Large Language Models (LLMs) have become increasingly popular, transforming a wide range of applications across various domains. However, the real-world effectiveness of their query cache systems has not been thoroughly investigated. In this work, we for the first time conducted an analysis on real-world human-to-LLM interaction data, identifying key challenges in existing caching solutions for LLM-based chat services. Our findings reveal that current caching methods fail to leverage semantic connections, leading to inefficient cache performance and extra token costs. To address these issues, we propose SCALM, a new cache architecture that emphasizes semantic analysis and identifies significant cache entries and patterns. We also detail the implementations of the corresponding cache storage and eviction strategies. Our evaluations show that SCALM increases cache hit ratios and reduces operational costs for LLMChat services. Compared with other state-of-the-art solutions in GPTCache, SCALM shows, on average, a relative increase of 63% in cache hit ratio and a relative improvement of 77% in tokens savings.

The rapid expansion of AI technology, driven by high-speed networks and high-performance mobile devices, enables personalized and cross-content recommendations across diverse applications. Recent methods consider a large number of textual content keywords or topics as labels for recommendations, transforming the problem into Extreme Multi-label Learning (XML). However, addressing the XML problem in AI-driven recommendation systems that handle extensive user-generated data while ensuring Quality of Service (QoS) faces two main challenges: significant computational costs and inferior tail label prediction performance. We propose SAT, a semantic-aware framework with a tree architecture that effectively tackles these challenges and demonstrates improved performance compared to well-established approaches.

Streaming videos from resource-constrained front-end devices over networks to resource-rich cloud servers has long been a common practice for surveillance and analytics. Most existing live video analytics (LVA) systems, however, have been built over terrestrial networks, limiting their applications during natural disasters and in remote areas that desperately call for real-time visual data delivery and scene analysis. With the recent advent of space networking, in particular, Low Earth Orbit (LEO) satellite constellations such as Starlink, high-speed truly global Internet access is becoming available and affordable. This paper examines the challenges and potentials of LVA over modern LEO satellite networking (LSN). Using Starlink as the testbed, we have carried out extensive in-the-wild measurements to gain insights into its achievable performance for LVA. The results reveal that the uplink bottleneck in today's LSN, together with the volatile network conditions, can significantly affect the service quality of LVA and necessitate prompt adaptation. We accordingly develop StarStream, a novel LSN-adaptive streaming framework for LVA. At its core, StarStream is empowered by a Transformer-based network performance predictor tailored for LSN and a content-aware configuration optimizer. We discuss a series of key design and implementation issues of StarStream and demonstrate its effectiveness and superiority through trace-driven experiments with real-world network and video processing data.