NSDI '26 - Observability Is Eating Your Cores: Fine-Grained Analysis of Microservice Metrics with... @UsenixOrg

Uploaded June 2026 | Updated September 2026, 3 weeks ago

Observability Is Eating Your Cores: Fine-Grained Analysis of Microservice Metrics with IPU-Hosted Sketches

Alessandro Cornacchia, King Abdullah University of Science and Technology; Theophilus A. Benson, Carnegie Mellon University; Muhammad Bilal and Marco Canini, King Abdullah University of Science and Technology

Observability has become mission-critical for troubleshooting cloud-native technology. However, today's observability fails to meet the demands of cloud-native environments, either resulting in crippling complexity and high costs for collecting and storing huge data volumes, or sacrificing events coverage by sampling at coarse time granularity. We present μView, which stands out from conventional cloud monitors by incorporating a lightweight observability data-plane on Infrastructure Processing Units (IPUs). Our novel architecture leverages the proximity of IPUs to the monitored services to tackle observability bloat. Crucially, μView's data-plane applies streaming data sketching techniques to continuously process and analyze microservice's metrics at fine time resolution, without hurting application performance. We show for several use cases that by anticipating SLO violations μView can help (i) narrow the focus on informative observability data, and (ii) trigger useful signals about service performance, thus enabling timely proactive actions. Our code and artifacts are available at: github.com/sands-lab/uview.

View the full NSDI '26 program at usenix.org/conference/nsdi26/technical-sessions

NSDI 26 - Observability Is Eating Your Cores: Fine-Grained Analysis of Microservice Metrics with...

NSDI 26 - UNUM: A New Framework for Network Control

PEPR 26 - Private AI: Building Trust Through Verifiable Computation

SREcon26 Americas - Building SRE Culture (without SREs, Technically)

$NSDI 26 - Detecting and Diagnosing Errors in Serving Archived Web Pages Detecting and Diagnosing Errors in Serving Archived Web Pages Jingyuan Zhu, University of Michigan; Huanchen Sun and Harsha V. Madhyastha, University of Southern California Community Award Winner! Web archives crawl and save copies of pages from the web, enabling users to interact with web pages in the form they existed in the past. Prior to serving any archived page, an archive rewrites the page’s source so that users’ browsers fetch the page’s resources from the archive, not from the servers which originally hosted them. But, on many modern pages, an archive’s edits to crawled scripts result in a loss of fidelity, i.e., an archived copy fails to accurately mimic the original page even when the archive had crawled all resources on the page. To help the developers of archival systems identify and fix the bugs which result in incorrect rewrites of crawled pages, we present FidEx. First, FidEx enables accurate identification of the pages on which an archive violates fidelity. It does so by tracking and comparing the execution of scripts between when a page is crawled and when its copy is loaded. In comparison to existing methods which compare the two loads using either screenshots or the errors reported by the browser, FidEx reduces the false positive rate from around 70% to less than 10%. Second, on every page on which it identifies a loss of fidelity, FidEx pinpoints which subset of the archive’s edits to the page are erroneous. Leveraging this input to fix bugs in the most widely used archival system, we reduced the fraction of archived pages which violate fidelity from 15% to 9%. View the full NSDI 26 program at https://www.usenix.org/conference/nsdi26/technical-sessions$

NSDI 26 - SYMI: Efficient Mixture-of-Experts Training via Model and Optimizer State Decoupling

PEPR 26 - Vision: Human-as-the-Unit Privacy Management with AI Agents

PEPR 26 - Surfacing Hidden Privacy Risks in Code: Lessons from LLM and Retrieval Assisted Detection

NSDI 26 - The GOODPUT System: A Machine Learning-Driven Optimization Framework for Dynamic...

NSDI 26 - PrvTel: Lightweight Models for Private and Accurate Telemetry Data Retention

NSDI 26 - Count-Based Abstractions for Performance Verification of Contention Points

NSDI 26 - A Systematic Threat Analysis and Practical Attacks on Automated Frequency Coordination