arXiv subjects

Patrick Crowley

Publications and source records attributed to Patrick Crowley.

6 recordsLinked to original sources

arXiv2026

BACC: Budget-Aware Calibration and Control for Horizontal Autoscaling

Cloud services must continuously adapt replica counts to fluctuating demand while respecting fixed-period reliability budgets. Many horizontal autoscalers either react to instantaneous utilization or provision against a fixed predictive risk target. These policies do not explicitly account for how much of the period-level violation budget has already been consumed, so they can be overly conservative when the budget is healthy and insufficiently conservative when the budget is being depleted. We present BACC, a model-agnostic framework for budget-aware horizontal autoscaling. BACC separates three concerns that are often entangled in prior systems: workload prediction, online uncertainty calibration, and budget-paced capacity control. It wraps an arbitrary forecaster with Adaptive Conformal Inference (ACI) to calibrate workload uncertainty online, then uses a proportional--integral controller to adjust provisioning aggressiveness based on the observed pace of budget consumption. We instantiate BACC for CPU-threshold-based horizontal autoscaling in Kubernetes and evaluate it through trace-driven simulation and cluster replay experiments. Across five Azure Functions traces, three compliance levels, and two forecasting backends, BACC tracks the requested violation target closely, achieving mean absolute compliance gaps of 0.44 and 0.42 percentage points with ARIMA and Chronos, respectively. The Kubernetes experiments further show that the same controller improves CPU-threshold compliance over native HPA under deployment effects such as measurement delay and replica readiness.

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arXiv2025

Time-Series Foundation Models for ISP Traffic Forecasting

Accurate network-traffic forecasting enables proactive capacity planning and anomaly detection in Internet Service Provider (ISP) networks. Recent advances in time-series foundation models (TSFMs) have demonstrated strong zero-shot and few-shot generalization across diverse domains, yet their effectiveness for computer networking remains unexplored. This paper presents a systematic evaluation of a TSFM, IBM's Tiny Time Mixer (TTM), on the CESNET-TimeSeries24 dataset, a 40-week real-world ISP telemetry corpus. We assess TTM under zero-shot and few-shot settings across multiple forecasting horizons (hours to days), aggregation hierarchies (institutions, subnets, IPs), and temporal resolutions (10-minute and hourly). Results show that TTM achieves consistent accuracy (RMSE 0.026-0.057) and stable $R^2$ scores across horizons and context lengths, outperforming or matching fully trained deep learning baselines such as GRU and LSTM. Inference latency remains under 0.05s per 100 points on a single MacBook Pro using CPU-only computation, confirming deployability without dedicated GPU or MPS acceleration. These findings highlight the potential of pretrained TSFMs to enable scalable, efficient, and training-free forecasting for modern network monitoring and management systems.

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arXiv2025

Rethinking HTTP API Rate Limiting: A Client-Side Approach

HTTP underpins modern Internet services, and providers enforce quotas to regulate HTTP API traffic for scalability and reliability. When requests exceed quotas, clients are throttled and must retry. Server-side enforcement protects the service. However, when independent clients' usage counts toward a shared quota, server-only controls are inefficient; clients lack visibility into others' load, causing their retry attempts to potentially fail. Indeed, retry timing is important since each attempt incurs costs and yields no benefit unless admitted. While centralized coordination could address this, practical limitations have led to widespread adoption of simple client-side strategies like exponential backoff. As we show, these simple strategies cause excessive retries and significant costs. We design adaptive client-side mechanisms requiring no central control, relying only on minimal feedback. We present two algorithms: ATB, an offline method deployable via service workers, and AATB, which enhances retry behavior using aggregated telemetry data. Both algorithms infer system congestion to schedule retries. Through emulations with real-world traces and synthetic datasets with up to 100 clients, we demonstrate that our algorithms reduce HTTP 429 errors by up to 97.3% compared to exponential backoff, while the modest increase in completion time is outweighed by the reduction in errors.

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arXiv2025

Enabling Network Policy Enforcement in Service Meshes

Portable service mesh implementations enable Layer 4 to Layer 7 policy enforcement across heterogeneous infrastructures, yet they depend on the underlying network's connectivity and policies. Layer 3 network policies govern IP traffic regardless of whether upper layers authorize the flow. While these policies are integral to security, correct enforcement often requires coordination across multiple teams, and achieving consistent policy behavior across heterogeneous environments is challenging. Studies show that most Kubernetes clusters do not enforce any network policies. We propose integrating Layer 3 network policy enforcement with service meshes to protect data-plane traffic in a portable, infrastructure-agnostic manner. This integration allows developers to define Layer 3-7 policies and to ensure enforcement across any infrastructure. Our solution builds an overlay Layer 3 network and enforces Layer 3 policies by routing traffic through specific policy enforcement points and applying default-deny principles with authorization keys. We prototyped our approach using Kubernetes and Istio and found that it adds less than 1ms of latency while supporting complex policies comparable to native Kubernetes network policies.

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arXiv2024

Hermes: A General-Purpose Proxy-Enabled Networking Architecture

We introduce Hermes, a general-purpose networking architecture that aims to improve service delivery over the Internet. Hermes delegates networking responsibilities from applications and services to proxies and is designed as a portable, adaptable solution to four fundamental challenges of efficient service delivery over the Internet: end-to-end traffic management, backward compatibility, data-plane security and privacy models, and adaptable communication layers. The design centers on an overlay of reconfigurable proxies and HTTP tunneling and proxying techniques, utilizing assisting components to extend proxy functionality when needed. Through prototyping and emulation, we demonstrate that Hermes improves key performance metrics across multiple use cases: it provides backward compatibility through protocol translation and tunneling, improves reliability by delegating retry logic to proxies, enables unified policy-based Layer 3 routing across network segments, and serves as an efficient substrate for future architectures like NDN, facilitating their operation over the Internet. Beyond evaluating Hermes across various use cases, we measured the overhead of Hermes' HTTP tunneling and proxying mechanisms and found it to be modest, typically under 2 ms per proxy pair traversal in an isolated collocated setup. Although the HTTP proxying and tunneling techniques used by Hermes increase single-connection processing overhead, we also show that, with up to 1,000 concurrent requests, proxies can amortize connection setup time and reduce end-to-end latency by utilizing connection pooling and multiplexing.

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arXiv2024

Performance Comparison of HTTP/3 and HTTP/2 with Proxy Integration

This paper systematically evaluates the performance of QUIC/HTTP3 (H3) and TCP/HTTP2 (H2) in proxy-enhanced environments. H3 integrates UDP-based flow-controlled streams, built-in TLS, multiplexing, and connection migration to better support modern web communication. While prior studies show that H3 can outperform or underperform H2 depending on network conditions, the role of proxies and connection migration remains underexplored. We assess a variety of H2 and H3 client implementations across, particularly in lossy networks and proxy setups. Our findings show that proxies can significantly enhance H2 performance, yielding a 90% improvement in single-stream downloads under severe impairments when used with the BBR congestion control algorithm. In contrast, proxies have minimal impact on H3, which maintains consistent performance due to its internal mechanisms. H3 excels under high-loss and high-latency conditions, leveraging connection migration and multiplexing to deliver up to 88.36% improvement in migration scenarios and 81.5% in extreme loss cases. While optimized H2 can match H3 in some settings, H3 is more robust overall, showing less sensitivity to proxies, impairments, and congestion control variations.

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