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Optimizing Cloudlets for Faster Feedback in LLM-Based Code-Evaluation Systems

Wedi'i Gadw mewn:
Cyhoeddwyd yn:Computers vol. 14, no. 12 (2025), p. 557-571
Prif Awdur: Daniel-Florin, Dosaru
Awduron Eraill: Olteanu Alexandru-Corneliu, Țăpuș Nicolae
Cyhoeddwyd:
MDPI AG
Pynciau:
Object oriented programming
Software
User experience
Computer science
Mathematical models
Optimization techniques
Resource allocation
Automation
Resource management
Workloads
Feedback
Generative artificial intelligence
Simulation
Static code analysis
Large language models
Educational objectives
Pattern analysis
Science education
User satisfaction
Optimization
Teaching assistants
Response time
Resource utilization
Learning
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Crynodeb:This paper addresses the challenge of optimizing cloudlet resource allocation in a code evaluation system. The study models the relationship between system load and response time when users submit code to an online code-evaluation platform, LambdaChecker, which operates a cloudlet-based processing pipeline. The pipeline includes code correctness checks, static analysis, and design-pattern detection using a local Large Language Model (LLM). To optimize the system, we develop a mathematical model and apply it to the LambdaChecker resource management problem. The proposed approach is evaluated using both simulations and real contest data, with a focus on improvements in average response time, resource utilization efficiency, and user satisfaction. The results indicate that adaptive scheduling and workload prediction effectively reduce waiting times without substantially increasing operational costs. Overall, the study suggests that systematic cloudlet optimization can enhance the educational value of automated code evaluation systems by improving responsiveness while preserving sustainable resource usage.
ISSN:2073-431X
DOI:10.3390/computers14120557
Ffynhonnell:Advanced Technologies & Aerospace Database