TY - GEN
T1 - A Dynamic Multi-Resource Management for Edge Computing
AU - Chuang, I. Hsun
AU - Sun, Rong Chen
AU - Tsai, Hsiang Jen
AU - Horng, Mong Fong
AU - Kuo, Yau Hwang
N1 - Funding Information:
ACKNOWLEDGMENT This paper is based partially on work supported by the Ministry of Science and Technology (MOST) of Taiwan, R.O.C., and Southern Taiwan Science Park (STSP), under grant 105-2221-E-006-138-MY2, 106-2221-E-006-244-MY3 and 107-2221-E-006-164-MY2.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - With IoT devices are widely deployed, numerous IoT data are generated and transmitted to cloud servers for further process. As numerous IoT data usually lead to congestion, the edge computing technology is further emerging to alleviate this problem. Generally, the decision that which data are transmitted to edge nodes or cloud servers and which data stay will significantly influence the system performance. Therefore, proposing a practical resource management for edge computing becomes a necessity. However, in IoT environments, not only the data request model is highly dynamic but also multiple types of resource are required in resource allocation. Existing resource managements cannot totally solve these problems. To this end, this paper proposes a Dynamic Multiple Resource Management (DMRM) applying the Multi-Resource Binary Particle Swarm Optimization (MR-BPSO) to allocate multiple resources in dynamic IoT environments. Moreover, three experiments are provided to present the task complete rate in three situations, including the dynamic request, dynamic resource as well as dynamic request and resource. Comparing with other resource managements, the proposed MR-BPSO has better performance, and therefore the DMRM is more applicable in dynamic IoT environments.
AB - With IoT devices are widely deployed, numerous IoT data are generated and transmitted to cloud servers for further process. As numerous IoT data usually lead to congestion, the edge computing technology is further emerging to alleviate this problem. Generally, the decision that which data are transmitted to edge nodes or cloud servers and which data stay will significantly influence the system performance. Therefore, proposing a practical resource management for edge computing becomes a necessity. However, in IoT environments, not only the data request model is highly dynamic but also multiple types of resource are required in resource allocation. Existing resource managements cannot totally solve these problems. To this end, this paper proposes a Dynamic Multiple Resource Management (DMRM) applying the Multi-Resource Binary Particle Swarm Optimization (MR-BPSO) to allocate multiple resources in dynamic IoT environments. Moreover, three experiments are provided to present the task complete rate in three situations, including the dynamic request, dynamic resource as well as dynamic request and resource. Comparing with other resource managements, the proposed MR-BPSO has better performance, and therefore the DMRM is more applicable in dynamic IoT environments.
UR - http://www.scopus.com/inward/record.url?scp=85071722116&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071722116&partnerID=8YFLogxK
U2 - 10.1109/EuCNC.2019.8802051
DO - 10.1109/EuCNC.2019.8802051
M3 - Conference contribution
AN - SCOPUS:85071722116
T3 - 2019 European Conference on Networks and Communications, EuCNC 2019
SP - 379
EP - 383
BT - 2019 European Conference on Networks and Communications, EuCNC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th European Conference on Networks and Communications, EuCNC 2019
Y2 - 18 June 2019 through 21 June 2019
ER -