TY - GEN
T1 - Energy Efficient Resource Allocation in Federated Fog Computing Networks
AU - Alqahtani, Abdullah M.
AU - Yosuf, Barzan
AU - Mohamed, Sanaa H.
AU - El-Gorashi, Taisir E.H.
AU - Elmirghani, Jaafar M.H.
N1 - Funding Information:
The authors would like to acknowledge funding from the Engineering and Physical Sciences Research Council (EPSRC), INTERNET (EP/H040536/1), STAR (EP/K016873/1) and TOWS (EP/S016570/1) projects. The first author would like to acknowledge the Government of Saudi Arabia and JAZAN University for funding his PhD scholarship. All data are provided in full in the results section of this paper.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - There is a continuous growth in demand for time sensitive applications which has shifted the cloud paradigm from a centralized computing architecture towards distributed heterogeneous computing platforms where resources located at the edge of the network are used to provide cloud-like services. This paradigm is widely known as fog computing. Virtual machines (VMs) have been widely utilized in both paradigms to enhance the network scalability, improve resource utilization, and energy efficiency. Moreover, Passive Optical Networks (PON s) are a technology suited to handling the enormous volumes of data generated in the access network due to their energy efficiency and large bandwidth. In this paper, we utilize a PON to provide the connectivity between multiple distributed fog units to achieve federated (i.e., cooperative) computing units in the access network to serve intensive demands. We propose a mixed integer linear program (MILP) to optimize the VM placement in the federated fog computing units with the objective of minimizing the total power consumption while considering inter-Vmtraffic. The results show a significant power saving as a result of the proposed optimization model by up to 52%, in the VM-allocation compared to a baseline approach that allocates the VM requests while neglecting the power consumption and inter-VMs traffic in the optimization framework.
AB - There is a continuous growth in demand for time sensitive applications which has shifted the cloud paradigm from a centralized computing architecture towards distributed heterogeneous computing platforms where resources located at the edge of the network are used to provide cloud-like services. This paradigm is widely known as fog computing. Virtual machines (VMs) have been widely utilized in both paradigms to enhance the network scalability, improve resource utilization, and energy efficiency. Moreover, Passive Optical Networks (PON s) are a technology suited to handling the enormous volumes of data generated in the access network due to their energy efficiency and large bandwidth. In this paper, we utilize a PON to provide the connectivity between multiple distributed fog units to achieve federated (i.e., cooperative) computing units in the access network to serve intensive demands. We propose a mixed integer linear program (MILP) to optimize the VM placement in the federated fog computing units with the objective of minimizing the total power consumption while considering inter-Vmtraffic. The results show a significant power saving as a result of the proposed optimization model by up to 52%, in the VM-allocation compared to a baseline approach that allocates the VM requests while neglecting the power consumption and inter-VMs traffic in the optimization framework.
KW - Energy Efficiency
KW - Fog Computing
KW - Internet of Things (IoT)
KW - Mixed Integer Linear Programming (MILP)
KW - optimization
KW - Passive Optical Networks (PONs)
KW - VMs placements
UR - http://www.scopus.com/inward/record.url?scp=85125292389&partnerID=8YFLogxK
U2 - 10.1109/CSCN53733.2021.9686117
DO - 10.1109/CSCN53733.2021.9686117
M3 - Conference contribution
AN - SCOPUS:85125292389
SN - 9781665423502
T3 - 2021 IEEE Conference on Standards for Communications and Networking, CSCN 2021
SP - 199
EP - 204
BT - 2021 IEEE Conference on Standards for Communications and Networking, CSCN 2021
A2 - Adelantado , Ferran
A2 - Afifi, Hossam
A2 - Aguero, Ramon
A2 - Alexandropoulos, George
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - United States
T2 - 2021 IEEE Conference on Standards for Communications and Networking, CSCN 2021
Y2 - 15 December 2021 through 17 December 2021
ER -