Retry Amplification in Distributed Systems: A Systematic Analysis of Retry Policies and Their Role in Cascading Failures

• URL: http://arxiv.org/abs/2608.25403v1
• Date: Wed, 26 Aug 2026 06:04:03 GMT
• Title: Retry Amplification in Distributed Systems: A Systematic Analysis of Retry Policies and Their Role in Cascading Failures
• Abstract summary: This paper introduces the retry amplification factor (RAF), a metric quantifying the additional request volume that retry policies generate during partial failures.<n>In a study of 200 open-source Python microservice projects, explicit retry logic is detected in 11.5%.<n>We show that budget-constrained retries maintain success rates close to the no-retry baseline while still recovering from transient faults.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retry mechanisms are a standard component of resilient distributed systems, but their collective behavior, when every tier in a call path retries concurrently, is less well understood than the per-client guidance that produced them. This paper introduces the retry amplification factor (RAF), a metric quantifying the additional request volume that retry policies generate during partial failures. In a study of 200 open-source Python microservice projects, explicit retry logic is detected in 11.5%, and an audit of our own false negatives places true prevalence near 41%. Among the projects detected, 60.9% contain at least one configuration without backoff, and after manual verification exactly one of 113 production configurations randomizes its delay. We then evaluate these policies in simulation (n = 100 trials per strategy). Under correlated failures, a naive standard retry policy reduces the success rate from 55.4% to 41.5% relative to performing no retries at all. We catalog five recurring anti-patterns, propose Adaptive Retry Budgeting (ARB), and show that budget-constrained retries maintain success rates close to the no-retry baseline while still recovering from transient faults. These results indicate that retry behavior should be designed as a system-level property rather than configured locally at each call site.

Related papers

• FailForge: Distilling Procedural Competence from Persistent Failures into Code Agents [49.519184792774304]
  FailForge is an agentic framework that converts failed rollouts into training signal.<n>FailForge recovers over 26% of previously failed instances at marginal additional cost.
  arXiv  Detail & Related papers  (2026-08-09T08:22:57Z)
• A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination [51.06539604709775]
  We propose A-SR, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views.<n>A-SR coordinates formula discovery through routing among coordination protocols, an online evaluator-reward role policy, and state-routed process memory.
  arXiv  Detail & Related papers  (2026-08-05T14:01:10Z)
• ARBOR: Online Process Rewards via a Reusable Rubric Buffer for Search Agents [48.80766702702854]
  LLM-based search agents are trained predominantly with outcome-only reward, leaving the search process itself unsupervised.<n>This signal degenerates on outcome-homogeneous groups where all sampled trajectories share the same correctness, yielding zero within-group advantage and no gradient.<n>We propose ARBOR (Adaptive Buffer for Online Reward), a reusable process-reward framework that maintains a rubric memory shared across queries.
  arXiv  Detail & Related papers  (2026-06-02T06:58:54Z)
• Self-Healing Agentic Orchestrators for Reliable Tool-Augmented Large Language Model Systems [0.10210859604701106]
  Self-healing agentic orchestrator treats reliability as a runtime bounded control problem.<n>Self-healing achieves 98.8% task success, compared with 94.5% for retry-only and 93.8% for full replanning.
  arXiv  Detail & Related papers  (2026-05-31T19:27:22Z)
• Accuracy, Stability, and Repeated-Run Reliability of Large Language Models on Deterministic Programming Tasks [0.8699677835130409]
  Run-level pass rate overstates retry-free coverage by up to 17.8 percentage points.<n>We present a repeated-run evaluation protocol with metrics for run-level accuracy, retry-free coverage, and per-problem variability.
  arXiv  Detail & Related papers  (2026-05-30T23:03:05Z)
• Verifier-First Evaluation of Agentic LLMs for Infrastructure-as-Code Generation [0.0]
  Infrastructure-as-Code (IaC) generation from natural language requires satisfying provider schemas, dependency planning, and organizational policy constraints.<n>We present a verifier-first empirical study of seven agentic strategies for Terraform generation evaluated on IaC-Eval v2, a modernized 186-task AWS/Terraform benchmark with Rego v1 policies.
  arXiv  Detail & Related papers  (2026-05-29T12:43:27Z)
• Stochastic Estimation of the Layer-wise Hessian Trace for Monitoring Neural-network Training [45.88028371034407]
  We present a parameter estimator of the trace of the diagonal blocks of the Hessian matrix of the empirical risk of a neural network.<n>The procedure combines the Hutchinson trace estimator with a single Hessian-vector product over the whole parameter vector.<n>We show that correctness under weight sharing requires the layer-wise Hessian to be assembled before the second differentiation.
  arXiv  Detail & Related papers  (2026-05-25T10:24:32Z)
• ANNEAL: Adapting LLM Agents via Governed Symbolic Patch Learning [12.44509691077682]
  We introduce ANNEAL, a neuro-symbolic agent that converts recurring failures into governed symbolic edits of a process knowledge graph.<n>Its core mechanism, Failure-Driven Knowledge Acquisition (FDKA), localizes the responsible operator, synthesizes a typed patch through constrained LLM generation, and validates the proposal.<n>Across four domains and 27 multi-seed runs, ANNEAL is the only evaluated system that commits persistent structural repairs.
  arXiv  Detail & Related papers  (2026-05-04T05:24:03Z)
• Solver-in-the-Loop: MDP-Based Benchmarks for Self-Correction and Behavioral Rationality in Operations Research [19.31559944205485]
  Operations Research practitioners routinely debug infeasible models through an iterative process.<n>We introduce two benchmarks that place the textbfsolver in the evaluation loop<n>We find that domain-specific RLVR training enables an 8B model to surpass frontier APIs.
  arXiv  Detail & Related papers  (2026-01-28T20:02:44Z)
• Rethinking LLM Evaluation: Can We Evaluate LLMs with 200x Less Data? [82.09573568241724]
  EssenceBench is a coarse-to-fine framework utilizing an iterative Genetic Algorithm (GA)<n>Our approach yields superior compression results with lower reconstruction error and markedly higher efficiency.<n>On the HellaSwag benchmark (10K samples), our method preserves the ranking of all models shifting within 5% using 25x fewer samples, and achieves 95% ranking preservation shifting within 5% using only 200x fewer samples.
  arXiv  Detail & Related papers  (2025-10-12T05:38:10Z)
• REBEL: Reinforcement Learning via Regressing Relative Rewards [59.68420022466047]
  We propose REBEL, a minimalist RL algorithm for the era of generative models.<n>In theory, we prove that fundamental RL algorithms like Natural Policy Gradient can be seen as variants of REBEL.<n>We find that REBEL provides a unified approach to language modeling and image generation with stronger or similar performance as PPO and DPO.
  arXiv  Detail & Related papers  (2024-04-25T17:20:45Z)

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.