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Early Adapting to Trends: Self-Stabilizing Information Spread using Passive Communication 22

Early Adapting to Trends: Self-Stabilizing Information Sprea...

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acm symposium on principles of distributed computing (PODC)

作者： Korman, Amos Vacus, Robin Tel Aviv Univ French Natl Ctr Sci Res CNRS French Israeli Lab Fdn Comp Sci UMI FILOFOCSUP7HUJIWISInt Joint Res Unit Tel Aviv Israel French Natl Ctr Sci Res CNRS Inst Res Fundamental Comp Sci IRIF Paris France

ISBN: (纸本)9781450392624

How to efficiently and reliably spread information in a system is one of the most fundamental problems in distributed computing. Recently, inspired by biological scenarios, several works focused on identifying the minimal communication resources necessary to spread information under faulty conditions. Here we study the self-stabilizing bit-dissemination problem, introduced by Boczkowski, Korman, and Natale in [SODA 2017]. The problem considers a fully-connected network of n agents, with a binary world of opinions, one of which is called correct. At any given time, each agent holds an opinion bit as its public output. The population contains a source agent which knows which opinion is correct. This agent adopts the correct opinion and remains with it throughout the execution. We consider the basic PULL model of communication, in which each agent observes relatively few randomly chosen agents in each round. The goal of the non-source agents is to quickly converge on the correct opinion, despite having an arbitrary initial configuration, i.e., in a self-stabilizing manner. Once the population converges on the correct opinion, it should remain with it forever. Motivated by biological scenarios in which animals observe and react to the behavior of others, we focus on the extremely constrained model of passive communication, which assumes that when observing another agent the only information that can be extracted is the opinion bit of that agent. We prove that this problem can be solved in a poly-logarithmic in n number of rounds with high probability, while sampling a logarithmic number of agents at each round. Previous works solved this problem faster and using fewer samples, but they did that by decoupling the messages sent by agents from their output opinion, and hence do not fit the framework of passive communication. Moreover, these works use complex recursive algorithms with refined clocks that are unlikely to be used by biological entities. In contrast, our propo

关键词： Consensus protocols Population protocols Biological distributed algorithms Self-stabilization Passive communication

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FASOP: Fast yet Accurate Automated Search for Optimal Parallelization of Transformers on Heterogeneous GPU Clusters 24

FASOP: Fast yet Accurate Automated Search for Optimal Parall...

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33rd International symposium on High-Performance Parallel and distributed computing (HPDC)

作者： Hwang, Sunyeol Lee, Eungyeong Oh, Hongseok Yi, Youngmin Univ Seoul Seoul South Korea

ISBN: (纸本)9798400704130

Transformer-based large language models have recently shown remarkable performance, but their significantly large parameters require efficient training, which is commonly realized by utilizing both data- and model-parallel deep learning on a GPU cluster. To minimize the training time, the optimal degrees of data and model parallelisms and the optimal model partitioning should be searched. When heterogeneous GPU clusters are used to utilize as many GPUs as possible, it becomes more challenging. In this work, we propose a framework named FASOP that automatically and rapidly finds the (near-)optimal degrees of parallelisms and model partitioning of Transformer-based models on heterogeneous GPU clusters, with an accurate estimation of pipelining latency and communications. Moreover, it can search for optimal cluster configurations that minimize the training time while satisfying the cost of GPU clusters. The proposed model partitioning algorithm in FASOP is three orders of magnitude faster than Dynamic Programming in the state-of-the-art for GPT-2 1.5B on a mixed set of 32 GPUs with A100 and A10, leading to a few seconds instead of several hours. And, FASOP shows only 8.7% mean absolute error in training time estimation for GPT-2 1.5B. With a fast yet accurate search, FASOP achieved up to 1.37x speedup compared to Megatron-LM.

关键词： Heterogeneous cluster distributed deep learning Parallelization strategy Large language model

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Brief Announcement: Gathering Despite a Linear Number of Weakly Byzantine Agents 22

Brief Announcement: Gathering Despite a Linear Number of Wea...

