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Institution

National University of Defense Technology

EducationChangsha, China
About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Radar & Synthetic aperture radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.


Papers
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Journal ArticleDOI
TL;DR: Combining a statistical technique and a fast matrix recovery algorithm, CloudDiag can efficiently pinpoint fine-grained causes of the performance problems, which does not require any domain-specific knowledge to the target system.
Abstract: Performance diagnosis is labor intensive in production cloud computing systems. Such systems typically face many real-world challenges, which the existing diagnosis techniques for such distributed systems cannot effectively solve. An efficient, unsupervised diagnosis tool for locating fine-grained performance anomalies is still lacking in production cloud computing systems. This paper proposes CloudDiag to bridge this gap. Combining a statistical technique and a fast matrix recovery algorithm, CloudDiag can efficiently pinpoint fine-grained causes of the performance problems, which does not require any domain-specific knowledge to the target system. CloudDiag has been applied in a practical production cloud computing systems to diagnose performance problems. We demonstrate the effectiveness of CloudDiag in three real-world case studies.

105 citations

Proceedings ArticleDOI
28 Jul 2013
TL;DR: Within a learning to-rank framework, a wide range of features, such as retweet history, followers status, followers active time and followers interests, are explored, finding that followers who retweeted or mentioned the author's tweets frequently before and have common interests are more likely to be retweeters.
Abstract: An important aspect of communication in Twitter (and other Social Network is message propagation -- people creating posts for others to share. Although there has been work on modelling how tweets in Twitter are propagated (retweeted), an untackled problem has been who will retweet a message. Here we consider the task of finding who will retweet a message posted on Twitter. Within a learning to-rank framework, we explore a wide range of features, such as retweet history, followers status, followers active time and followers interests. We find that followers who retweeted or mentioned the author's tweets frequently before and have common interests are more likely to be retweeters.

105 citations

Journal ArticleDOI
TL;DR: A model to optimize the PM policy in terms of the maintenance related cost jointly considering the mission constraints and the imperfect PM nature is developed and a numerical example is presented to demonstrate the proposed model.

105 citations

Journal ArticleDOI
TL;DR: Experimental results demonstrate that the proposed CPR-based approach is substantially more effective compared to an existing Discrete Fourier Transform (DFT)-based approach - one of the most efficient approaches in detecting LDDoS attacks.

105 citations

Journal ArticleDOI
TL;DR: An asymptotic closed-form allocation rule is derived which is easy to compute and implement and can help provide more insights about the allocation and enhance the simulation efficiency.
Abstract: We develop a new Optimal Computing Budget Allocation (OCBA) approach for the ranking and selection problem with stochastic constraints. The goal is to maximize the probability of correctly selecting the best feasible design within a fixed simulation budget. Based on some approximations, we derive an asymptotic closed-form allocation rule which is easy to compute and implement and can help provide more insights about the allocation. The numerical testing shows that our approach can enhance the simulation efficiency.

105 citations


Authors

Showing all 39659 results

NameH-indexPapersCitations
Rui Zhang1512625107917
Jian Li133286387131
Chi Lin1251313102710
Wei Xu103149249624
Lei Liu98204151163
Xiang Li97147242301
Chang Liu97109939573
Jian Huang97118940362
Tao Wang97272055280
Wei Liu96153842459
Jian Chen96171852917
Wei Wang95354459660
Peng Li95154845198
Jianhong Wu9372636427
Jianhua Zhang9241528085
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
202397
2022468
20212,986
20203,468
20193,695