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23rd International Middleware Conference Doctoral symposium, Middleware 2022 - Part of Middleware 2022

ISBN: (纸本)9781450399425

The proceedings contain 5 papers. The topics discussed include: secure and resilient data replication for the client-centric decentralized web;towards efficient privacy-enhancing protocols for highly distributed systems;fast and adaptive BFT state machine replication;binding efficiency and robustness for blockchains using reputation-based Byzantine fault-tolerant consensus algorithms;and SkyBridge: a cross-cloud storage system for sky computing.

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Brief Announcement: Wake Up and Join Me! An Energy-Efficient Algorithm for Maximal Matching in Radio Networks 21

Brief Announcement: Wake Up and Join Me! An Energy-Efficient...

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40th acm SIGACT-SIGOPS symposium on principles of distributed computing (PODC)

作者： Dani, Varsha Gupta, Aayush Hayes, Thomas P. Pettie, Seth Ronin Inst Montclair NJ 07043 USA Univ New Mexico Dept Comp Sci Albuquerque NM 87131 USA Univ Michigan Dept EECS Ann Arbor MI 48109 USA

ISBN: (纸本)9781450385480

We consider networks of small, autonomous devices that communicate with each other wirelessly. Minimizing energy usage is an important consideration in designing algorithms for such networks, as battery life is a crucial and limited resource. Working in a model where both sending and listening for messages deplete energy, we consider the problem of finding a maximal matching of the nodes in a radio network of arbitrary and unknown topology. We present a distributed randomized algorithm that produces, with high probability, a maximal matching. The maximum energy cost per node is O(log(2) n), and the time complexity is O(Delta log(n)). Here n is any upper bound on the number of nodes, and Delta is any upper bound on the maximum degree;n and Delta are parameters of our algorithm that we assume are known a priori to all the processors. We note that there exist families of graphs for which our bounds on energy cost and time complexity are simultaneously optimal up to polylog factors, so any significant improvement would need additional assumptions about the network topology. We also consider the related problem of assigning, for each node in the network, a neighbor to back up its data in case of eventual node failure. Here, a key goal is to minimize the maximum load, defined as the number of nodes assigned to a single node. We present an efficient decentralized low-energy algorithm that finds a neighbor assignment whose maximum load is at most a polylog(n) factor bigger that the optimum.

关键词： distributed Algorithms Energy-Aware Computation Radio Networks Maximal Matching Sensor Networks

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The acm PODS Alberto O. Mendelzon Test-of-Time Award 2023

Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Pri...

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proceedings of the acm SIGACT-SIGMOD-SIGART symposium on principles of Database Systems 2023年 III-IV页

作者： Arenas, Marcelo Fan, Wenfei Neven, Frank Pontificia Universidad Catolica de Chile Santiago Chile Shenzhen Institute of Computing Sciences Shenzhen China Hasselt University Diepenbeek Belgium

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Brief Announcement: On the Message Complexity of Fault-Tolerant Computation: Leader Election and Agreement 21

Brief Announcement: On the Message Complexity of Fault-Toler...

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40th acm SIGACT-SIGOPS symposium on principles of distributed computing (PODC)

作者： Kumar, Manish Molla, Anisur Rahaman Indian Stat Inst Kolkata India

ISBN: (纸本)9781450385480

This paper investigates on the message complexity of the two fundamental problems, namely, leader election and agreement in the crash-fault synchronous and fully-connected distributed network. We present randomized algorithms for both the problems and also develop non-trivial lower bounds on the message complexity. In the so-called implicit version of the two problems, our algorithms achieve sublinear message complexity while tolerating more than a constant fraction of faulty nodes. The algorithms work in anonymous networks, where nodes do not know each other. Specifically, our main results are: (1) A randomized leader election algorithm which elects a leader with high probability in a complete network with n nodes, in which at least [alpha n] nodes are non-faulty and the remaining can be faulty, where alpha >= (log(2) n)In. The time complexity of the algorithm is O(log n/alpha) rounds and message complexity is O((n(0.5) log(2.5) n)/alpha(2.5)) with high probability (i.e., with probability >= 1 - 1/n). A non-trivial lower bound of Omega (n(0.)(5)/alpha(1.5)) messages for any leader election algorithm that tolerates at most (1- alpha)-fraction faulty nodes and succeeds with high probability. (2) A randomized algorithm, tolerating at most left perpendicular(1 - alpha)nright perpendicular faulty nodes, solves agreement in O (log n/alpha) rounds and with high probability uses only O ((n(0.5) log(1.5)n)/alpha(1.)(5)) messages. A match- ing lower bound (up to a polylog n factor) of Omega(n(0.)(5)/alpha(1.5)) messages for any agreement algorithm that tolerates at most (1-alpha)-fraction faulty nodes and succeeds with high probability. A full version of the paper is available at [17].

关键词： distributed algorithm randomized algorithm fault-tolerant algorithm crash-fault leader election agreement message complexity

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TACOS: Topology-Aware Collective Algorithm Synthesizer for distributed Machine Learning 57

TACOS: Topology-Aware Collective Algorithm Synthesizer for D...

