Delayed-Dynamic-Selective (DDS) Prediction for Reducing Extreme Tail Latency in Web Search

Author:

Kim Saehoon1,He Yuxiong2,Hwang Seung-won1,Elnikety Sameh2,Choi Seungjin1

Affiliation:

1. POSTECH, Pohang, South Korea

2. Microsoft Research, Redmond, USA

Publisher

ACM

Cited by 62 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. RASK: Range Spatial Keyword Queries on Massive Encrypted Geo-Textual Data;IEEE Transactions on Services Computing;2023-09

2. SketchPolymer: Estimate Per-item Tail Quantile Using One Sketch;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04

3. Many are Better than One: Algorithm Selection for Faster Top-K Retrieval;Information Processing & Management;2023-07

4. An Efficient Scheduler for Task-Parallel Interactive Applications;Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures;2023-06-17

5. DDPC: Automated Data-Driven Power-Performance Controller Design on-the-fly for Latency-sensitive Web Services;Proceedings of the ACM Web Conference 2023;2023-04-30

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