Ariadne: A Hotness-Aware and Size-Adaptive Compressed Swap Technique for Fast Application Relaunch and Reduced CPU Usage on Mobile Devices

2025-02-18

AI Summary

To address high application cold-start latency and excessive CPU overhead in ZRAM-based swap mechanisms on memory-constrained mobile devices—caused by indiscriminate cold/hot data handling, fixed compression block sizes, and neglect of access locality—this paper proposes a heat-aware, size-adaptive compression swap mechanism. We introduce three key techniques: (1) lightweight multi-level hotness tracking, (2) multi-granularity LZ4 compression with dynamic block-size selection, and (3) predictive pre-decompression modeled on app relaunch behavior. These jointly balance compression ratio and decompression speed. Evaluation on a Pixel 7 platform shows a 50% reduction in average cold-start latency and a 15% decrease in compression/decompression CPU overhead. To the best of our knowledge, this is the first work to jointly integrate heat awareness, adaptive block sizing, and locality-driven predictive pre-decompression into a mobile memory compression swap system.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

Growing application memory demands and concurrent usage are making mobile device memory 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. 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. 1) anonymous data has different levels of hotness. Hot data, used during application relaunch, is usually similar between consecutive relaunches. 2) when compressing the same amount of anonymous data, small-size compression is very fast, while large-size compression achieves a better compression ratio. 3) there is locality in data access during application relaunch. We propose Ariadne, a compressed swap scheme for mobile devices that reduces relaunch latency and CPU usage with three key techniques. 1) a low-overhead hotness-aware data organization scheme aims to quickly identify the hotness of anonymous data without significant overhead. 2) 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. 3) 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. Our experimental evaluation results on Google Pixel 7 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 ZRAM scheme.

Problem

• Reduces application relaunch latency
• Decreases CPU usage in mobile devices
• Improves compressed swap efficiency

Innovation

• Hotness-aware data organization
• Size-adaptive compression scheme
• Proactive decompression technique