TY - GEN
T1 - Energy-Aware Streaming Analytics Job Scheduling for Edge Computing
AU - Trihinas, Demetris
AU - Symeonides, Moysis
AU - Georgiou, Joanna
AU - Pallis, George
AU - Dikaiakos, Marios D.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Energy profiling and optimization are expected to be crucial factors impacting the realisation of the Internet of Things (IoT) as more intelligence is deployed at the network extremes to achieve better response times in the proximity of where data are harvested. To improve the performance of streaming analytics jobs, several schedulers have been designed to tackle key challenges in edge computing realms, including resource heterogeneity and highly volatile network links. However, energy-aware scheduling for streaming analytic jobs is at best, not adequately examined. In this article, we introduce PowerStorm, a scheduler for streaming analytic jobs that is designed to explore trade-offs between performance and energy consumption in geodistributed edge computing settings. We implement our scheduler for Apache Storm and show the scheduler's energy saving capabilities over the Yahoo streaming benchmark with worker nodes featuring heterogeneous power and resource capabilities on both a physical and emulated testbed.
AB - Energy profiling and optimization are expected to be crucial factors impacting the realisation of the Internet of Things (IoT) as more intelligence is deployed at the network extremes to achieve better response times in the proximity of where data are harvested. To improve the performance of streaming analytics jobs, several schedulers have been designed to tackle key challenges in edge computing realms, including resource heterogeneity and highly volatile network links. However, energy-aware scheduling for streaming analytic jobs is at best, not adequately examined. In this article, we introduce PowerStorm, a scheduler for streaming analytic jobs that is designed to explore trade-offs between performance and energy consumption in geodistributed edge computing settings. We implement our scheduler for Apache Storm and show the scheduler's energy saving capabilities over the Yahoo streaming benchmark with worker nodes featuring heterogeneous power and resource capabilities on both a physical and emulated testbed.
KW - Big Data
KW - Energy Profiling
KW - Internet of Things
UR - http://www.scopus.com/inward/record.url?scp=85189621557&partnerID=8YFLogxK
U2 - 10.1109/CloudCom59040.2023.00036
DO - 10.1109/CloudCom59040.2023.00036
M3 - Conference contribution
AN - SCOPUS:85189621557
T3 - Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom
SP - 161
EP - 168
BT - Proceedings - 2023 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023
PB - IEEE Computer Society
T2 - 14th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2023
Y2 - 4 December 2023 through 6 December 2023
ER -