deTector: a Topology-aware Monitoring System for Data Center Networks

Troubleshooting network performance issues is a challenging task especially in large-scale data center networks. This paper presents deTector, a network monitoring system that is able to detect and localize network failures (manifested mainly by packet losses) accurately in near real time while minimizing the monitoring overhead. deTector achieves this goal by tightly coupling detection and localization and carefully selecting probe paths so that packet losses can be localized only according to end-to-end observations without the help of additional tools (e.g., tracert). In particular, we quantify the desirable properties of the matrix of probe paths, i.e., coverage and identifiability, and leverage an efficient greedy algorithm with a good approximation ratio and fast speed to select probe paths. We also propose a loss localization method according to loss patterns in a data center network. Our algorithm analysis, experimental evaluation on a Fattree testbed and supplementary large-scale simulation validate the scalability, feasibility and effectiveness of deTector.published_or_final_versio

In-Network Outlier Detection in Wireless Sensor Networks

To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy usage,(3) only uses single hop communication thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance using simulation with real sensor data streams. Our results demonstrate that our approach is accurate and imposes a reasonable communication load and level of power consumption.Comment: Extended version of a paper appearing in the Int'l Conference on Distributed Computing Systems 200

MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features

In this work, we tackle the problem of instance segmentation, the task of simultaneously solving object detection and semantic segmentation. Towards this goal, we present a model, called MaskLab, which produces three outputs: box detection, semantic segmentation, and direction prediction. Building on top of the Faster-RCNN object detector, the predicted boxes provide accurate localization of object instances. Within each region of interest, MaskLab performs foreground/background segmentation by combining semantic and direction prediction. Semantic segmentation assists the model in distinguishing between objects of different semantic classes including background, while the direction prediction, estimating each pixel's direction towards its corresponding center, allows separating instances of the same semantic class. Moreover, we explore the effect of incorporating recent successful methods from both segmentation and detection (i.e. atrous convolution and hypercolumn). Our proposed model is evaluated on the COCO instance segmentation benchmark and shows comparable performance with other state-of-art models.Comment: 10 pages including referenc