TY - GEN
T1 - Optimized distributed processing in a vehicular cloud architecture
AU - Behbehani, Fatemah S.
AU - Musa, Mohamed
AU - Elgorashi, Taisir
AU - Elmirghani, J. M.H.
N1 - Funding Information:
The authors would like to acknowledge funding from the Engineering and Physical Sciences Research Council (EPSRC), through INTERNET (EP/H040536/1), STAR (EP/K016873/1) and TOWS (EP/S016570/1) projects. All data are provided in full in the results section of this paper.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - The introduction of cloud data centres has opened new possibilities for the storage and processing of data, augmenting the limited capabilities of peripheral devices. Large data centres tend to be located away from the end users, which increases latency and power consumption in the interconnecting networks. These limitations led to the introduction of edge processing where small-distributed data centres or fog units are located at the edge of the network close to the end user. Vehicles can have substantial processing capabilities, often un-used, in their on-board-units (OBUs). These can be used to augment the network edge processing capabilities. In this paper, we extend our previous work and develop a mixed integer linear programming (MILP) formulation that optimizes the allocation of networking and processing resources to minimize power consumption. Our edge processing architecture includes vehicular processing nodes, edge processing and cloud infrastructure. Furthermore, in this paper our optimization formulation includes delay. Compared to power minimization, our new formulation reduces delay significantly, while resulting in a very limited increase in power consumption.
AB - The introduction of cloud data centres has opened new possibilities for the storage and processing of data, augmenting the limited capabilities of peripheral devices. Large data centres tend to be located away from the end users, which increases latency and power consumption in the interconnecting networks. These limitations led to the introduction of edge processing where small-distributed data centres or fog units are located at the edge of the network close to the end user. Vehicles can have substantial processing capabilities, often un-used, in their on-board-units (OBUs). These can be used to augment the network edge processing capabilities. In this paper, we extend our previous work and develop a mixed integer linear programming (MILP) formulation that optimizes the allocation of networking and processing resources to minimize power consumption. Our edge processing architecture includes vehicular processing nodes, edge processing and cloud infrastructure. Furthermore, in this paper our optimization formulation includes delay. Compared to power minimization, our new formulation reduces delay significantly, while resulting in a very limited increase in power consumption.
KW - Delay
KW - Distributed processing
KW - Edge nodes
KW - Energy consumption
KW - Vehicular networks
UR - http://www.scopus.com/inward/record.url?scp=85092456224&partnerID=8YFLogxK
UR - https://icton2020.fbk.eu/home
U2 - 10.1109/ICTON51198.2020.9203472
DO - 10.1109/ICTON51198.2020.9203472
M3 - Conference contribution
AN - SCOPUS:85092456224
SN - 9781728184241
T3 - International Conference on Transparent Optical Networks
SP - 1
EP - 5
BT - 2020 22nd International Conference on Transparent Optical Networks, ICTON 2020
A2 - Prudenzano, Francesco
A2 - Cojocaru, Crina
A2 - Urban, Patryk
A2 - Marciniak, Marian
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - United States
T2 - 22nd International Conference on Transparent Optical Networks, ICTON 2020
Y2 - 19 July 2020 through 23 July 2020
ER -
