) coverage path planning (CPP) deep reinforcement learning (DRL) wafer probing

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Evaluation of Neural Network Verification Methods for Air-to-Air Collision Avoidance

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Journal of Air Transportation 2023年第1期31卷 1-17页

作者： Lopez, Diego Manzanas Johnson, Taylor T. Bak, Stanley Tran, Hoang-Dung Hobbs, Kerianne L. Vanderbilt University Electrical Engineering and Computer Science 1025 16th Avenue South Suite 102 NashvilleTN37235 United States Stony Brook University 100 Nicolls Road Stony Brook NY11794 United States University of Nebraska at Lincoln LincolnNE68588 United States U.S. Air Force Research Laboratory 2210 8th Street Wright-Patterson Air Force Base OH45433 United States

Neural network approximations have become attractive to compress data for automation and autonomy algorithms for use on storage-limited and processing-limited aerospace hardware. However, unless these neural network approximations can be exhaustively verified to be safe, they cannot be certified for use on aircraft. An example of such systems is the unmanned Airborne Collision Avoidance System (ACAS) Xu, which is a very popular benchmark for open-loop neural network control system verification tools. This paper proposes a new closed-loop extension of this benchmark, which consists of a set of 10 closed-loop properties selected to evaluate the safety of an ownship aircraft in the presence of a co-altitude intruder aircraft. These closed-loop safety properties are used to evaluate five of the 45 neural networks that comprise the ACAS Xu benchmark (corresponding to co-altitude cases) as well as the switching logic between the five neural networks. The combination of nonlinear dynamics and switching between five neural networks is a challenging verification task accomplished with star-set reachability methods in two verification tools. The safety of the ownship aircraft under initial position uncertainty is guaranteed in every scenario proposed. © 2022 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

关键词： approximation algorithms

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Mixed-integer simulation optimization for multi-echelon inventory problems with lost sales

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Journal of the Operational Research Society 2023年第11期74卷 2312-2326页

作者： Tsai, Shing Chih Wang, Honggang Hung, Li Hsuan National Cheng Kung University Tainan City Taiwan California State Polytechnic University-Pomona PomonaCA United States

We propose a mixed-integer simulation optimization framework for solving multi-echelon inventory problems with lost sales. We want to seek optimal settings of the order-up-to levels and the review intervals for warehouse and retailers. The aim is to minimize the total expected costs including the inventory holding cost, the ordering cost and the penalty cost. The proposed optimization method represents a complementary combination of ranking-and-selection procedures and stochastic-approximation algorithms for both integer-valued and real-valued variables. We provide a proof for the finite-time statistical validity of the developed algorithm. We also discuss the convergence conditions for the asymptotic optimality of our algorithm. The algorithmic performance is examined with experiments under different parameter settings and stopping conditions. During the experiments, our algorithm performs favorably in comparison to the popular Arena optimization tool, OptQuest. © Operational Research Society 2022.

关键词： approximation algorithms

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Quickly Determining Who Won an Election 15

Quickly Determining Who Won an Election

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15th Innovations in Theoretical Computer Science Conference (ITCS)

作者： Hellerstein, Lisa Liu, Naifeng Schewior, Kevin NYU Dept Comp Sci & Engn Tandon Sch Engn Brooklyn NY 11201 USA CUNY Grad Ctr Dept Comp Sci New York NY USA Univ Mannheim Dept Econ Mannheim Germany Univ Southern Denmark Dept Comp Sci & Math Odense Denmark

ISBN: (纸本)9783959773096

This paper considers elections in which voters choose one candidate each, independently according to known probability distributions. A candidate receiving a strict majority (absolute or relative, depending on the version) wins. After the voters have made their choices, each vote can be inspected to determine which candidate received that vote. The time (or cost) to inspect each of the votes is known in advance. The task is to (possibly adaptively) determine the order in which to inspect the votes, so as to minimize the expected time to determine which candidate has won the election. We design polynomial-time constant-factor approximation algorithms for both the absolute-majority and the relative-majority version. Both algorithms are based on a two-phase approach. In the first phase, the algorithms reduce the number of relevant candidates to O(1), and in the second phase they utilize techniques from the literature on stochastic function evaluation to handle the remaining candidates. In the case of absolute majority, we show that the same can be achieved with only two rounds of adaptivity.

关键词： stochastic function evaluation voting approximation algorithms

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Scheduling with Fully Compressible Tasks: Application to Deep Learning Inference with Neural Network Compression 24

Scheduling with Fully Compressible Tasks: Application to Dee...