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acm symposium on principles of distributed computing (PODC)

作者： Hirose, Jion Nakamura, Junya Ooshita, Fukuhito Inoue, Michiko Nara Inst Sci & Technol Ikoma Nara Japan Toyohashi Univ Technol Toyohashi Aichi Japan Fukui Univ Technol Fukui Fukui Japan

ISBN: (纸本)9781450392624

We study the gathering problem to make multiple agents initially scattered in arbitrary networks gather at a single node. There exist k agents with unique identifiers (IDs) in the network, and.. of them are weakly Byzantine agents, which behave arbitrarily except for falsifying their IDs. The agents behave in synchronous rounds, and each node does not have any memory like a whiteboard. In the literature, there exists a gathering algorithm that tolerates any number of Byzantine agents, while the fastest gathering algorithm requires Omega(f(2)) non-Byzantine agents. This paper proposes an algorithm that solves the gathering problem efficiently with Omega(f(2)) non-Byzantine agents since there is a large gap between the number of non-Byzantine agents in the previous works. The proposed algorithm achieves the gathering in O(f center dot |Lambda(good) | center dot X (Nu)) rounds in case of 9f + 8 <= k and simultaneous startup if.. is given to agents, where ||Lambda(good) | | is the length of the largest ID among nonByzantine agents, and |Lambda(good) | is the number of rounds required to explore any network composed of n nodes. This algorithm is faster than the most fault-tolerant existing algorithm and requires fewer non-Byzantine agents than the fastest algorithm if.. is given to agents, although the guarantees on simultaneous termination and startup delay are not the same. To achieve this property, we propose a new technique to simulate a Byzantine consensus algorithm for synchronous message-passing systems on agent systems.

关键词： Gathering distributed algorithms Byzantine environments

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Serialization-Oriented Data Layout for distributed and Real-Time Agent-Based Simulation 28

Serialization-Oriented Data Layout for Distributed and Real-...

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28th IEEE/acm International symposium on distributed Simulation and Real Time Applications, DS-RT 2024

作者： Meng, Zhuoxiao Gao, Mingyue Bortoli, Stefano Sommer, Christoph Knoll, Alois Technical University of Munich Department of Informatics Germany Huawei Munich Research Center Intelligent Cloud Technologies Laboratory Germany TU Dresden Faculty of Computer Science Germany

ISBN: (纸本)9798331527211

Data transfer efficiency is a frequent bottleneck of distributed (co-)simulations and X-in-the-loop systems. One of the key reasons, particularly in Agent-Based Simulation (ABS), is related to the low serialization performance caused by non-optimal data layout in memory. To address these challenges, this paper explores the potentials of a Serialization-oriented Data Layout (SoDaLa) approach for ABS building on Data Oriented Design (DOD) principles. In this work we also introduce ABS_M, a model-based abstraction of memory access in ABS. Using this model, we evaluate the impact of SoDaLa. This is done also to promote the adoption of SoDaLa and to ease the assessment of data layout strategies prior to full implementation in an existing codebases. The results indicate that our proposed approach enhances serialization efficiency and highlights the trade-offs between serialization efficiency and simulation performance across different model specifics and hardware conditions. © 2024 IEEE.

关键词： Digital elevation model

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Antipode: Enforcing Cross-Service Causal Consistency in distributed Applications 23

Antipode: Enforcing Cross-Service Causal Consistency in Dist...

