TY - GEN
T1 - Delay-sensitive and Priority-aware task offloading for edge computing-assisted healthcare services
AU - Mukherjee, Mithun
AU - Kumar, Vikas
AU - Maity, Dipendu
AU - Matam, Rakesh
AU - Mavromoustakis, Constandinos X.
AU - Zhang, Qi
AU - Mastorakis, George
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - In this paper, we study the priority-aware task data offloading in edge computing-assisted healthcare service provisioning. The edge server aims to provide additional computing resources to the end-users for processing the delay-sensitive tasks. However, at the same time, it becomes a challenging issue when some of the tasks demand lower response time compared to the other tasks. We present a priority-aware task offloading and scheduling strategy that allocates the computing resources to the high-priority tasks. The hard-deadline tasks are processed first. Later, the remaining computing resources are used to tolerate longer average response time for the soft-deadline tasks. Moreover, we derive a lower bound of the average response time for all hard- and soft-deadline tasks. Through extensive simulations, we show that the proposed task scheduling manages to allocate the computing resources of both end-users and edge server to the hard-deadline tasks while scheduling the soft-deadline tasks with low priority.
AB - In this paper, we study the priority-aware task data offloading in edge computing-assisted healthcare service provisioning. The edge server aims to provide additional computing resources to the end-users for processing the delay-sensitive tasks. However, at the same time, it becomes a challenging issue when some of the tasks demand lower response time compared to the other tasks. We present a priority-aware task offloading and scheduling strategy that allocates the computing resources to the high-priority tasks. The hard-deadline tasks are processed first. Later, the remaining computing resources are used to tolerate longer average response time for the soft-deadline tasks. Moreover, we derive a lower bound of the average response time for all hard- and soft-deadline tasks. Through extensive simulations, we show that the proposed task scheduling manages to allocate the computing resources of both end-users and edge server to the hard-deadline tasks while scheduling the soft-deadline tasks with low priority.
KW - Delay-sensitive tasks
KW - E-Health
KW - Edge Computing
KW - Fog computing
KW - Task offloading
UR - http://www.scopus.com/inward/record.url?scp=85100888626&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM42002.2020.9348064
DO - 10.1109/GLOBECOM42002.2020.9348064
M3 - Conference contribution
AN - SCOPUS:85100888626
T3 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
BT - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Global Communications Conference, GLOBECOM 2020
Y2 - 7 December 2020 through 11 December 2020
ER -