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Filters: Author is Xiong, Y.  [Clear All Filters]
2018-02-21
Lu, Y., Chen, G., Luo, L., Tan, K., Xiong, Y., Wang, X., Chen, E..  2017.  One more queue is enough: Minimizing flow completion time with explicit priority notification. IEEE INFOCOM 2017 - IEEE Conference on Computer Communications. :1–9.

Ideally, minimizing the flow completion time (FCT) requires millions of priorities supported by the underlying network so that each flow has its unique priority. However, in production datacenters, the available switch priority queues for flow scheduling are very limited (merely 2 or 3). This practical constraint seriously degrades the performance of previous approaches. In this paper, we introduce Explicit Priority Notification (EPN), a novel scheduling mechanism which emulates fine-grained priorities (i.e., desired priorities or DP) using only two switch priority queues. EPN can support various flow scheduling disciplines with or without flow size information. We have implemented EPN on commodity switches and evaluated its performance with both testbed experiments and extensive simulations. Our results show that, with flow size information, EPN achieves comparable FCT as pFabric that requires clean-slate switch hardware. And EPN also outperforms TCP by up to 60.5% if it bins the traffic into two priority queues according to flow size. In information-agnostic setting, EPN outperforms PIAS with two priority queues by up to 37.7%. To the best of our knowledge, EPN is the first system that provides millions of priorities for flow scheduling with commodity switches.

2021-03-01
Tao, J., Xiong, Y., Zhao, S., Xu, Y., Lin, J., Wu, R., Fan, C..  2020.  XAI-Driven Explainable Multi-view Game Cheating Detection. 2020 IEEE Conference on Games (CoG). :144–151.
Online gaming is one of the most successful applications having a large number of players interacting in an online persistent virtual world through the Internet. However, some cheating players gain improper advantages over normal players by using illegal automated plugins which has brought huge harm to game health and player enjoyment. Game industries have been devoting much efforts on cheating detection with multiview data sources and achieved great accuracy improvements by applying artificial intelligence (AI) techniques. However, generating explanations for cheating detection from multiple views still remains a challenging task. To respond to the different purposes of explainability in AI models from different audience profiles, we propose the EMGCD, the first explainable multi-view game cheating detection framework driven by explainable AI (XAI). It combines cheating explainers to cheating classifiers from different views to generate individual, local and global explanations which contributes to the evidence generation, reason generation, model debugging and model compression. The EMGCD has been implemented and deployed in multiple game productions in NetEase Games, achieving remarkable and trustworthy performance. Our framework can also easily generalize to other types of related tasks in online games, such as explainable recommender systems, explainable churn prediction, etc.