L
Leana Golubchik
Researcher at University of Southern California
Publications - 165
Citations - 4289
Leana Golubchik is an academic researcher from University of Southern California. The author has contributed to research in topics: Server & The Internet. The author has an hindex of 33, co-authored 157 publications receiving 3944 citations. Previous affiliations of Leana Golubchik include National University of Singapore & University of California, Los Angeles.
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Journal ArticleDOI
Sensor faults: Detection methods and prevalence in real-world datasets
TL;DR: This work explores and characterize four qualitatively different classes of fault detection methods, and finds that time-series-analysis-based methods are more effective for detecting short duration faults than long duration ones, and incur more false positives than the other methods.
Proceedings ArticleDOI
VideoEdge: Processing Camera Streams using Hierarchical Clusters
Chien-Chun Hung,Ganesh Ananthanarayanan,Peter Bodik,Leana Golubchik,Minlan Yu,Paramvir Bahl,Matthai Philipose +6 more
TL;DR: This work proposes VideoEdge, a system that introduces dominant demand to identify the best tradeoff between multiple resources and accuracy, and narrows the search space by identifying a "Pareto band" of promising configurations.
Proceedings ArticleDOI
Data centers power reduction: A two time scale approach for delay tolerant workloads
TL;DR: This work focuses on a stochastic optimization based approach to make distributed routing and server management decisions in the context of large-scale, geographically distributed data centers, which offers significant potential for exploring power cost reductions.
Journal ArticleDOI
Adaptive piggybacking: a novel technique for data sharing in video-on-demand storage servers
TL;DR: A novel approach to data sharing is discussed, termed adaptive piggybacking, which can be used to reduce the aggregate I/O demand on the multimedia storage server and thus reduce latency for servicing new requests.
Proceedings ArticleDOI
Early prediction of software component reliability
TL;DR: This paper develops a software component reliability prediction framework by exploiting architectural models and associated analysis techniques, stochastic modeling approaches, and information sources available early in the development lifecycle to illustrate its utility as an early reliability prediction approach.