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Sparse convex optimization toolkit: a mixed-integer framework

OPTIMIZATION METHODS & SOFTWARE 2023年第6期38卷 1269-1295页

作者： Olama, Alireza Camponogara, Eduardo Kronqvist, Jan Univ Fed Santa Catarina Automation & Syst Engn Dept Florianopolis Brazil KTH Royal Inst Technol Dept Math Stockholm Sweden Fed Univ Santa Catarina UFSC Dept Automation & Syst Engn Florianopolis Brazil

This paper proposes an open-source distributed solver for solving Sparse Convex Optimization (SCO) problems over computational networks. Motivated by past algorithmic advances in mixed-integer optimization, the Sparse Convex Optimization Toolkit (SCOT) adopts a mixed-integer approach to find exact solutions to SCO problems. In particular, SCOT combines various techniques to transform the original SCO problem into an equivalent convex Mixed-Integer Nonlinear Programming (MINLP) problem that can benefit from high-performance and parallel computing platforms. To solve the equivalent mixed-integer problem, we present the distributed Hybrid Outer Approximation (DiHOA) algorithm that builds upon the LP/NLP-based branch-and-bound and is tailored for this specific problem structure. The DiHOA algorithm combines the so-called single- and multi-tree outer approximation, naturally integrates a decentralized algorithm for distributed convex nonlinear subproblems, and employs enhancement techniques such as quadratic cuts. Finally, we present detailed computational experiments that show the benefit of our solver through numerical benchmarks on 140 SCO problems with distributed datasets. To show the overall efficiency of SCOT we also provide solution profiles comparing SCOT to other state-of-the-art MINLP solvers.

关键词： Sparse optimization mixed-integer nonlinear programming distributed computing outer approximation >

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Bankrupting Sybil despite churn

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2023年第1期135卷 89-124页

作者： Gupta, Diksha Saia, Jared Young, Maxwell IBM Res Singapore Lab 9 Changi Business Pk Cent 1 Singapore 486048 Singapore Univ New Mexico Dept Comp Sci Albuquerque NM 87131 USA Mississippi State Univ Dept Comp Sci & Engn Mississippi State MS 39762 USA

A Sybil attack occurs when an adversary controls multiple system identifiers (IDs). Limiting the number of Sybil (bad) IDs to a minority is critical for tolerating malicious behavior. A popular tool for enforcing a bad minority is resource burning (RB): the verifiable consumption of a network resource. Unfortunately, typical RB defenses require non-Sybil (good) IDs to consume at least as many resources as the adversary. We present a new defense, ERGO, that guarantees (1) there is always a bad minority;and (2) during a significant attack, the good IDs consume asymptotically less resources than the bad. Specifically, despite high churn, the good-ID RB rate is O (root T J + J), where T is the adversary's RB rate, and J is the good-ID join rate. We show this RB rate is asymptotically optimal for a large class of algorithms, and we empirically demonstrate the benefits of ERGO. (c) 2023 Elsevier Inc. All rights reserved.

关键词： distributed computing Algorithm design and analysis Sybil attack Resource burning Security

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Game-Aided Blockchain Twin for Incentive and Relay-Free Model Sharing in Heterogeneous Chain-Driven Swarm Learning

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IEEE SYSTEMS JOURNAL 2023年第4期17卷 5786-5797页

作者： Qi, Yuxin Lin, Xi Wu, Jun Han, Yunyun Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Key Lab Integrated Adm Technol Informat S Shanghai 200240 Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Fukuoka 8080135 Japan Bitverse Shanghai Technol Co Ltd Shanghai 200240 Peoples R China

Swarm learning (SL) is a novel decentralized machine learning paradigm that provides a privacy-preserving approach based on permissioned blockchain without the need for a centralized coordinator. However, the various architectures and design characteristics of blockchains make it difficult to employ applications on heterogeneous blockchains, which limits the scalability, efficiency, and interoperability of blockchains ecology and restricts the application of SL. To solve this problem, first, we propose a Blockchain Twin mechanism consisting of multichains to enable model sharing between heterogeneous blockchains without single central relay-chain. Next, to encourage roles in Blockchain Twin to actively and honestly participate in consensus phase, we design a multileader multifollower Stackelberg game-based incentive mechanism. Additionally, we prove that a unique Stackelberg equilibrium exists in the game and propose an alternating direction method of multipliers (ADMM)-based algorithm to obtain the optimal solution. Finally, we evaluate the performance of twin-chain interactions regarding average delay and throughput. We also conduct numerical simulation on the proposed incentive mechanism, and the results show that our mechanism can jointly maximize the reward of every participant roles in Blockchain Twin.