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57th Annual IEEE/acm International symposium on Microarchitecture, MICRO 2024

作者： Won, William Elavazhagan, Midhilesh Srinivasan, Sudarshan Gupta, Swati Krishna, Tushar Georgia Institute of Technology United States Intel United States Massachusetts Institute of Technology United States

ISBN: (纸本)9798350350579

The surge of artificial intelligence, particularly large language models, has driven the rapid development of large-scale machine learning clusters. Executing distributed models on these clusters is often constrained by communication overhead, making efficient utilization of available network resources crucial. As a result, the routing algorithm employed for collective communications (i.e., collective algorithms) plays a pivotal role in determining overall performance. Unfortunately, existing collective communication libraries for distributed machine learning are limited by a fixed set of basic collective algorithms. This limitation hinders communication optimization, especially in modern clusters with heterogeneous and asymmetric topologies. Furthermore, manually designing collective algorithms for all possible combinations of network topologies and collective patterns requires heavy engineering and validation efforts. To address these challenges, this paper presents Tacos, an autonomous synthesizer capable of automatically generating topology-aware collective algorithms tailored to specific collective patterns and network topologies. Tacos is highly flexible, synthesizing an All-Reduce algorithm for a heterogeneous 128-NPU system in just 1.08 seconds, while achieving up to a 4.27× performance improvement over state-of-the-art synthesizers. Additionally, Tacos demonstrates better scalability with polynomial synthesis times, in contrast to NP-hard approaches which only scale to systems with tens of NPUs. Tacos can synthesize for 40K NPUs in just 2.52 hours. © 2024 IEEE.

关键词： Adversarial machine learning

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SNTA'22: The 5thWorkshop on Systems and Network Telemetry and Analytics 22

SNTA'22: The 5thWorkshop on Systems and Network Telemetry an...

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31st International symposium on High-Performance Parallel and distributed computing (HPDC)

作者： Kim, Jinoh Cafaro, Massimo Chou, Jerry Sim, Alex Texas A&M Univ Commerce TX 75428 USA Univ Salento Lecce LE Italy Natl Tsing Hua Univ Hsinchu Taiwan Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781450391993

HPC and distributed systems are the driving force for the advancement of many emerging technologies, such as exascale systems, quantum machines, terabit networking, 5G/6G wireless, and cloud/edge computing. The tasks of systems and network telemetry are a key element for effective operations and management of the advancement of many emerging systems and technologies, and require more scalable telemetry and analysis techniques for comprehensive monitoring and analysis. Various input sources such as end systems, switches, firewalls, intrusion sensors and the emerging network elements speaking with different syntax and semantics make organizing and incorporating the generated data challenging for the quantitative and qualitative analysis. This workshop looks for new approaches and methods at the intersection of HPC systems and data sciences to address these difficult challenges of emerging technologies from the diverse angles of systems/network performance, availability, reliability, and security.

关键词： distributed computing methodologies parallel computing methodologies intrusion/anomaly detection and malware mitigation network security network performance evaluation

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Data-Driven Orchestration for distributed RAN Intelligent Controller Placement in 6G Networks 25

Data-Driven Orchestration for Distributed RAN Intelligent Co...

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40th Annual acm symposium on Applied computing, SAC 2025

作者： Hasheminezhad, Elham Di Maio, Antonio Braun, Torsten University of Bern Bern Switzerland

ISBN: (纸本)9798400706295

Open-Radio Access Network (O-RAN) brings an innovative approach to address the issues of controller placement in large-scale 6G networks. In this work, we introduce a Reinforcement Learning (RL) algorithm for decentralized RAN Intelligent Controller Orchestration in 6G Networks, which leverages the online learning capabilities of a multi-agent RL system. Our method achieves up to 66% lower user latency and up to 14% higher user packet delivery ratio compared to state-of-the-art baselines in a broad range of simulated scenarios. Copyright © 2025 held by the owner/author(s).

关键词： Reinforcement learning

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Deterministic Massively Parallel Symmetry Breaking for Sparse Graphs 23

Deterministic Massively Parallel Symmetry Breaking for Spars...

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35th acm symposium on Parallelism in Algorithms and Architectures (SPAA)

作者： Fischer, Manuela Giliberti, Jeff Grunau, Christoph Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781450395458

