On Queueing Theory for Large-Scale CI/CD Pipelines Optimization

• URL: http://arxiv.org/abs/2504.18705v1
• Date: Fri, 25 Apr 2025 21:34:19 GMT
• Title: On Queueing Theory for Large-Scale CI/CD Pipelines Optimization
• Abstract summary: Continuous Integration and Continuous Deployment (CI/CD) pipelines are central to modern software development.<n>This article proposes a modeling framework based on queueing theory to optimize large-scale CI/CD.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Continuous Integration and Continuous Deployment (CI/CD) pipelines are central to modern software development. In large organizations, the high volume of builds and tests creates bottlenecks, especially under shared infrastructure. This article proposes a modeling framework based on queueing theory to optimize large-scale CI/CD workflows. We formalize the system using classical $M/M/c$ queueing models and discuss strategies to minimize delays and infrastructure costs. Our approach integrates theoretical results with practical techniques, including dynamic scaling and prioritization of CI/CD tasks.

Related papers

• Geometry-Aware Online Scheduling for LLM Serving: From Theoretical Bound to System Practice [12.809268527016599]
  We introduce geometry-aware online scheduling by introducing Smallest Volume First (SVF) algorithm and its highly efficient variant, 1-bit SVF.<n>SVF delivers strong reductions in both average and tail latency, while 1-bit SVF, with merely a single bit information, achieves competitive throughput and latency.<n>This work establishes a theoretically sound and empirically proven approach for resolving memory-constrained scheduling in modern Large Language Model deployments.
  arXiv  Detail & Related papers  (2026-06-21T04:05:38Z)
• A Model-Driven Digital Twin for the Systematic Improvement of DevOps Pipelines [0.0]
  This paper presents a model-driven Digital Twin (DT) for CI/CD pipelines.<n>The proposed DT architecture enables visual abstraction of pipeline structure, failure tracing, and systematic version comparison.<n>The framework provides a modular, foundation for integrating advanced analytical and prescriptive services into software delivery processes.
  arXiv  Detail & Related papers  (2026-04-02T14:09:14Z)
• IQuest-Coder-V1 Technical Report [56.045533451719905]
  We introduce the IQuest-Coder-V1 series-(7B/14B/40B/40B-Loop), a new family of code large language models (LLMs)<n>IQuest-Coder-V1 achieves state-of-the-art performance among competitive models across critical dimensions of code intelligence.
  arXiv  Detail & Related papers  (2026-03-17T16:15:31Z)
• Reinforcement Learning for Dynamic Workflow Optimization in CI/CD Pipelines [0.0]
  This paper proposes a reinforcement learning (RL) based approach to dynamically optimize CI/CD pipeline.<n>The pipeline is modeled as a Markov Decision Process, and an RL agent is trained to make runtime decisions such as selecting full, partial, or no test execution.<n> Experimental results show that the RL optimized pipeline achieves up to a 30 percent improvement in throughput and approximately a 25 percent reduction in test execution time.
  arXiv  Detail & Related papers  (2026-01-15T05:19:46Z)
• Training Report of TeleChat3-MoE [77.94641922160359]
  This technical report mainly presents the underlying training infrastructure that enables reliable and efficient scaling to frontier model sizes.<n>We detail systematic methodologies for operator-level and end-to-end numerical verification accuracy, ensuring consistency across hardware platforms.<n>A systematic parallelization framework, leveraging analytical estimation and integer linear programming, is also proposed to optimize multi-dimensional parallelism configurations.
  arXiv  Detail & Related papers  (2025-12-30T11:42:14Z)
• The Conductor and the Engine: A Path Towards Co-Designed Reasoning [2.112973599034511]
  LLM reasoning relies on extensive test-time computation, driven by internal model training and external agentic orchestration.<n>We analyze this capability-cost trade-off and introduce an optimized reasoning workflow (cepo) that empowers smaller open-source models to outperform models multiple times their size.
  arXiv  Detail & Related papers  (2025-09-24T05:09:43Z)
• Multi-Agent Collaboration via Evolving Orchestration [55.574417128944226]
  Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-solving.<n>We propose a puppeteer-style paradigm for LLM-based multi-agent collaboration, where a centralized orchestrator ("puppeteer") dynamically directs agents ("puppets") in response to evolving task states.<n> Experiments on closed- and open-domain scenarios show that this method achieves superior performance with reduced computational costs.
  arXiv  Detail & Related papers  (2025-05-26T07:02:17Z)
• Scalable Chain of Thoughts via Elastic Reasoning [61.75753924952059]
  Elastic Reasoning is a novel framework for scalable chain of thoughts.<n>It separates reasoning into two phases--thinking and solution--with independently allocated budgets.<n>Our approach produces more concise and efficient reasoning even in unconstrained settings.
  arXiv  Detail & Related papers  (2025-05-08T15:01:06Z)
• LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders [23.70714095931094]
  Long-sequence optimized traNsformer for GPU-Efficient Recommenders.<n>Longer consistently outperforms strong baselines in offline metrics and online A/B testing.
  arXiv  Detail & Related papers  (2025-05-07T13:54:26Z)
• Understanding and Optimizing Multi-Stage AI Inference Pipelines [11.254219071373319]
  HERMES is a Heterogeneous Multi-stage LLM inference Execution Simulator.<n> HERMES supports heterogeneous clients executing multiple models concurrently unlike prior frameworks.<n>We explore the impact of reasoning stages on end-to-end latency, optimal strategies for hybrid pipelines, and the architectural implications of remote KV cache retrieval.
  arXiv  Detail & Related papers  (2025-04-14T00:29:49Z)
• ParallelFlow: Parallelizing Linear Transformers via Flow Discretization [4.272515397452792]
  We present a theoretical framework for analyzing linear attention models through matrix-valued state space models (SSMs)<n>Our approach, Parallel Flows, provides a perspective that systematically decouples temporal dynamics from implementation constraints.<n>As a concrete application, we analyze DeltaNet in a generalized low-rank setting motivated by recent theoretical advances.
  arXiv  Detail & Related papers  (2025-04-01T07:34:07Z)
• Adaptive Orchestration for Inference of Large Foundation Models at the Edge [46.1232919707345]
  Large Foundation Models (LFMs) promise to unlock new capabilities for next-generation Edge AI applications.<n> performing inference with LFMs in resource-constrained and heterogeneous edge environments presents significant challenges for workload orchestration.<n>We propose a novel adaptive orchestration method and system tailored specifically for managing distributed inference workloads.
  arXiv  Detail & Related papers  (2025-03-19T15:35:56Z)
• Will Pre-Training Ever End? A First Step Toward Next-Generation Foundation MLLMs via Self-Improving Systematic Cognition [86.21199607040147]
  Self-Improving cognition (SIcog) is a self-learning framework for constructing next-generation foundation language models.<n>We introduce Chain-of-Description, a step-by-step visual understanding method, and integrate structured chain-of-thought (CoT) reasoning to support in-depth multimodal reasoning.<n>Extensive experiments demonstrate that SIcog produces next-generation foundation MLLMs with substantially improved multimodal cognition.
  arXiv  Detail & Related papers  (2025-03-16T00:25:13Z)
• Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design [59.00758127310582]
  We propose a novel framework Read-ME that transforms pre-trained dense LLMs into smaller MoE models. Our approach employs activation sparsity to extract experts. Read-ME outperforms other popular open-source dense models of similar scales.
  arXiv  Detail & Related papers  (2024-10-24T19:48:51Z)
• Inference Optimization of Foundation Models on AI Accelerators [68.24450520773688]
  Powerful foundation models, including large language models (LLMs), with Transformer architectures have ushered in a new era of Generative AI. As the number of model parameters reaches to hundreds of billions, their deployment incurs prohibitive inference costs and high latency in real-world scenarios. This tutorial offers a comprehensive discussion on complementary inference optimization techniques using AI accelerators.
  arXiv  Detail & Related papers  (2024-07-12T09:24:34Z)
• Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training [32.154166415680066]
  Methods like distillation, compression or quantization help leverage the highly performant large models to induce smaller performant ones. This paper explores the hypothesis that a single training run can simultaneously train a larger model for performance and derive a smaller model for deployment.
  arXiv  Detail & Related papers  (2024-02-07T17:07:41Z)
• Decentralized Training of Foundation Models in Heterogeneous Environments [77.47261769795992]
  Training foundation models, such as GPT-3 and PaLM, can be extremely expensive. We present the first study of training large foundation models with model parallelism in a decentralized regime over a heterogeneous network.
  arXiv  Detail & Related papers  (2022-06-02T20:19:51Z)
• Concepts and Algorithms for Agent-based Decentralized and Integrated Scheduling of Production and Auxiliary Processes [78.120734120667]
  This paper describes an agent-based decentralized and integrated scheduling approach. Part of the requirements is to develop a linearly scaling communication architecture. The approach is explained using an example based on industrial requirements.
  arXiv  Detail & Related papers  (2022-05-06T18:44:29Z)
• Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning [55.198301429316125]
  Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increasing applications and data amounts, distributed learning is a promising emerging paradigm since it is often impractical or inefficient to share/aggregate data. This paper studies the problem of training an ML model over decentralized systems, where data are distributed over many user devices.
  arXiv  Detail & Related papers  (2022-02-07T15:04:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.