Decentralized and Policy-Aware Serverless Orchestration for the Federated Web
Bibliographic record
Abstract
As Web services and Web-of-Things (WoT) applications increasingly span multiple administrative domains such as clouds, edges, and community-run platforms, centralized serverless orchestration frameworks struggle to handle heterogeneous policies, data governance constraints, and dynamic workloads. This situation prompts a new design space: federated and decentralized scheduling models for serverless computing that do not rely on a single global coordinator. In this paper, we explore an approach in which each administrative domain autonomously manages its own resources and complies with local regulations, while lightweight decentralized protocols enable these domains to cooperatively share load information, offer resource availability hints, and negotiate function placements. We illustrate how a small set of core mechanisms such as metadata exchange protocols, compliance-driven constraints, and trust-aware placement negotiations, could enable serverless functions to be flexibly placed and migrated across a federated ecosystem. Our experimental analysis suggests that decentralized coordination can maintain service-level objectives (SLOs) under diverse conditions, especially under load spikes, and align better with the policy and compliance requirements that characterize a heterogeneous Web environment. This paper charts a path forward for research on federated serverless orchestration, aiming to foster more flexible, scalable, and policy-aware infrastructure at global scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".