We consider the problem of designing deterministic graph algorithms for the model of Massively Parallel Computation (MPC) that improve with the sparsity of the input graph, as measured by the standard notion of arboricity. For the problems of maximal independent set (MIS), maximal matching (MM), and vertex coloring, we improve the state of the art as follows. Let lambda denote the arboricity of the..-node input graph with maximum degree Delta. MIS andMM. We develop a low-space MPC algorithm that deterministically reduces the maximum degree to poly(lambda) in O(log log n) rounds, improving and simplifying the randomized O(log log n)-round poly(max(lambda, log n))-degree reduction of Ghaffari, Grunau, Jin [DISC'20]. Our approach when combined with the state-of-the-art O(log Delta + log log n)-round algorithm by Czumaj, Davies, Parter [SPAA'20, TALG'21] leads to an improved deterministic round complexity of O(log lambda + log log n). The above MIS and MM algorithm however works in the setting where the global memory allowed, i.e., the number of machines times the local memory per machine, is superlinear in the input size. We extend them to obtain the first low-space MIS and MM algorithms that work with linear global memory. Specifically, we show that both problems can be solved in deterministic time O(log lambda center dot log log(lambda) n), and even in O(log log n) time for graphs with arboricity at most log(O(1)) log n. In this setting, only a O(log(2) log n)-running time bound for trees was known due to Latypov and Uitto [ArXiv'21]. Vertex Coloring. We present a O(1)-round deterministic algorithm for the problem of O(lambda)-coloring in the linear-memory regime of MPC, with relaxed global memory of n center dot poly(lambda). This matches the round complexity of the state-of-the-art randomized algorithm by Ghaffari and Sayyadi [ICALP'19] and significantly improves upon the deterministic O(lambda(epsilon))-round algorithm by Barenboim and Khazanov [CSR'18]. Our algor

关键词： deterministic algorithms distributed computing massively parallel computation sparse graphs graph algorithms derandomization

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distributed Maximum Flow in Planar Graphs 25

Distributed Maximum Flow in Planar Graphs

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proceedings of the acm symposium on principles of distributed computing

作者： Yaseen Abd-Elhaleem Michal Dory Merav Parter Oren Weimann University of Haifa Haifa Israel Weizmann Institute of Science Rehovot Israel

ISBN: (纸本)9798400718854

The dual of a planar graph G is a planar graph G* that has a vertex for each face of G and an edge for each pair of adjacent faces of G. The profound relationship between a planar graph and its dual has been the algorithmic basis for solving numerous (centralized) classical problems on planar graphs involving distances, flows, and cuts. In the distributed setting however, the only use of planar duality is for finding a recursive decomposition of G [DISC 2017, STOC 2019].In this paper, we initiate the study of distributed algorithms on dual planar graphs. Namely, we extend the distributed algorithmic toolkit (such as recursive decomposition and minor-aggregation) to work on the dual graph G*. These tools can then facilitate various algorithms on G by solving a suitable dual problem on G*.Given a directed planar graph G with positive and negative edge-lengths and hop-diameter D, our key result is an Õ(D2)-round algorithm1 for Single Source Shortest Paths on G*. This algorithm implies an Õ(D2)-round algorithm for Maximum st-Flow in G. Prior to our work, no Õ(poly(D))-round algorithms were known for this problem. When G is undirected, we further obtain a near optimal Õ(D)-round algorithm for computing the weighted girth of G, and a D · no(1)-rounds (1 - ϵ)-approximation algorithm for Maximum st-Flow in G when s and t lie on the same *** main challenges in our work are that G* is not the communication graph (e.g., a vertex of G is mapped to multiple vertices of G*), and that the diameter of G* can be much larger than D (i.e., possibly by a linear factor). We overcome these challenges by carefully defining and maintaining subgraphs of the dual graph G* while applying the recursive decomposition on the primal graph G The main technical difficulty, is that along the recursive decomposition, a face of G gets shattered into (disconnected) components yet we still need to treat it as a dual *** believe that the toolkit developed in this paper for exploiting planar dual

关键词： maximum flow

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FlexScience'22: 12th Workshop on AI and Scientific computing at Scale using Flexible computing Infrastructures 31

FlexScience'22: 12th Workshop on AI and Scientific Computing...

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31st International symposium on High-Performance Parallel and distributed computing, HPDC 2022

作者： Ferrucci, Luca Coppola, Massimo Kavalionak, Hanna Kontopoulos, Ioannis Consiglio Nazionale Delle Ricerche Pisa Italy Harokopio University of Athens Athens Greece

ISBN: (纸本)9781450391993

The 2nd International Workshop on Flexible Resource and Application Management on the Edge (FRAME 2022) is dedicated to the so-called Cloud/Edge Continuum, where Cloud and Edge infrastructures can work together to fulfill requirements from a variety of NextGen applications. Clouds provide appropriate levels of performance to large groups of different users, whereas Edge resources act as a first layer of computing capacity that is closer to the user, to reduce the service latency. With respect to Clouds, Edge infrastructures typically are composed of heterogeneous and constrained resources and introduce new challenges from the viewpoint of security, orchestration and resource management. Tackling these new issues calls for innovative combinations of tools and abstractions, where AI and machine learning techniques complement algorithmic orchestration and optimization, bringing about new levels of distributed adaptivity and self-management. As real-time data-driven decisions can be promptly taken on the spot, without the need to wait for data to travel to the Cloud and back, also interactive and time-sensitive services like the immersive data processing of Extended Reality (XR) applications can be partially extended toward the edge, thus exploiting a better computation to communication tradeoff and smoother connections to improve their QoE and remote collaboration. The FRAME'22 workshop proceedings are available at: https://***/***?id=3526059. © 2022 Owner/Author.

关键词： Edge computing

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