TY - GEN
T1 - Temporal Task Scheduling for Delay-Constrained Applications in Geo-Distributed Cloud Data Centers
AU - Bi, Jing
AU - Yuan, Haitao
AU - Zhang, Jia
AU - Zhou, Mengchu
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/7
Y1 - 2018/9/7
N2 - A growing number of global companies select Green Cloud Data Centers (GCDCs) to manage their delay-constrained applications. The fast growth of users' tasks dramatically increases the energy consumed by GCDC, e.g., Google. The random nature of tasks brings a big challenge of scheduling tasks of each application with limited infrastructure resources of GCDCs. This work accurately computes a mathematical relation between task service rates and the number of tasks refusal in GCDC. Besides, it proposes a Temporal Task Scheduling (TTS) algorithm investigating the temporal variation in geo-distributed cloud data centers to schedule all tasks within their delay constraints. Furthermore, a novel dynamic hybrid meta-heuristic algorithm is developed for the formulated profit maximization problem, based on genetic simulated annealing and particle swarm optimization. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. Trace-driven simulations demonstrate that larger throughput and profit is achieved than several existing scheduling algorithms.
AB - A growing number of global companies select Green Cloud Data Centers (GCDCs) to manage their delay-constrained applications. The fast growth of users' tasks dramatically increases the energy consumed by GCDC, e.g., Google. The random nature of tasks brings a big challenge of scheduling tasks of each application with limited infrastructure resources of GCDCs. This work accurately computes a mathematical relation between task service rates and the number of tasks refusal in GCDC. Besides, it proposes a Temporal Task Scheduling (TTS) algorithm investigating the temporal variation in geo-distributed cloud data centers to schedule all tasks within their delay constraints. Furthermore, a novel dynamic hybrid meta-heuristic algorithm is developed for the formulated profit maximization problem, based on genetic simulated annealing and particle swarm optimization. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. Trace-driven simulations demonstrate that larger throughput and profit is achieved than several existing scheduling algorithms.
KW - Delay-constrained application
KW - Green cloud data center
KW - Hybrid meta-heuristic optimization
KW - Profit maximization
KW - Temporal task scheduling
UR - https://www.scopus.com/pages/publications/85057505820
U2 - 10.1109/CLOUD.2018.00025
DO - 10.1109/CLOUD.2018.00025
M3 - 会议稿件
AN - SCOPUS:85057505820
T3 - IEEE International Conference on Cloud Computing, CLOUD
SP - 138
EP - 145
BT - Proceedings - 2018 IEEE International Conference on Cloud Computing, CLOUD 2018 - Part of the 2018 IEEE World Congress on Services
PB - IEEE Computer Society
T2 - 11th IEEE International Conference on Cloud Computing, CLOUD 2018
Y2 - 2 July 2018 through 7 July 2018
ER -