This chapter presents an optimization framework to manage green datacenters using multilevel energy reduction techniques in a joint approach. A green datacenter exploits renewable energy sources and active Uninterruptible Power Supply (UPS) units to reduce the energy intake from the grid while improving its Quality of Service (QoS) . At server level, the state-of-the-art correlation-aware Virtual Machines (VMs) consolidation technique allows to maximize server’s energy efficiency. At system level, heterogeneous Energy Storage Systems (ESS) replace standard UPSs, while a dedicated optimization strategy aims at maximizing the lifetime of the battery banks and to reduce the energy bill , considering the load of the servers. Results demonstrate, under different number of VMs in the system, up to 11.6% energy savings, 10.4% improvement of QoS compared to existing correlation-aware VM allocation schemes for datacenters and up to 96% electricity bill savings.
Pahlevan, A., Rossi, M., Pablo, G.&., Del Valle, Brunelli, D., Atienza, D. (2016). Joint Computing and Electric Systems Optimization for Green Datacenters. Netherlands : Springer Netherlands [10.1007/978-94-017-7358-4_35-1].
Joint Computing and Electric Systems Optimization for Green Datacenters
Brunelli, Davide;
2016
Abstract
This chapter presents an optimization framework to manage green datacenters using multilevel energy reduction techniques in a joint approach. A green datacenter exploits renewable energy sources and active Uninterruptible Power Supply (UPS) units to reduce the energy intake from the grid while improving its Quality of Service (QoS) . At server level, the state-of-the-art correlation-aware Virtual Machines (VMs) consolidation technique allows to maximize server’s energy efficiency. At system level, heterogeneous Energy Storage Systems (ESS) replace standard UPSs, while a dedicated optimization strategy aims at maximizing the lifetime of the battery banks and to reduce the energy bill , considering the load of the servers. Results demonstrate, under different number of VMs in the system, up to 11.6% energy savings, 10.4% improvement of QoS compared to existing correlation-aware VM allocation schemes for datacenters and up to 96% electricity bill savings.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
