Abstract — Monitoring global states of an application deployed over distributed nodes becomes prevalent in today’s datacenters. State monitoring requires not only correct monitoring results but also minimum communication cost for efficiency and scalability. Most existing work adopts an instantaneous state monitoring approach, which triggers state alerts whenever a constraint is violated. Such an approach, however, may cause frequent and unnecessary state alerts due to unpredictable monitored value bursts and momentary outliers that are common in large-scale Internet applications. These false alerts may further lead to expensive and problematic counter-measures. To address this issue, we introduce window-based state monitoring in this paper. Window-based state monitoring evaluates whether state violation is continuous within a time window, and thus, gains immunity to short-term value bursts and outliers. Furthermore, we find that exploring the monitoring time window at distributed nodes achieves significant communication savings over instantaneous monitoring. Based on this finding, we develop WISE, a system that efficiently performs WIndow-based StatE monitoring at datacenter-scale. WISE is highlighted with three sets of techniques. First, WISE uses distributed filtering time windows and intelligently avoids global information collecting to achieve communication efficiency, while guaranteeing monitoring correctness at the same time. Second, WISE provides a suite of performance tuning techniques to minimize communication cost based on a sophisticated cost model. Third, WISE also employs a set of novel performance optimization techniques. Extensive experiments over both real world and synthetic traces show that WISE achieves a 50%-90 % reduction in communication cost compared with existing instantaneous monitoring approaches and simple alternative schemes. I.

In recent years, due to the proliferation of sensor networks, there has been a genuine need of researching techniques for sensor data acquisition and management. To this end, a large number of techniques have emerged that advocate model-based sensor data acquisition and management. These techniques use mathematical models for performing various, day-to-day tasks involved in managing sensor data. In this chapter, we survey the state-of-the-art techniques for model-based sensor data acquisition and management. We start by discussing the techniques for acquiring sensor data. We, then, discuss the application of models in sensor data cleaning; followed by a discussion on model-based methods for querying sensor data. Lastly, we survey model-based methods proposed for data compression and synopsis generation.

Query processing in sensor networks is emerging as a frontier area in data management research. This is exemplified by a flurry of research and vision papers in premium database journals and international conferences. While we are still years away from the smart dust vision, there is a consensus that our future will incorporate a plethora of sensing devices that will participate and help us in our daily activities. In the DBSENSE proposal we seek to understand the fundamental principles in designing a scalable data processing infrastructure in support of emerging applications that utilize wireless sensor node technology. We have identified four major tasks that we pursed in order to accomplish this goal. The first involves exploiting in-network processing techniques in order to leverage the large number of nodes available in such networks and reduce unnecessary data movement. The second objective is to explore distributed compression schemes that will be tailored to the types of multi-valued data streams produced by the sensing nodes. The third objective is to explore new query models that are emerging in applications of sensor networks and differ significantly from those considered in traditional information management systems. Finally, we investigate resilient query processing algorithms that can tolerate the amount of dirty data and failures that are frequent in sensor networks deployments. 1.

Abstract In recent years, due to the proliferation of sensor networks, there has been a genuine need of researching techniques for sensor data acquisition and manage-ment. To this end, a large number of techniques have emerged that advocate model-based sensor data acquisition and management. These techniques use mathematical models for performing various, day-to-day tasks involved in man-aging sensor data. In this chapter, we survey the state-of-the-art techniques for model-based sensor data acquisition and management. We start by discussing the techniques for acquiring sensor data. We, then, discuss the application of models in sensor data cleaning; followed by a discussion on model-based meth-2ods for querying sensor data. Lastly, we survey model-based methods proposed for data compression and synopsis generation.

management techniques