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29th acm symposium on Operating Systems principles (SOSP)

作者： Loff, Joao Ferreira Porto, Daniel Garcia, Joao Mace, Jonathan Rodrigues, Rodrigo Univ Lisbon INESC ID Inst Super Tecn Lisbon Portugal Max Planck Inst Software Syst Saarbrucken Germany Microsoft Res Redmond WA USA

ISBN: (纸本)9798400702297

Modern internet-scale applications *** from cross-service inconsistencies, arising because applications combine multiple independent and mutually-oblivious datastores. The end-to-end *** of each user request spans many *** services and datastores along the way, implicitly establishing ordering dependencies among operations at different datastores. Readers should observe this ordering and, in today's systems, they do not. In this work, we present Antipode, a bolt-on technique for preventing cross-service consistency violations in distributed applications. It enforces cross-service consistency by propagating lineages of datastore operations both alongside end-to-end requests and within datastores. Antipode enables a novel cross-service causal consistency model, which extends existing causality models, and whose enforcement requires us to bring in a series of technical contributions to address fundamental semantic, scalability, and deployment challenges. We implemented Antipode as an application-level library, which can easily be integrated into existing applications with minimal ***, is incrementally deployable, and does not require global knowledge of all datastore operations. We apply Antipode to eight open-source and public cloud datastores and two microservice benchmark applications. Our evaluation demonstrates that Antipode is able to prevent cross-service inconsistencies with limited programming *** and less than 2% impact on end-user latency and throughput.

关键词： Semantics

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computing in Data Science or Data in Computer Science? Exploring the Relationship between Data Science and Computer Science in K-12 Education 2024

Computing in Data Science or Data in Computer Science? Explo...

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55th acm Technical symposium on Computer Science Education, SIGCSE 2024

作者： Drozda, Zarek Walker, Justice Toshiba Fisler, Kathi Weintrop, David The University of Chicago ChicagoIL United States The University of Texas at El Paso El PasoTX United States Brown University ProvidenceRI United States University of Maryland College ParkMD United States

ISBN: (纸本)9798400704246

students to learn in order to succeed in an increasingly data-driven world. Foundational data literacy skills currently live in a number of subjects across K-12 (e.g., data collection and analysis in science classes, statistical calculations in mathematics/statistics, data visualization and communication in civics/social studies), however, a growing number of schools and districts are introducing stand-alone data science (DS) courses. Given the centrality of computing and programming in the contemporary practice of DS, many of these courses include topics historically reserved for computer science (CS) classes. Further, many CS courses include dedicated time for DS topics (e.g., AP Computer Science principles' unit on Data). In many ways, DS educators and CS educators are working towards the same ends in complementary ways. However, at other times, the two disciplines are in tension, especially given the scarcity of time in K-12 student schedules for non-core subjects. This panel will explore what DS education and CS education can learn from each other, how each can contribute and advance the goals of the other, and how these two intertwined disciplines can productively live alongside each other in K-12 settings. © 2024 Owner/Author.

关键词： Data visualization

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Dissecting Overheads of Service Mesh Sidecars 14

Dissecting Overheads of Service Mesh Sidecars

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14th Annual acm symposium on Cloud computing (SoCC)

作者： Zhu, Xiangfeng She, Guozhen Xue, Bowen Zhang, Yu Zhang, Yongsu Zou, Xuan Kelvin Duan, XiongChun He, Peng Krishnamurthy, Arvind Lentz, Matthew Zhuo, Danyang Mahajan, Ratul Univ Washington Seattle WA 98195 USA Duke Univ Durham NC 27706 USA ByteDance Inc Culver City CA USA

ISBN: (纸本)9798400703874

Service meshes play a central role in the modern application ecosystem by providing an easy and flexible way to connect microservices of a distributed application. However, because of how they interpose on application traffic, they can substantially increase application latency and its resource consumption. We develop a tool called MeshInsight to help developers quantify the overhead of service meshes in deployment scenarios of interest and make informed trade-offs about their functionality vs. overhead. Using MeshInsight, we confirm that service meshes can have high overhead-up to 269% higher latency and up to 163% more virtual CPU cores for our benchmark applications-but the severity is intimately tied to how they are configured and the application workload. IPC (inter-process communication) and socket writes dominate when the service mesh operates as a TCP proxy, but protocol parsing dominates when it operates as an HTTP proxy. MeshInsight also enables us to study the end-to-end impact of optimizations to service meshes. We show that not all seemingly-promising optimizations lead to a notable overhead reduction in realistic settings.

