TY - CHAP
T1 - Energy-Efficient Scheduling
AU - Albers, Susanne
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022
Y1 - 2022
N2 - We review algorithmic techniques for energy conservation in processing environments handling big data sets. Firstly, we address dynamic speed scaling, where processors can run at variable speed/frequency. The goal is to use the speed spectrum of the processors so as to minimize energy consumption while providing a desired service. Here we focus on multi-processor platforms with heterogeneous CPUs. Secondly, we examine power-down mechanisms where idle devices can be transitioned into low-power standby and sleep states. We consider power-down mechanisms in massively parallel systems, where the components have to coordinate their active and idle periods. In particular we focus on data centers with homogeneous as well as heterogeneous servers.
AB - We review algorithmic techniques for energy conservation in processing environments handling big data sets. Firstly, we address dynamic speed scaling, where processors can run at variable speed/frequency. The goal is to use the speed spectrum of the processors so as to minimize energy consumption while providing a desired service. Here we focus on multi-processor platforms with heterogeneous CPUs. Secondly, we examine power-down mechanisms where idle devices can be transitioned into low-power standby and sleep states. We consider power-down mechanisms in massively parallel systems, where the components have to coordinate their active and idle periods. In particular we focus on data centers with homogeneous as well as heterogeneous servers.
KW - Approximation algorithm
KW - Competitive analysis
KW - Dynamic speed scaling
KW - Homogeneous processors
KW - Online algorithm
KW - Polynomial-time algorithm
KW - Power-down mechanisms
KW - Power-heterogeneous processors
UR - http://www.scopus.com/inward/record.url?scp=85147439236&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-21534-6_11
DO - 10.1007/978-3-031-21534-6_11
M3 - Chapter
AN - SCOPUS:85147439236
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 196
EP - 212
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Science and Business Media Deutschland GmbH
ER -