关键词： Artificial intelligence data storage systems distributed computing distributed information systems system improvement

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Synchronization modulo P in dynamic networks

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THEORETICAL COMPUTER SCIENCE 2023年 942卷 200-212页

作者： Monterno, Louis Penet de Charron-Bost, Bernadette Merz, Stephan IP Paris Ecole Polytech F-91128 Palaiseau France Ecole Normale Super DI ENS 45 Rue Ulm F-75005 Paris France Univ Lorraine CNRS Inria LORIA F-54000 Nancy France

We define the mod P-synchronization problem as a weakening of the firing squad problem, where all nodes fire not at the same round, but at rounds that are all equal modulo P. We introduce an algorithm that achieves mod P-synchronization despite asynchronous starts in every dynamic network whose dynamic radius is bounded by some integer A, that is, there always exists a temporal path of length at most A from some fixed node y, called a central node of the network, to all the other nodes. As opposed to the perfect synchronization achieved in the firing squad problem, mod P-synchronization thus does not require the network to be strongly connected. In our algorithm, nodes know A, but they ignore which nodes are central in the network. We also prove that if the bound A on the radius exists but is unknown, then mod P-synchronization is *** nodes in our algorithm fire in less that 6Pn rounds, where n is the number of nodes, after all nodes become active, but use unbounded counters. We then present a refinement of this algorithm so that memory usage becomes bounded while maintaining the same time complexity. The correctness of our first algorithm has been formally established in the proof assistant Isabelle.(c) 2022 Elsevier B.V. All rights reserved.

关键词： distributed computing Dynamic graph Synchronization Firing squad

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Multirobot Onsite Shared Analytics Information and computing

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2023年第1期10卷 169-181页

作者： Hook, Joshua Vander Rossi, Federico Vaquero, Tiago Troesch, Martina Net, Marc Sanchez Schoolcraft, Joshua de la Croix, Jean-Pierre Chien, Steve CALTECH Jet Prop Lab Pasadena CA 91101 USA

Computation load sharing across a network of heterogeneous robots is a promising approach to increase robots' capabilities and efficiency as a team in extreme environments. However, in such environments, communication links may be intermittent and connections to the cloud or Internet may be nonexistent. In this article, we introduce a communication-aware, computation task-scheduling problem for multirobot systems and propose an integer linear program (ILP) that optimizes the allocation of computational tasks across a network of heterogeneous robots, accounting for the networked robots' computational capabilities and for available (and possibly time-varying) communication links. We consider scheduling of a set of interdependent required and optional tasks modeled by a dependency graph. We present a consensus-backed scheduling architecture for shared-world, distributed systems. We validate the ILP formulation and the distributed implementation in different computation platforms and in simulated scenarios with a bias toward lunar or planetary exploration scenarios. Our results show that the proposed implementation can optimize schedules to allow a three-fold increase in the amount of rewarding tasks performed (e.g., science measurements) compared to an analogous system with no computational load sharing.

关键词： Task analysis Robots Scheduling Computer architecture Computational modeling Throughput Schedules Autonomous robots concurrency control-scheduling algorithms distributed computing multi robot systems space exploration

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Moving small files in a networked environment

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年 139卷 167-180页

作者： Jin, Chao Abramson, David Carroll, Jake Liu, Zhengchun Kettimuthu, Rajkumar Univ Queensland St Lucia Qld 4072 Australia Argonne Natl Lab Data Sci & Learning Div Argonne IL 60439 USA

Globally distributed computing infrastructures, such as clouds and supercomputers, are currently used to manage data that is generated with an unprecedented speed from a variety of resources. Coping with this trend, the volume of data exchanged across distant sites increases substantially. To accelerate data transfer, high-speed networks are provided to connect remote sites. Most existing data movement solutions are optimized for moving large files. However, it is still challenging to transfer a large number of small files across networks. This disadvantage not only lowers data transfer performance, but also decreases overall system utilization. We identify that moving small files is mainly constrained by degraded file system throughput, not just network performance as might be suspected. We have built a data transfer pipeline model to analyze the impact of small network I/O and storage I/O on data movement. Extending one of the widely used open source data movement solutions, GridFTP, we demonstrate several appropriate engineering approaches that mitigate the bottleneck and increase data transfer efficiency. We show optimizations that improve data transfer performance more than 5 times. In comparison to existing solutions, our approaches can save a significant amount of system resources for moving lots of small *** Copyright (c) 2022 Published by Elsevier B.V. All rights reserved.

关键词： Data movement File transfer Small files High-speed networking distributed computing Data transfer pipeline GridFTP

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Systematic Literature Review on Parallel Trajectory-based Metaheuristics

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ACM computing SURVEYS 2023年第8期55卷 1-34页

作者： Barroso Almeida, Andre Luis Lima, Joubert de Castro Carvalho, Marco Antonio M. Univ Fed Ouro Preto Ouro Preto MG Brazil Inst Fed Educ Ciencia & Tecnol Minas Gerais Ouro Preto MG Brazil

In the past 35 years, parallel computing has drawn increasing interest from the academic community, especially in solving complex optimization problems that require large amounts of computational power. The use of parallel (multi-core and distributed) architectures is a natural and effective alternative to speeding up search methods, such as metaheuristics, and to enhancing the quality of the solutions. This survey focuses particularly on studies that adopt high-performance computing techniques to design, implement, and experiment trajectory-based metaheuristics, which pose a great challenge to high-performance computing and represent a large gap in the operations research literature. We outline the contributions from 1987 to the present, and the result is a complete overview of the current state-of-the-art with respect to multi-core and distributed trajectory-based metaheuristics. Basic notions of high-performance computing are introduced, and different taxonomies for multi-core and distributed architectures and metaheuristics are reviewed. A comprehensive list of 127 publications is summarized and classified according to taxonomies and application types. Furthermore, past and future trends are indicated, and open research gaps are identified.

