In this paper, we propose an adaptive online energy-aware scheduling algorithm by exploiting the reconfiguration capability of a Virtualized Networked Data Centers (VNetDCs) processing large amount of data in parallel. To achieve energy efficiency in such intensive computing scenarios, a joint balanced provisioning and scaling of the networking-plus-computing resources is required. We propose a scheduler that manages both the incoming workload and the VNetDC infrastructure to minimize the communication-plus-computing energy dissipated by processing incoming traffic under hard real-time constraints on the per-job computing-plus-communication delays. Specifically, our scheduler can distribute the workload among multiple virtual machines (VMs) and can tune the processor frequencies and the network bandwidth. The energy model used in our scheduler is rather sophisticated and takes into account also the internal/external frequency switching energy costs. Our experiments demonstrate that the proposed scheduler guarantees high quality of service to the users respecting the service level agreements. Furthermore, it attains minimum energy consumptions under two real-world operating conditions: a discrete and finite number of CPU frequencies and not negligible VMs reconfiguration costs. Our results confirm that the overall energy savings of data center can be significantly higher with respect to the existing solutions.

An energy-aware scheduling algorithm in DVFS-Enabled Networked Data Centers / Shojafar, Mohammad; Canali, Claudia; Lancellotti, Riccardo; Abolfazli, Saeid. - STAMPA. - 2:(2016), pp. 387-397. (Intervento presentato al convegno 6th International Conference on Cloud Computing and Services Science, CLOSER 2016 tenutosi a Rome Italy nel April 23-25, 2016) [10.5220/0005928903870397].

An energy-aware scheduling algorithm in DVFS-Enabled Networked Data Centers

SHOJAFAR, MOHAMMAD;CANALI, Claudia;LANCELLOTTI, Riccardo;
2016

Abstract

In this paper, we propose an adaptive online energy-aware scheduling algorithm by exploiting the reconfiguration capability of a Virtualized Networked Data Centers (VNetDCs) processing large amount of data in parallel. To achieve energy efficiency in such intensive computing scenarios, a joint balanced provisioning and scaling of the networking-plus-computing resources is required. We propose a scheduler that manages both the incoming workload and the VNetDC infrastructure to minimize the communication-plus-computing energy dissipated by processing incoming traffic under hard real-time constraints on the per-job computing-plus-communication delays. Specifically, our scheduler can distribute the workload among multiple virtual machines (VMs) and can tune the processor frequencies and the network bandwidth. The energy model used in our scheduler is rather sophisticated and takes into account also the internal/external frequency switching energy costs. Our experiments demonstrate that the proposed scheduler guarantees high quality of service to the users respecting the service level agreements. Furthermore, it attains minimum energy consumptions under two real-world operating conditions: a discrete and finite number of CPU frequencies and not negligible VMs reconfiguration costs. Our results confirm that the overall energy savings of data center can be significantly higher with respect to the existing solutions.
2016
6th International Conference on Cloud Computing and Services Science, CLOSER 2016
Rome Italy
April 23-25, 2016
2
387
397
Shojafar, Mohammad; Canali, Claudia; Lancellotti, Riccardo; Abolfazli, Saeid
An energy-aware scheduling algorithm in DVFS-Enabled Networked Data Centers / Shojafar, Mohammad; Canali, Claudia; Lancellotti, Riccardo; Abolfazli, Saeid. - STAMPA. - 2:(2016), pp. 387-397. (Intervento presentato al convegno 6th International Conference on Cloud Computing and Services Science, CLOSER 2016 tenutosi a Rome Italy nel April 23-25, 2016) [10.5220/0005928903870397].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1112635
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