SENTRY: Drift-Aware Validation Placement for AI Services in the Cloud-Edge Continuum

Publication at a glance

• Paper: SENTRY: Drift-Aware Validation Placement for AI Services in the Cloud-Edge Continuum
• Authors: Nariman Mani, Xiaozhou Li, Shakthi Weerasinghe, and Tomas Cerny
• Status: Submitted to the Cloud-Edge Continuum Workshop (CEC 2026), held with IEEE ICNP 2026
• Workshop date and location: October 5, 2026, Tempe, Arizona, USA
• Workshop: Cloud-Edge Continuum Workshop 2026

The research problem

AI services deployed across cloud and edge environments face a moving target. Input distributions and operating conditions can drift, while bandwidth, latency, compute capacity, privacy constraints, and connectivity differ by location. Validation that always runs in one fixed place can respond too slowly, consume unnecessary resources, or miss important local behavior.

The challenge is to decide where validation should happen as conditions change: close to an edge workload, in a more capable cloud environment, or across both.

The paper's central contribution

SENTRY investigates drift-aware validation placement for AI services in the cloud-edge continuum. Its core idea is that evidence of drift should inform not only whether a service needs validation, but also where that validation can be performed effectively under current system constraints.

This connects AI quality monitoring with distributed-systems resource decisions. Validation becomes an adaptive operational capability rather than a fixed stage that occurs only before deployment.

How the approach works

The high-level workflow links three concerns:

1. Drift awareness observes changes in the data or behavior relevant to an AI service.
2. Placement reasoning considers cloud-edge constraints such as latency, resources, and communication cost.
3. Validation execution places the appropriate checks where they can provide timely, useful evidence.

By reasoning across these concerns, a system can react to changing conditions without assuming that either the cloud or edge is always the best validation location.

Why this work matters

Cloud-edge AI systems must balance responsiveness with assurance. Local validation may reduce latency and preserve data locality, while cloud validation may provide stronger compute capacity and broader context. Drift makes that tradeoff dynamic.

The work is relevant to continuous validation, MLOps, edge intelligence, adaptive monitoring, and dependable AI services operating across distributed infrastructure.

Scope and responsible interpretation

Drift signals do not automatically prove that a model is unsafe or incorrect. Detectors can produce false alarms, resource measurements can become stale, and placement decisions can carry privacy and reliability consequences. A production implementation needs explicit policies, secure telemetry, confidence thresholds, audit trails, and safe fallback behavior.

Readers should consult the paper, if accepted and published, for the exact method, assumptions, evaluation design, and results.

Publication status and citation

As of August 6, 2026, this paper is submitted, not formally accepted. The workshop lists August 11, 2026 as its acceptance-notification date. No DOI, page numbers, volume, ISBN, or IEEE Xplore record should be inferred before formal publication metadata is assigned.

Current reference:

N. Mani, X. Li, S. Weerasinghe, and T. Cerny, “SENTRY: Drift-Aware Validation Placement for AI Services in the Cloud-Edge Continuum.” Submitted to the Cloud-Edge Continuum Workshop (CEC 2026), held with IEEE ICNP 2026.

Visit the CEC 2026 workshop site for the workshop schedule and current publication information.

Related research on this weblog

• Browse the complete Nariman Mani publications list.
• Explore multi-agent change impact analysis and test optimization.
• Read about layers of AI, from classical to agentic systems.