Large companies need to monitor various metrics (for example, Page Views and Revenue) of their applications and services in real time. At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time. In this paper, we introduce the pipeline and algorithm of our anomaly detection service, which is designed to be accurate, efficient and general. The pipeline consists of three major modules, including data ingestion, experimentation platform and online compute. To tackle the problem of time-series anomaly detection, we propose a novel algorithm based on Spectral Residual (SR) and Convolutional Neural Network (CNN). Our work is the first attempt to borrow the SR model from visual saliency detection domain to time-series anomaly detection. Moreover, we innovatively combine SR and CNN together to improve the performance of SR model. Our approach achieves superior experimental results compared with state-of-the-art baselines on both public datasets and Microsoft production data.

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Abstract-This paper introduces power saving strategies for cooperative wireless communication systems. The described scenario focuses on IP-services over DVB-H networks showing the strength of non-altruistic cooperation between mobile devices. The envisioned cooperation is based on cellular reception of data, which is then shared among mobile devices within each others' proximity over short-range links. As the state-of-the-art, we use Bluetooth technology for the short-range link communication in this cooperative scheme. In this paper, three topology based cooperative algorithms for the short-range link communication are designed. Then numerical results show that a power saving gain of over 50% can be achieved by cooperative networking of three mobile terminals in fully cooperating mode.

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