Research topic

Predictive Monitoring Research

This topic page groups Naser Ezzati-Jivan research papers related to predictive monitoring. Each linked record provides the paper's problem, method, findings, limitations, keywords, and authoritative source links.

Related search terms: predictive monitoring

4 papers in this topic, ordered newest first. The detailed paper records contain the evidence-grounded methods, tools, datasets, findings, and citation guidance.

Selected papers

2025 · ACM/SPEC International Conference on Performance Engineering (ICPE)

Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures

Ghazal Khodabandeh, Alireza Ezaz, Majid Babaei, Naser Ezzati-Jivan

The paper applies graph attention networks to predict future interactions in microservice call graphs, supporting proactive monitoring.

Keywords: microservice call graphs · link prediction · graph attention networks · temporal segmentation · negative sampling

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2024 · 2024 IEEE International Conference on Big Data (BigData)

Assessing Predictive Models for Energy Consumption Across Varied Software Environments

Tong Zhang, Sarwat Islam Dipanzan, Leila Tahmooresnejad, Naser Ezzati-Jivan

The paper evaluates whether software-energy predictors transfer across applications when they use hardware-performance and operating-system event representations rather than application-specific measurements alone.

Keywords: software energy consumption · predictive models · energy efficiency · software environments · IEEE Big Data 2024

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2024 · 2024 IEEE International Conference on Collaborative Advances in Software and COmputiNg (CASCON)

MemAdapt: Adaptive Monitoring of Memory Usage Through Irregularly Sampled Data

Pranjal Chakraborty, Majid Babaei, Leila Tahmooresnejad, Naser Ezzati-Jivan

MemAdapt forecasts memory behavior under irregular sampling and uses the forecast to choose an adaptive monitoring rate that balances estimation quality with collection overhead.

Keywords: memory monitoring · irregular sampling · adaptive monitoring · time series · memory usage

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2023 · IEEE International Conference on Big Data

AltOOM: A Data-driven Out of Memory Root Cause Identification Strategy

Pranjal Chakraborty, Naser Ezzati-Jivan, Vahid Azhari, François Tetreault

AltOOM combines early memory-pressure forecasting with selective process-level profiling to identify the process most responsible for an impending out-of-memory event.

Keywords: out-of-memory · OOM diagnosis · data-driven RCA · resource analysis · memory pressure forecasting

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