TY - JOUR
T1 - End-to-end energy models for Edge Cloud-based IoT platforms
T2 - Application to data stream analysis in IoT
AU - Li, Yunbo
AU - Orgerie, Anne Cécile
AU - Rodero, Ivan
AU - Amersho, Betsegaw Lemma
AU - Parashar, Manish
AU - Menaud, Jean Marc
N1 - Funding Information:
This work has received a French state support granted to the CominLabs excellence laboratory and managed by the National Research Agency in the “Investing for the Future” program under reference Nb. ANR-10-LABX-07-01. The research presented in this work is supported in part by National Science Foundation (NSF) via grants numbers ACI-1464317 , ACI-1339036 , ACI-1310283 , and CNS-1305375 . We thank Yifu Tang for numerous fruitful discussions.
Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2018/10
Y1 - 2018/10
N2 - Internet of Things (IoT) is bringing an increasing number of connected devices that have a direct impact on the growth of data and energy-hungry services. These services are relying on Cloud infrastructures for storage and computing capabilities, transforming their architecture into more a distributed one based on edge facilities provided by Internet Service Providers (ISP). Yet, between the IoT device, communication network and Cloud infrastructure, it is unclear which part is the largest in terms of energy consumption. In this paper, we provide end-to-end energy models for Edge Cloud-based IoT platforms. These models are applied to a concrete scenario: data stream analysis produced by cameras embedded on vehicles. The validation combines measurements on real test-beds running the targeted application and simulations on well-known simulators for studying the scaling-up with an increasing number of IoT devices. Our results show that, for our scenario, the edge Cloud part embedding the computing resources consumes 3 times more than the IoT part comprising the IoT devices and the wireless access point.
AB - Internet of Things (IoT) is bringing an increasing number of connected devices that have a direct impact on the growth of data and energy-hungry services. These services are relying on Cloud infrastructures for storage and computing capabilities, transforming their architecture into more a distributed one based on edge facilities provided by Internet Service Providers (ISP). Yet, between the IoT device, communication network and Cloud infrastructure, it is unclear which part is the largest in terms of energy consumption. In this paper, we provide end-to-end energy models for Edge Cloud-based IoT platforms. These models are applied to a concrete scenario: data stream analysis produced by cameras embedded on vehicles. The validation combines measurements on real test-beds running the targeted application and simulations on well-known simulators for studying the scaling-up with an increasing number of IoT devices. Our results show that, for our scenario, the edge Cloud part embedding the computing resources consumes 3 times more than the IoT part comprising the IoT devices and the wireless access point.
KW - Data stream analysis
KW - Edge Cloud computing
KW - End-to-end energy model
KW - Energy-efficiency
KW - IoT
UR - http://www.scopus.com/inward/record.url?scp=85039909917&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85039909917&partnerID=8YFLogxK
U2 - 10.1016/j.future.2017.12.048
DO - 10.1016/j.future.2017.12.048
M3 - Article
AN - SCOPUS:85039909917
SN - 0167-739X
VL - 87
SP - 667
EP - 678
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -