TY - GEN
T1 - PLCD
T2 - 32nd International Conference on Computer Communications and Networks, ICCCN 2023
AU - Mwasinga, Lusungu J.
AU - Raza, Syed M.
AU - Le, Duc Tai
AU - Kim, Moonseong
AU - Choo, Hyunseung
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Service mobility in Multi-access Edge Computing (MEC) paradigm is necessary to provide ultra-Reliable Low Latency Communications for the erratically roaming MEC users. It involves relocation of containerized application services to a strategically selected optimal edge host. During relocation, service containers are unavailable (downtime), resulting in the interruption of ongoing user sessions and increased operational expenses for the network operator. Prolonged service downtime degrades perceived quality of experience for users, and this study handles this problem by proposing a downtime-aware Policy Learning based Capped Downtime (PLCD) service mobility strategy. It exploits Deep Actor-Critic prowess for effectively deciding when and where to relocate a containerized application service while taking user mobility and MEC server resource fluctuations into account. Efficacy of the proposed PLCD strategy is confirmed through simulation experiments, and results indicate over 90% average reduction in service downtime comparing to a baseline scheme.
AB - Service mobility in Multi-access Edge Computing (MEC) paradigm is necessary to provide ultra-Reliable Low Latency Communications for the erratically roaming MEC users. It involves relocation of containerized application services to a strategically selected optimal edge host. During relocation, service containers are unavailable (downtime), resulting in the interruption of ongoing user sessions and increased operational expenses for the network operator. Prolonged service downtime degrades perceived quality of experience for users, and this study handles this problem by proposing a downtime-aware Policy Learning based Capped Downtime (PLCD) service mobility strategy. It exploits Deep Actor-Critic prowess for effectively deciding when and where to relocate a containerized application service while taking user mobility and MEC server resource fluctuations into account. Efficacy of the proposed PLCD strategy is confirmed through simulation experiments, and results indicate over 90% average reduction in service downtime comparing to a baseline scheme.
KW - Actor-Critic
KW - Availability
KW - Deep Policy-Gradients
KW - Deep reinforcement learning
KW - Edge Computing
KW - Service mobility management
UR - https://www.scopus.com/pages/publications/85173583742
U2 - 10.1109/ICCCN58024.2023.10230207
DO - 10.1109/ICCCN58024.2023.10230207
M3 - Conference contribution
AN - SCOPUS:85173583742
T3 - Proceedings - International Conference on Computer Communications and Networks, ICCCN
BT - ICCCN 2023 - 2023 32nd International Conference on Computer Communications and Networks
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 July 2023 through 27 July 2023
ER -