关键词： Metaheuristics high performance computing parallel computing distributed computing

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Auction-and-Learning Based Lagrange Coded computing Model for Privacy-Preserving, Secure, and Resilient Mobile Edge computing

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IEEE TRANSACTIONS ON MOBILE computing 2023年第2期22卷 744-764页

作者： Asheralieva, Alia Niyato, Dusit Xiong, Zehui Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Guangdong Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Singapore Univ Technol & Design Informat Syst Technol & Design Pillar Singapore 487372 Singapore

We design a novel encoding model based on Lagrange coded computing (LCC) for private, secure, and resilient distributed mobile edge computing (MEC) systems, where multiple base stations (BSs) act as "masters" offloading their computations to edge nodes acting as "workers". A two-fold objective of the scheme is: i) efficient allocation of computing tasks to the workers;ii) providing the workers with appropriate incentives to complete their tasks. As such, each master must decide on its offloading requests to the workers including the allocated tasks and service fees to be paid. This problem is complex due to the following reasons: i) masters can be privately-owned or managed by different operators, i.e., there is no communication and no coordination among them;ii) workers are heterogeneous non-dedicated nodes with limited and nondeterministic transmission and computing resources. As a result, the masters must compete for constrained resources of workers in a stochastic partially-observable environment. To address this problem, we define the interactions between masters and workers as a direct stochastic first-price-sealed-bid (FPSB) auction. To analyze the auction, we represent it as a stochastic Bayesian game and develop a Bayesian learning framework to perfect the auction solution.

关键词： Task analysis distributed computing Computational modeling Security Resilience Bayes methods Privacy Auction theory Bayesian methods coded distributed computing data privacy deep learning game theory incomplete information Internet of Things machine learning Markov processes mobile edge computing pricing reinforcement learning resiliency resource allocation security

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Cooperative hybrid consensus with function optimization for blockchain

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CLUSTER computing-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第6期26卷 3565-3576页

作者： Sheshgelani, Mohammadreza Ipchi Pashazadeh, Saeid Salehpoor, Pedram Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

Blockchain-based consensus methods such as PoW (Proof of Work) and PoS (Proof of Stake) are widely used and favored these days, but each has disadvantages. One of the significant issues PoW encountered with it is wasting a large amount of energy. PoW faces environmental protection problems and has serious opponents. To address this problem and others, we propose a cooperative hybrid consensus method that uses PoW initially, followed by PoS to choose a leader for adding the block to the blockchain. We change the primary hash calculation in PoW with function optimization. Our protocol provides a cooperation mechanism to collaborate with decentralized nodes. Every node that works fine gets a reward for its cooperation. To evaluate the performance of the proposed method, we simulated it using different benchmark functions. Simulated experiments demonstrate that our proposed protocol successfully performs optimization with the iteration-best measurement. Also, we proposed the security analysis of our protocol. The security analysis and experimental results represent that our proposed protocol is appropriate in practice.

关键词： Consensus Metaheuristic function optimization distributed computing Blockchain

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Machine-learning assisted scheduling optimization and its application in quantum chemical calculations

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JOURNAL OF COMPUTATIONAL CHEMISTRY 2023年第12期44卷 1174-1188页

作者： Ma, Yingjin Li, ZhiYing Chen, Xin Ding, Bowen Li, Ning Lu, Teng Zhang, Baohua Suo, BingBing Jin, Zhong Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China ShenZhen Bay Lab Shenzhen Peoples R China Chinese Acad Sci Inst Chem Beijing Peoples R China Wenzhou Univ Coll Chem & Mat Engn Wen Zhou Peoples R China Northwest Univ Dept Phys Xian Peoples R China Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China Northwest Univ Dept Phys Xian 710069 Peoples R China

Easy and effective usage of computational resources is crucial for scientific calculations, both from the perspectives of timeliness and economic efficiency. This work proposes a bi-level optimization framework to optimize the computational sequences. Machine-learning (ML) assisted static load-balancing, and different dynamic load-balancing algorithms can be integrated. Consequently, the computational and scheduling engine of the ParaEngine is developed to invoke optimized quantum chemical (QC) calculations. Illustrated benchmark calculations include high-throughput drug suit, solvent model, P38 protein, and SARS-CoV-2 systems. The results show that the usage rate of given computational resources for high throughput and large-scale fragmentation QC calculations can primarily profit, and faster accomplishing computational tasks can be expected when employing high-performance computing (HPC) clusters.

关键词： distributed computing fragmentation approach high throughput computing interaction energy calculations load-balancing

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