GRASP: A Graph-Based Proactive Framework for SLA-breach Prediction in Cloud-Native Microservices (CASCON 2025 - Technical Papers) - CASCON 2025

Mon 10 - Thu 13 November 2025

Who

Sara fehresti, Farhoud Jafari Kaleibar, Marin Litoiu

Track

CASCON 2025 74 Technical Papers

Time Zone

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When

Tue 11 Nov 2025 15:00 - 15:30 at Room 5 - TP:225:135:88:204: Cloud & Microservices

Sara fehresti

York University

Iran

Farhoud Jafari Kaleibar

York University

Canada

Marin Litoiu

York University, Canada

Canada

Time Zone

The program is currently displayed in (GMT-05:00) Eastern Time (US & Canada).

Use conference time zone: (GMT-05:00) Eastern Time (US & Canada)Select other time zone

The GMT offsets shown reflect the offsets at the moment of the conference.

Time Band

By setting a time band, the program will dim events that are outside this time window. This is useful for (virtual) conferences with a continuous program (with repeated sessions).
The time band will also limit the events that are included in the personal iCalendar subscription service.

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Session Program

Tue 11 Nov
Displayed time zone: Eastern Time (US & Canada) change

	15:00 - 16:30	TP:225:135:88:204: Cloud & Microservices74 Technical Papers at Room 5

	15:00 30m Talk		GRASP: A Graph-Based Proactive Framework for SLA-breach Prediction in Cloud-Native MicroservicesTCSE Distinguished Paper Award 74 Technical Papers Sara fehresti York University, Farhoud Jafari Kaleibar York University, Marin Litoiu York University, Canada
	15:30 20m Talk		Key Considerations for Auto-Scaling: Lessons from Benchmark MicroservicesShort-Paper 74 Technical Papers Majid Dashtbani University of Waterloo, Ladan Tahvildari University of Waterloo Pre-print
	15:50 20m Talk		AttentiveDRL: Fair and Efficient GPU Job Scheduling via Dual-Agent RLShort-Paper 74 Technical Papers Yiming Shao York University, Aijun An York University, Hajer Ayadi York University, Hao Zhou IBM, Michael Feiman IBM
	16:10 20m Talk		Anomaly Detection in Time Series Data: A Comparative Study of Time-Series Clustering and Recurring Neural NetworksIndustry 74 Technical Papers Wejdene Haouari York university, Marios-Eleftherios Fokaefs , Michael Harrison IBM, Eugene Kharlamov IBM Canada