TY - JOUR
T1 - Analyzing vertical and horizontal offloading in federated cloud and edge computing systems
AU - Akutsu, Kohei
AU - Phung-Duc, Tuan
AU - Lai, Yuan Cheng
AU - Lin, Ying Dar
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022/3
Y1 - 2022/3
N2 - Mobile Edge Computing architecture is one of the most promising architectures that can satisfy different quality of Services required by various applications. In this paper, we model mobile edge computing architecture with queue-length thresholds at user equipments and edges to determine whether the task is offloaded or not in federated cloud and edge computing systems. We propose two models as vertical default & vertical (VDV) model and vertical default & horizontal shortest (VDHS) model. The former only does vertical offloading, meaning that the edge can offload tasks to the cloud, while the latter does vertical offloading and horizontal offloading, meaning that the edge can offload tasks to other edges. However, it is very difficult to directly derive the performance metrics in our models, so we approximate them. Based on these approximations, we determine the optimal queue-length thresholds of UEs and edges. Experiment results show that analytical and simulation results match very well. Also VDHS can reduce the mean task sojourn time by 30% at most and increase delay satisfaction ratio by 11% at most compared with VDV.
AB - Mobile Edge Computing architecture is one of the most promising architectures that can satisfy different quality of Services required by various applications. In this paper, we model mobile edge computing architecture with queue-length thresholds at user equipments and edges to determine whether the task is offloaded or not in federated cloud and edge computing systems. We propose two models as vertical default & vertical (VDV) model and vertical default & horizontal shortest (VDHS) model. The former only does vertical offloading, meaning that the edge can offload tasks to the cloud, while the latter does vertical offloading and horizontal offloading, meaning that the edge can offload tasks to other edges. However, it is very difficult to directly derive the performance metrics in our models, so we approximate them. Based on these approximations, we determine the optimal queue-length thresholds of UEs and edges. Experiment results show that analytical and simulation results match very well. Also VDHS can reduce the mean task sojourn time by 30% at most and increase delay satisfaction ratio by 11% at most compared with VDV.
KW - Horizontal offloading
KW - Mobile edge computing
KW - Vertical offloading
UR - http://www.scopus.com/inward/record.url?scp=85123122879&partnerID=8YFLogxK
U2 - 10.1007/s11235-021-00864-0
DO - 10.1007/s11235-021-00864-0
M3 - Article
AN - SCOPUS:85123122879
SN - 1018-4864
VL - 79
SP - 447
EP - 459
JO - Telecommunication Systems
JF - Telecommunication Systems
IS - 3
ER -