关键词： Service Mesh Microservices Cloud computing

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Trust-based Knowledge Sharing Among Federated Learning Servers in Vehicular Edge computing 13

Trust-based Knowledge Sharing Among Federated Learning Serve...

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13th acm International symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (DIVANet)

作者： Mazloomi, Fateme Heydari, Shahram Shah El-Khatib, Khalil Univ Ontario Inst Technol Fac Business & IT Oshawa ON Canada

ISBN: (纸本)9798400703690

Federated Learning (FL) protects privacy during autonomous vehicle machine learning (ML) operations. FL enables cooperative training of a single ML model across multiple edge devices, leveraging distributed datasets while maintaining data locality. Although much research has concentrated on single-server FL for autonomous driving applications within vehicular networks, real-world scenarios often involve several concurrent servers capable of benefiting from each other's knowledge. However, these servers' trustworthiness is paramount when using their global models, as an imprudent choice could significantly decrease FL performance and accuracy. In this paper, we introduce a novel trust-based knowledge-sharing approach among FL servers, wherein the accuracy of shared global models on clients' local data serves as the trust metric. Our proposed methodology enables servers to utilize shared global models from reliable servers for their clients, thereby improving training accuracy and reducing loss. This enhancement is particularly notable during the initial training rounds compared to base FL implementation.

关键词： Federated Learning Vehicular edge computing Trust Multi-server

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36th International symposium on distributed computing, DISC 2022

36th International Symposium on Distributed Computing, DISC ...

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36th International symposium on distributed computing, DISC 2022

ISBN: (纸本)9783959772556

The proceedings contain 49 papers. The topics discussed include: managing the cyber risk in a decoupled world: does this bring potential opportunities in computer science?;using linearizable objects in randomized concurrent programs;good-case early-stopping latency of synchronous byzantine reliable broadcast: the deterministic case;polynomial-time verification and testing of implementations of the snapshot data structure;almost universally optimal distributed Laplacian solvers via low-congestion shortcuts;byzantine connectivity testing in the congested clique;efficient classification of locally checkable problems in regular trees;holistic verification of blockchain consensus;how to meet at a node of any connected graph;liveness and latency of byzantine state-machine replication;and how to wake up your neighbors: safe and nearly optimal generic energy conservation in radio networks.

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Building AI Agents for Autonomous Clouds: Challenges and Design principles 15

Building AI Agents for Autonomous Clouds: Challenges and Des...

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15th Annual acm symposium on Cloud computing, SoCC 2024

作者： Shetty, Manish Chen, Yinfang Somashekar, Gagan Ma, Minghua Simmhan, Yogesh Zhang, Xuchao Mace, Jonathan Vandevoorde, Dax Las-Casas, Pedro Gupta, Shachee Mishra Nath, Suman Bansal, Chetan Rajmohan, Saravan Microsoft United States University of California Berkeley United States University of Illinois Urbana-Champaign United States Indian Institue of Science India Microsoft Research United States Agnes Scott College United States

ISBN: (纸本)9798400712869

The rapid growth in the use of Large Language Models (LLMs) and AI Agents as part of software development and deployment is revolutionizing the information technology landscape. While code generation receives significant attention, a higher-impact application lies in using agents for the operational resilience of cloud services, which currently require significant human effort and domain knowledge. There is a growing interest in AI for IT Operations (AIOps), which aims to automate complex operational tasks, like fault localization and root cause analysis, reducing human intervention and customer impact. However, achieving the vision of autonomous and self-healing clouds through AIOps is hampered by the lack of standardized frameworks for building, evaluating, and improving AIOps agents. This vision paper lays the groundwork for such a framework by framing the requirements and then discussing design decisions that satisfy them. We also propose AIOpsLab, a prototype implementation leveraging agent-cloud-interface that orchestrates an application, injects real-time faults using chaos engineering, and interfaces with an agent to localize and resolve the faults. We report promising results and lay the groundwork to build a modular and robust framework for building, evaluating, and improving agents for autonomous clouds. © 2024 Owner/Author.

关键词： Software design

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