TaskRouter: A newly designed online data processing framework

Author(s):  
Minhao Gu ◽  
Kejun Zhu ◽  
Fei Li ◽  
Wei Shen
2016 ◽  
Vol 181 ◽  
pp. 139-146 ◽  
Author(s):  
Yingjie Xia ◽  
Jinlong Chen ◽  
Xindai Lu ◽  
Chunhui Wang ◽  
Chao Xu

2009 ◽  
Vol 3 (0) ◽  
pp. 239-258 ◽  
Author(s):  
Roberto Casarin ◽  
Jean-Michel Marin

Author(s):  
Daniel Warneke

In recent years, so-called Infrastructure as a Service (IaaS) clouds have become increasingly popular as a flexible and inexpensive platform for ad-hoc parallel data processing. Major players in the cloud computing space like Amazon EC2 have already recognized this trend and started to create special offers which bundle their compute platform with existing software frameworks for these kinds of applications. However, the data processing frameworks which are currently used in these offers have been designed for static, homogeneous cluster systems and do not support the new features which distinguish the cloud platform. This chapter examines the characteristics of IaaS clouds with special regard to massively-parallel data processing. The author highlights use cases which are currently poorly supported by existing parallel data processing frameworks and explains how a tighter integration between the processing framework and the underlying cloud system can help to lower the monetary processing cost for the cloud customer. As a proof of concept, the author presents the parallel data processing framework Nephele, and compares its cost efficiency against the one of the well-known software Hadoop.


Procedia CIRP ◽  
2019 ◽  
Vol 83 ◽  
pp. 661-664 ◽  
Author(s):  
Yinghao Ye ◽  
Meilin Wang ◽  
Shuhong Yao ◽  
Jarvis N. Jiang ◽  
Qing Liu

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 817 ◽  
Author(s):  
Dan Popescu ◽  
Florin Stoican ◽  
Grigore Stamatescu ◽  
Loretta Ichim ◽  
Cristian Dragana

The growing need for food worldwide requires the development of a high-performance, high-productivity, and sustainable agriculture, which implies the introduction of new technologies into monitoring activities related to control and decision-making. In this regard, this paper presents a hierarchical structure based on the collaboration between unmanned aerial vehicles (UAVs) and federated wireless sensor networks (WSNs) for crop monitoring in precision agriculture. The integration of UAVs with intelligent, ground WSNs, and IoT proved to be a robust and efficient solution for data collection, control, analysis, and decisions in such specialized applications. Key advantages lay in online data collection and relaying to a central monitoring point, while effectively managing network load and latency through optimized UAV trajectories and in situ data processing. Two important aspects of the collaboration were considered: designing the UAV trajectories for efficient data collection and implementing effective data processing algorithms (consensus and symbolic aggregate approximation) at the network level for the transmission of the relevant data. The experiments were carried out at a Romanian research institute where different crops and methods are developed. The results demonstrate that the collaborative UAV–WSN–IoT approach increases the performances in both precision agriculture and ecological agriculture.


2020 ◽  
Vol 12 (17) ◽  
pp. 2797
Author(s):  
Gabriel Vasile

This paper proposes a novel data processing framework dedicated to bedload monitoring in underwater environments. After calibration, by integration the of total energy in the nominal bandwidth, the proposed experimental set-up is able to accurately measure the mass of individual sediments hitting the steel plate. This requires a priori knowledge of the vibration transients in order to match a predefined dictionary. Based on unsupervised hierarchical agglomeration of complex vibration spectra, the proposed algorithms allow accurate localization of the transients corresponding to the shocks created by sediment impacts on a steel plate.


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