TY - GEN
T1 - HeteroEdge
T2 - 4th ACM/IEEE International Conference on Internet of Things Design and Implementation, IoTDI 2019
AU - Zhang, Daniel Yue
AU - Rashid, Tahmid
AU - Li, Xukun
AU - Vance, Nathan
AU - Wang, Dong
N1 - Funding Information:
This research is supported in part by the National Science Foundation under Grant No. CNS-1831669, CBET-1637251, CNS-1566465 and IIS-1447795, Army Research Office under Grant W911NF-17-1-0409, Google 2017 Faculty Research Award. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. We also thank our shepherd Dr. Mirco Musolesi for providing valuable input to our paper.
Publisher Copyright:
© 2019 ACM.
PY - 2019/4/15
Y1 - 2019/4/15
N2 - Social sensing has emerged as a new sensing application paradigm where measurements about the physical world are collected from humans or devices on their behalf. The advent of edge computing pushes the frontier of computation, service, and data along the cloud-to-IoT continuum. The merge of these two technical trends (referred to as Social Sensing based Edge Computing or SSEC) generates a set of new research challenges. One critical issue in SSEC is the heterogeneity of the edge where the edge devices owned by human sensors often have diversified computational power, runtime environments, network interfaces, and hardware equipment. Such heterogeneity poses significant challenges in the resource management of SSEC systems. Examples include masking the pronounced heterogeneity across diverse platforms, allocating interdependent tasks with complex requirements on devices with different resources, and adapting to the dynamic and diversified context of the edge devices. In this paper, we develop a new resource management framework, HeteroEdge, to address the heterogeneity of SSEC by 1) providing a uniform interface to abstract the device details (hardware, operating system, CPU); and 2) effectively allocating the social sensing tasks to the heterogeneous edge devices. We implemented HeteroEdge on a real-world edge computing testbed that consists of heterogeneous edge devices (Jetson TX2, TK1, Raspberry Pi3, and personal computer). Evaluations based on two real-world social sensing applications show that the HeteroEdge achieved up to 42% decrease in end-to-end delay for the application and 22% more energy savings compared to the state-of-the-art baselines.
AB - Social sensing has emerged as a new sensing application paradigm where measurements about the physical world are collected from humans or devices on their behalf. The advent of edge computing pushes the frontier of computation, service, and data along the cloud-to-IoT continuum. The merge of these two technical trends (referred to as Social Sensing based Edge Computing or SSEC) generates a set of new research challenges. One critical issue in SSEC is the heterogeneity of the edge where the edge devices owned by human sensors often have diversified computational power, runtime environments, network interfaces, and hardware equipment. Such heterogeneity poses significant challenges in the resource management of SSEC systems. Examples include masking the pronounced heterogeneity across diverse platforms, allocating interdependent tasks with complex requirements on devices with different resources, and adapting to the dynamic and diversified context of the edge devices. In this paper, we develop a new resource management framework, HeteroEdge, to address the heterogeneity of SSEC by 1) providing a uniform interface to abstract the device details (hardware, operating system, CPU); and 2) effectively allocating the social sensing tasks to the heterogeneous edge devices. We implemented HeteroEdge on a real-world edge computing testbed that consists of heterogeneous edge devices (Jetson TX2, TK1, Raspberry Pi3, and personal computer). Evaluations based on two real-world social sensing applications show that the HeteroEdge achieved up to 42% decrease in end-to-end delay for the application and 22% more energy savings compared to the state-of-the-art baselines.
KW - edge computing
KW - heterogeneity
KW - resource management
KW - social sensing
KW - supply chain
UR - http://www.scopus.com/inward/record.url?scp=85066036909&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85066036909&partnerID=8YFLogxK
U2 - 10.1145/3302505.3310067
DO - 10.1145/3302505.3310067
M3 - Conference contribution
AN - SCOPUS:85066036909
T3 - IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation
SP - 37
EP - 48
BT - IoTDI 2019 - Proceedings of the 2019 Internet of Things Design and Implementation
A2 - Ramachandran, Gowri Sankar
A2 - Ortiz, Jorge
PB - Association for Computing Machinery
Y2 - 15 April 2019 through 18 April 2019
ER -