Establishing trust for secure elasticity in edge-cloud microservices
Bibliographic record
Abstract
Platform services are increasingly becoming distributed to improve the availability and latency of Industrial Internet of Things (IIoT) applications. Modern infrastructure services such as Kubernetes have enabled a seamless deployment of these platform services across the distributed edge and cloud subsystems. These infrastructure services support dynamic addition and removal of resources, and thus, they enable the elasticity of the edge-cloud platform services. However, these infrastructure services currently do not have a high-level view of platform services and make elasticity decisions based on low-level configurations provided by the stakeholder. This thesis aims to support trust establishment in the elasticity operations of these edge-cloud platform services. We present the ZETA framework that introduces Zero Trust Architecture (ZTA) secure design paradigm into these elasticity operations. ZETA ensures trusted elasticity of platform services via contextual Gaussian Process Regression (GPR) based trust computation from the ``observed'' and ``service'' knowledge. Moreover, it supports elasticity delegation capabilities through a token-based platform-agnostic interaction model. Finally, ZETA allows the stakeholder to provide custom trust policies, fine-tune the trust algorithm and even extend it. The evaluation of the ZETA framework on multiple real-world scenarios demonstrates its ability to support zero-trust elasticity in variety of operations. Moreover, the encouraging results from the performance evaluation exhibit a low resource utilization and delineate precise resource requirements of ZETA provisioning.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".