TY - GEN
T1 - Distributed analytics in fog computing platforms using tensorflow and kubernetes
AU - Tsai, Pei Hsuan
AU - Hong, Hua Jun
AU - Cheng, An Chieh
AU - Hsu, Cheng Hsin
PY - 2017/11/1
Y1 - 2017/11/1
N2 - Modern Internet-of-Things (IoT) applications produce large amount of data and require powerful analytics approaches, such as Deep Learning to extract useful information. Existing IoT applications transmit the data to resource-rich data centers for analytics. However, it may congest networks, overload data centers, and increase security vulnerability. In this paper, we implement a platform, which integrates resources from data centers (servers) to end devices (IoT devices). We launch distributed analytics applications among the devices without sending everything to the data centers. We analyze challenges to implement such a platform and carefully adopt popular open-source projects to overcome the challenges. We then conduct comprehensive experiments on the implemented platform. The results show: (i) the benefits/limitations of distributed analytics, (ii) the importance of decisions on distributing an application across multiple devices, and (iii) the overhead caused by different components in our platform.
AB - Modern Internet-of-Things (IoT) applications produce large amount of data and require powerful analytics approaches, such as Deep Learning to extract useful information. Existing IoT applications transmit the data to resource-rich data centers for analytics. However, it may congest networks, overload data centers, and increase security vulnerability. In this paper, we implement a platform, which integrates resources from data centers (servers) to end devices (IoT devices). We launch distributed analytics applications among the devices without sending everything to the data centers. We analyze challenges to implement such a platform and carefully adopt popular open-source projects to overcome the challenges. We then conduct comprehensive experiments on the implemented platform. The results show: (i) the benefits/limitations of distributed analytics, (ii) the importance of decisions on distributing an application across multiple devices, and (iii) the overhead caused by different components in our platform.
UR - http://www.scopus.com/inward/record.url?scp=85040552444&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040552444&partnerID=8YFLogxK
U2 - 10.1109/APNOMS.2017.8094194
DO - 10.1109/APNOMS.2017.8094194
M3 - Conference contribution
AN - SCOPUS:85040552444
T3 - 19th Asia-Pacific Network Operations and Management Symposium: Managing a World of Things, APNOMS 2017
SP - 145
EP - 150
BT - 19th Asia-Pacific Network Operations and Management Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th Asia-Pacific Network Operations and Management Symposium, APNOMS 2017
Y2 - 27 September 2017 through 29 September 2017
ER -