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24th IEEE/ACM International Symposium on Cluster, Cloud, and Internet Computing (CCGrid)

作者： Barros, Tiago da Silva Giroire, Frederic Aparicio-Pardo, Ramon Perennes, Stephane Natale, Emanuele Univ Cote Azur CNRS I3S Inria Sophia Antipolis France

ISBN: (纸本)9798350395679;9798350395662

With the advent and the growing usage of Machine Learning as a Service (MLaaS), cloud and network systems are now offering the possibility to deploy ML tasks on heterogeneous clusters. Then, network and cloud operators have to schedule these tasks, determining both when and on which devices to execute them. In parallel, several solutions, such as neural network compression, were proposed to build small models which can run on limited hardware. These solutions allow choosing the model size at inference time for any targeted processing time without having to re-train the network. In this work, we consider the Deadline Scheduling with Compressible Tasks (DSCT) problem: a novel scheduling problem with task deadlines where the tasks can be compressed. Each task can be executed with a certain compression, presenting a trade-off between its compression level (and, its processing time) and its obtained utility. The objective is to maximize the tasks utilities. We propose an approximation algorithm with proved guarantees to solve the problem. We validate its efficiency with extensive simulation, obtaining near optimal results. As application scenario, we study the problem when the tasks are Deep Learning classification jobs, and the objective is to maximize their global accuracy, but we believe that this new framework and solutions apply to a wide range of application cases.

关键词： scheduling compressible tasks neural network compression approximation algorithms convex programming

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Inserting Multiple Edges into a Planar Graph

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Journal of Graph algorithms and Applications 2023年第6期27卷 489-522页

作者： Chimani, Markus Hliněný, Petr Theoretical Computer Science University Osnabrück Germany Faculty of Informatics Masaryk University Brno Czech Republic

Let G be a connected planar (but not yet embedded) graph and F a set of edges with ends in V (G) and not belonging to E(G). The multiple edge insertion problem (MEI) asks for a drawing of G + F with the minimum number of pairwise edge crossings, such that the subdrawing of G is plane. A solution to this problem is known to approximate the crossing number of the graph G + F, but unfortunately, finding an exact solution to MEI is NP-hard for general F . The MEI problem is linear-time solvable for the special case of |F | = 1 (SODA 01 and Algorithmica), and there is a polynomial-time solvable extension in which all edges of F are incident to a common vertex which is newly introduced into G (SODA 09). The complexity for general F but with constant k = |F | was open, but algorithms both with relative and absolute approximation guarantees have been presented (SODA 11, ICALP 11 and JoCO). We present a fixed-parameter algorithm for the MEI problem in the case that G is biconnected, which is extended to also cover the case of connected G with cut vertices of bounded degree. These are the first exact algorithms for the general MEI problem, and they run in time O(|V (G)|) for any constant k. © 2023, Brown University. All rights reserved.

关键词： approximation algorithms

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On the firefighter problem with spreading vaccination for maximizing the number of saved nodes: the IP model and LP rounding algorithms

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Optimization Letters 2023年第8期17卷 1853-1872页

作者： Yang, Yongge Chen, Po-An Lee, Yu-Ching Fanchiang, Yung-Yan Department of Industrial Engineering and Engineering Management National Tsing Hua University 101 Section 2 Kuang-Fu Road Hsinchu City300044 Taiwan Institute of Information Management National Yang Ming Chiao Tung University 1001 University Road Hsinchu City300010 Taiwan

When an infectious disease spreads, how to quickly vaccinate with a limited budget per time step to reduce the impact of the virus is very important. Specifically, vaccination will be carried out in every time step, and vaccinated nodes will no longer be infected. Meanwhile, the protection from vaccination can spread to the neighbors of a vaccinated node. Our goal is to efficiently find optimal and approximation solutions to our problem with various algorithms. In this paper, we first design an integer linear program to solve this problem. We then propose approximation algorithms of (1) Linear programming (LP) deterministic threshold rounding, (2) LP dependent randomized rounding, and (3) LP independent randomized rounding. We prove that the LP independent randomized rounding algorithm has a high probability of finding a feasible solution that gives an approximation ratio of (1 - δ) , where a small constant δ between 0 and 1 reduces the lower bound on the feasibility probability. We also provide experimental results for three different rounding algorithms to show that they perform numerically well in terms of approximation ratios. These analytical and numerical studies allow each individual to adopt the most appropriate approximation algorithm to efficiently resolve the vaccination problem when her reliance on commercial optimization solvers is costly. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： approximation algorithms

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