[Paper] RetryGuard: Preventing Self-Inflicted Retry Storms in Cloud Microservices Applications

Source: arXiv - 2511.23278v1

Overview

Modern cloud applications stitch together dozens—or even hundreds—of microservices that scale independently. While this architecture brings flexibility, the default “retry‑on‑failure” logic that many services inherit can backfire, creating self‑inflicted “retry storms” that waste compute cycles and inflate cloud bills. The paper RetryGuard: Preventing Self‑Inflicted Retry Storms in Cloud Microservices Applications proposes a distributed control plane that intelligently throttles retries, turning a costly denial‑of‑wallet scenario into a manageable, cost‑effective operation.

Key Contributions

• RetryGuard framework: A lightweight, language‑agnostic controller that enforces per‑service retry policies across a microservice graph.
• Analytic model: Derives a closed‑form relationship among retry rate, request throughput, induced latency, and monetary cost, enabling real‑time policy decisions.
• Empirical validation: Benchmarks on AWS (EC2, Lambda) and a Kubernetes‑Istio deployment show up to 45 % reduction in CPU usage and 30 % lower cloud spend versus native retry mechanisms.
• Scalability proof: Demonstrates that RetryGuard’s decision engine scales linearly with the number of services, handling >10 k concurrent requests with sub‑millisecond overhead.
• Open‑source prototype: The authors release a minimal implementation (≈200 LOC) that can be dropped into existing service meshes or sidecar proxies.

Methodology

1. Problem modeling – The authors first formalize a microservice topology as a directed graph where edges represent request flows. They then model retries as a feedback loop that amplifies traffic on upstream nodes.
2. Cost‑throughput analysis – Using queueing theory, they derive an expression linking the retry probability p, the service’s processing rate μ, and the expected cost C (CPU time, memory, network I/O).
3. Policy engine – RetryGuard runs a distributed consensus protocol (based on Raft) to share state (current load, error rates) among services. Each node computes a local “retry budget” from the analytic model and enforces it via a sidecar proxy (Envoy/Envoy‑based).
4. Experimental setup – Two testbeds were built: (a) a synthetic microservice chain on AWS (EC2 + Lambda) with configurable failure injection; (b) a real‑world e‑commerce workload deployed on a Kubernetes cluster with Istio. The authors compared three baselines: (i) no retries, (ii) exponential backoff (AWS default), and (iii) aggressive exponential backoff (AWS advanced).
5. Metrics – They measured CPU cycles, memory footprint, request latency, and total billable usage over 24‑hour runs under varying traffic spikes.

Results & Findings

• Storm mitigation: When a downstream service failed at 30 % error rate, the naive exponential backoff caused a 3× traffic surge upstream. RetryGuard capped the retry rate, flattening the surge to <1.2×.
• Latency impact: Because fewer redundant requests entered the system, end‑to‑end latency actually improved despite occasional back‑off delays.
• Scalability: Adding 20 more services increased the decision‑making latency from 0.8 ms to 1.3 ms—well within typical request budgets.

Practical Implications

• Cost control for DevOps – Teams can embed RetryGuard as a sidecar in their service mesh to automatically enforce cost‑aware retry limits, turning a “set‑and‑forget” retry policy into a dynamic, budget‑friendly guardrail.
• Reliability engineering – By preventing retry amplification, services experience fewer cascading failures, simplifying incident response and reducing mean‑time‑to‑recovery (MTTR).
• SLA compliance – Lower latency variance helps meet strict Service Level Agreements, especially for latency‑sensitive APIs (e.g., payment gateways).
• Cloud‑agnostic deployment – Because RetryGuard works at the network proxy layer, it can be used on AWS, GCP, Azure, or on‑prem Kubernetes clusters without code changes.
• Developer ergonomics – No need to rewrite business logic; developers keep their existing retry libraries while RetryGuard silently caps the effective retry count based on real‑time load signals.

Limitations & Future Work

• Model assumptions – The analytic model presumes Poisson arrival patterns and homogeneous request costs; workloads with heavy‑tailed distributions may need calibration.
• State synchronization overhead – In extremely large meshes (>50 k services), the Raft‑based consensus could become a bottleneck; the authors suggest hierarchical controllers as a remedy.
• Security considerations – The current prototype does not encrypt inter‑node state exchanges, which could be a concern in multi‑tenant environments.
• Future directions – Extending the framework to incorporate adaptive learning (e.g., reinforcement‑learning‑based retry budgeting), integrating with serverless platforms beyond AWS Lambda, and open‑sourcing a full‑featured Istio plugin.

Authors

• Jhonatan Tavori
• Anat Bremler-Barr
• Hanoch Levy
• Ofek Lavi

Paper Information

• arXiv ID: 2511.23278v1
• Categories: cs.NI, cs.CR, cs.DC
• Published: November 28, 2025
• PDF: Download PDF