Sultan Mahmud Sajal

San Jose, CA

I am a Performance Engineer at NetApp, where I work on feature performance engineering and research prototyping for the AI Data Engine (AIDE). Previously, I was a Performance and Capacity Engineer at Meta, where I worked on AI inference and training infrastructure and hardware power budget optimization.

I received my Ph.D. in Computer Science and Engineering from Pennsylvania State University in 2024, advised by Prof. Timothy Zhu and Prof. Bhuvan Urgaonkar. My dissertation, “Improving the Fidelity of Trace-Driven Experiments in Cloud Computing Systems,” introduced a family of trace scaling techniques now deployed in production at Azure.

My research sits at the intersection of cloud systems, performance engineering, and AI infrastructure. I publish at top systems venues — OSDI, EuroSys, ISCA, and ACM TOCS — and my work has earned a Best Paper Runner-Up award at EuroSys 2024.

Research areas: cloud computing · distributed systems · performance engineering · AI/ML infrastructure · systems evaluation methodology

news

Jan 06, 2026	Joined NetApp as a Performance Engineer, working on feature performance engineering and AI Data Engine (AIDE) research.
May 20, 2024	Joined Meta Platforms as a Performance and Capacity Engineer, working on AI inference and training infrastructure.
Apr 22, 2024	TraceUpscaler received the Best Paper Runner-Up Award at EuroSys 2024.
Apr 12, 2024	Defended my Ph.D. dissertation at Penn State: “Improving the Fidelity of Trace-Driven Experiments in Cloud Computing Systems.”
Mar 15, 2023	Kerveros accepted to OSDI 2023. The system is deployed in Azure production.

selected publications

ACM TOCS
TraceScaler: A Framework for Scaling Load in Real-World Traces for System Evaluation

Sultan Mahmud Sajal, Md Salman Estyak, Rubaba Hasan, and 3 more authors

ACM Transactions on Computer Systems, 2025

Invited paper

Abs DOI Bib

Trace-driven evaluation is a cornerstone of systems research, but real-world traces rarely match the load levels needed for stress-testing or capacity planning. TraceScaler is a unified framework that subsumes and extends prior trace scaling techniques (TraceSplitter and TraceUpscaler), providing practitioners a single tool to scale traces to arbitrary load levels while preserving statistical fidelity.
@article{sajal2025tracescaler, title = {TraceScaler: A Framework for Scaling Load in Real-World Traces for System Evaluation}, author = {Sajal, Sultan Mahmud and Estyak, Md Salman and Hasan, Rubaba and Zhu, Timothy and Urgaonkar, Bhuvan and Sen, Siddhartha}, journal = {ACM Transactions on Computer Systems}, year = {2025}, publisher = {ACM}, doi = {10.1145/3729282}, note = {Invited paper} }
EuroSys
TraceUpscaler: Upscaling Traces to Evaluate Systems at High Load

Sultan Mahmud Sajal, Timothy Zhu, Bhuvan Urgaonkar, and 1 more author

In Proceedings of the 19th European Conference on Computer Systems (EuroSys), 2024

Best Paper Runner-Up Abs DOI Bib PDF Slides

Best Paper Runner-Up Award

Evaluating systems under high load requires traces that capture realistic request patterns at scale. TraceUpscaler addresses the challenge of upscaling real-world traces — increasing their load level while preserving the statistical properties of the original trace — enabling more faithful stress-testing and capacity planning without requiring new trace collection campaigns.
@inproceedings{sajal2024traceupscaler, title = {TraceUpscaler: Upscaling Traces to Evaluate Systems at High Load}, author = {Sajal, Sultan Mahmud and Zhu, Timothy and Urgaonkar, Bhuvan and Sen, Siddhartha}, booktitle = {Proceedings of the 19th European Conference on Computer Systems (EuroSys)}, year = {2024}, publisher = {ACM}, doi = {10.1145/3627703.3629566}, }
OSDI
Kerveros: Efficient and Scalable Cloud Admission Control

Sultan Mahmud Sajal, Luke Marshall, Beibin Li, and 8 more authors

In Proceedings of the 17th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2023

Deployed in Azure production

Abs Bib PDF Slides

Cloud admission control must balance service guarantees against cluster utilization, at the scale of millions of requests per second across heterogeneous hardware. Kerveros is an admission control system deployed in Azure production that achieves high utilization while providing strong latency guarantees, using a novel combination of request classification and feedback-driven admission policies.
@inproceedings{sajal2023kerveros, title = {Kerveros: Efficient and Scalable Cloud Admission Control}, author = {Sajal, Sultan Mahmud and Marshall, Luke and Li, Beibin and Zhou, Shandan and Pan, Abhisek and Mellou, Konstantina and Narayanan, Deepak and Zhu, Timothy and Dion, David and Moscibroda, Thomas and Menache, Ishai}, booktitle = {Proceedings of the 17th USENIX Symposium on Operating Systems Design and Implementation (OSDI)}, year = {2023}, publisher = {USENIX Association}, note = {Deployed in Azure production} }
EuroSys
TraceSplitter: A New Paradigm for Downscaling Traces

Sultan Mahmud Sajal, Rubaba Hasan, Timothy Zhu, and 2 more authors

In Proceedings of the 16th European Conference on Computer Systems (EuroSys), 2021

Abs DOI Bib PDF

Trace-driven evaluation often requires running experiments at smaller scale than the original production system, yet existing downscaling approaches distort the statistical properties of traces in ways that undermine experimental validity. TraceSplitter introduces a new paradigm for downscaling cloud workload traces that provably preserves key statistical properties, enabling faithful small-scale experiments.
@inproceedings{sajal2021tracesplitter, title = {TraceSplitter: A New Paradigm for Downscaling Traces}, author = {Sajal, Sultan Mahmud and Hasan, Rubaba and Zhu, Timothy and Urgaonkar, Bhuvan and Sen, Siddhartha}, booktitle = {Proceedings of the 16th European Conference on Computer Systems (EuroSys)}, year = {2021}, publisher = {ACM}, doi = {10.1145/3447786.3456249}, }