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Record W2056047125 · doi:10.1145/2744196

Runtime Monitoring of Cyber-Physical Systems Under Timing and Memory Constraints

2015· article· en· W2056047125 on OpenAlexaff
Ramy Medhat, Borzoo Bonakdarpour, Deepak Kumar, Sebastian Fischmeister

Bibliographic record

VenueACM Transactions on Embedded Computing Systems · 2015
Typearticle
Languageen
FieldComputer Science
TopicReal-Time Systems Scheduling
Canadian institutionsMcMaster UniversityUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceCyber-physical systemEmbedded systemRuntime verificationJitterMemory footprintReal-time computingDistributed computingOperating systemFormal verification

Abstract

fetched live from OpenAlex

The goal of runtime monitoring is to inspect the well-being of a system by employing a monitor process that reads the state of the system during execution and evaluates a set of properties expressed in some specification language. The main challenge in runtime monitoring is dealing with the costs imposed in terms of resource utilization. In the context of cyber-physical systems, it is crucial for a software monitoring solution to be time predictable to improve scheduling, as well as support composition of monitoring solutions with an overall predictable behavior. Moreover, a small memory footprint is often required in components of cyber-physical systems, especially in deeply embedded systems. In this article, we propose a novel control-theoretic software monitoring solution for coordinating time predictability and memory utilization in runtime monitoring of systems that interact with the physical world. The controllers attempt to reduce monitoring jitter and maximize memory utilization while simultaneously ensuring the soundness of evaluation of properties. For systems where multiple properties are required to be monitored simultaneously, we construct a buffer sharing mechanism in which controllers dynamically share the memory space to negate the effect of bursts of environment actions, thus reducing jitter due to transient high loads. To validate our design choices, we present three case studies: (1) a Bluetooth mobile payment system, which shows a sporadic rate of events during peak hours; (2) a laser beam stabilizer for target tracking, and (3) a monitoring system for air/fuel ratio in a car engine exhaust and the CAM inlet position in the engine’s cylinders. The experimental results of the case studies demonstrate up to 40% improvement in time predictability of the monitoring solution when compared to a basic event-triggered approach. Moreover, memory utilization reaches an average of 90% when using our dynamic buffer resizing mechanism.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.051
GPT teacher head0.294
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2015
Admission routes1
Has abstractyes

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