Lei Li 0067

dblp:13/7007-67 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2026
0000-0002-0762-3222ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 3 · 3 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021
YearPublicationVenuePosition
2026 UnICom: A Universally High-Performant I/O Completion Mechanism for Modern Computer Systems
Riwei Pan, Yu Liang 0004, Sam H. Noh, Lei Li 0067, Nan Guan, Tei-Wei Kuo, Chun Jason Xue
FAST4
2025 Ariadne: A Hotness-Aware and Size-Adaptive Compressed Swap Technique for Fast Application Relaunch and Reduced CPU Usage on Mobile Devices
abstract
As the memory demands of individual mobile applications continue to grow and the number of concurrently running applications increases, available memory on mobile devices is becoming increasingly scarce. When memory pressure is high, current mobile systems use a RAM-based compressed swap scheme (called ZRAM) to compress unused execution-related data (called anonymous data in Linux) in main memory. This approach avoids swapping data to secondary storage (NAND flash memory) or terminating applications, thereby achieving shorter application relaunch latency.In this paper, we observe that the state-of-the-art ZRAM scheme prolongs relaunch latency and wastes CPU time because it does not differentiate between hot and cold data or leverage different compression chunk sizes and data locality. We make three new observations. First, anonymous data has different levels of hotness. Hot data, used during application relaunch, is usually similar between consecutive relaunches. Second, when compressing the same amount of anonymous data, small-size compression is very fast, while large-size compression achieves a better compression ratio. Third, there is locality in data access during application relaunch.Based on these observations, we propose a hotness-aware and size-adaptive compressed swap scheme, Ariadne, for mobile devices to mitigate relaunch latency and reduce CPU usage. Ariadne incorporates three key techniques. First, a low-overhead hotness-aware data organization scheme aims to quickly identify the hotness of anonymous data without significant overhead. Second, a size-adaptive compression scheme uses different compression chunk sizes based on the data’s hotness level to ensure fast decompression of hot and warm data. Third, a proactive decompression scheme predicts the next set of data to be used and decompresses it in advance, reducing the impact of data swapping back into main memory during application relaunch.We implement and evaluate Ariadne on a commercial smartphone, Google Pixel 7 with the latest Android 14. Our experimental evaluation results show that, on average, Ariadne reduces application relaunch latency by 50% and decreases the CPU usage of compression and decompression procedures by 15% compared to the state-of-the-art compressed swap scheme for mobile devices.
Yu Liang 0004, Aofeng Shen, Chun Jason Xue, Riwei Pan, Haiyu Mao, Nika Mansouri-Ghiasi, Qingcai Jiang, Rakesh Nadig, Lei Li 0067, Rachata Ausavarungnirun, Mohammad Sadrosadati, Onur Mutlu
HPCA9
2025 SolFS: An Operation-Log Versioning File System for Hash-free Efficient Mobile Cloud Backup
Riwei Pan, Yu Liang 0004, Lei Li 0067, Hongchao Du, Tei-Wei Kuo, Chun Jason Xue
USENIX ATC3