Yuanli Wang, Prateek Jain, Lei Huang, Le Xu, Vasiliki Kalavri
Dataflow streaming systems, like Apache Flink and Spark Streaming, are integral to modern business analytics, providing real-time insights across industries. However, for emerging hybrid edge-cloud analytics pipelines, neither processing everything in the cloud nor static edge offloading can reliably sustain the target rate under dynamic WAN conditions and limited edge resources. In this paper, we explore how these datacenter-centric platforms can be extended to support such hybrid analytics pipelines. Our goal is to maintain full transparency for user applications, while retaining built-in fault tolerance, out-of-order processing, and existing ecosystem integrations. Achieving this is challenging because existing dataflow engines assume homogeneous resources, rely on static task placement, and do not support adaptation under dynamic network and edge conditions. We introduce Sluice, an adaptive data stream processing system for hybrid clouds, compatible with dataflow streaming engines. Sluice's key contributions are: (i) an automatic query rewriter that decomposes applications into independent, fault-tolerant segments, and (ii) a live routing mechanism that shifts computations between edge and cloud without pausing execution while preserving event-time semantics. We implement Sluice on Apache Flink and evaluate it on a physical testbed with Raspberry Pi devices, as well as on a large simulated testbed of 100 VMs. Across five real-world applications, our results show that Sluice seamlessly shifts computations between edge and cloud, while consistently achieving higher throughput than baseline systems.
Gemini Team, Petko Georgiev, Ving Ian Lei, Ryan Burnell, Libin Bai, Anmol Gulati, Garrett Tanzer, Damien Vincent 等 1137 位作者
In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.