Dynamic Resource Provisioning for Energy Efficient Cloud Radio Access Networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number7949048
Pages (from-to)964-974
Journal / PublicationIEEE Transactions on Cloud Computing
Volume7
Issue number4
Online published15 Jun 2017
Publication statusPublished - Oct 2019

Abstract

Energy saving is critical for the cloud radio access networks (C-RANs), which are composed by massive radio access units (RAUs) and energy-intensive computing units (CUs) that host numerous virtual machines (VMs). We attempt to minimize the energy consumption of C-RANs, by leveraging the RAU sleep scheduling and VM consolidation strategies. We formulate the energy saving problem in C-RANs as a joint resource provisioning (JRP) problem of the RAUs and CUs. Since the active RAU selection is coupled with the VM consolidation, the JRP problem shares some similarities with a special bin-packing problem. In this problem, the number of items and the sizes of items are correlated and are both adjustable. No existing method can be used to solve this problem directly. Therefore, we propose an efficient low-complexity algorithm along with a context-aware strategy to dynamically select active RAUs and consolidate VMs to CUs. In this way, we can significantly reduce the energy consumption of C-RANs, while do not incur too much overhead due to VM migrations. Our proposed scheme is practical for a large-size network, and its effectiveness is demonstrated by the simulation results.

Research Area(s)

  • cellular networks, cloud computing, Cloud radio access networks, Copper, Dynamic scheduling, Energy consumption, energy saving, Heuristic algorithms, Radio access networks, Resource management, resource provisioning

Citation Format(s)

Dynamic Resource Provisioning for Energy Efficient Cloud Radio Access Networks. / Yu, Nuo; Song, Zhaohui; Du, Hongwei; Huang, Hejiao; Jia, Xiaohua.

In: IEEE Transactions on Cloud Computing, Vol. 7, No. 4, 7949048, 10.2019, p. 964-974.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review