arXiv subjects

Nanqinqin Li

Publications and source records attributed to Nanqinqin Li.

2 recordsLinked to original sources

arXiv2026

StorRep: Storage Research Experiment Patterns on Chameleon Cloud and Trovi

Storage experiments are vital to advancing storage research, but creating extensible and reproducible storage artifacts can be a challenging task. Our research has shown that only 1% of SSD simulator-based experiences are packaged and 0.5% of them can be easily reproduced. The lack of such artifacts without proper reproducibility can significantly impede the advancement of storage research. The biggest challenges in these types of experiments are ensuring that we have the correct environment to conduct them and creating extensible experiments that can be built upon in future research. To address this issue, we introduce StorRep, a thorough study that provides six extensible and reproducible storage experiment artifacts that serve as the foundation for further storage research, utilizing the Chameleon infrastructure. Our study offers experiment patterns and guidelines that can help researchers create transparent and dependable storage experiments. We have successfully integrated our methods in several experiments in multiple community and educational events over several years and produced publicly accessible artifacts that can be extended and fully reproduced without any restrictions.

cs.DC↗

arXiv2021

How Low Can You Go? Practical cold-start performance limits in FaaS

Function-as-a-Service (FaaS) has recently emerged as a new cloud computing paradigm. It promises high utilization of data center resources through allocating resources on demand at per-function request granularity. High cold-start overheads, however, have been limiting FaaS systems' such potential. Prior work has recognized that time redundancy exists across different cold function invocations and has proposed varied snapshots that capture the instantaneous execution state so allow for jump-starts through restoration. However, it remains unclear what the cold-start performance limits are as the previous snapshots employ different techniques and aredesigned for different environments. In this paper, we summarize these snapshots from a taxonomic perspective andpresent a model that depicts the cold-start performance fromfirst principles. To approximate the performance limits, we propose a snapshot design SnapFaaS. We evaluate SnapFaaS using real-world FaaS functions. Our empirical results prove SnapFaaS' efficiency. It is 2-10x as fast as prior work for most functions and achieves near-optimal cold-start performance.

cs.DC↗