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Resource Planning and Optimization in Cloud and 5G Radio Access Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Resource Planning and Optimization in Cloud and 5G Radio Access Networks./
Author:	Wu, Yu.
Description:	1 online resource (101 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 80-08, Section: B.
Contained By:	Dissertations Abstracts International80-08B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10934922click for full text (PQDT)
ISBN:	9780438846883

Resource Planning and Optimization in Cloud and 5G Radio Access Networks.
Wu, Yu.

Resource Planning and Optimization in Cloud and 5G Radio Access Networks. - 1 online resource (101 pages)

Source: Dissertations Abstracts International, Volume: 80-08, Section: B.

Thesis (Ph.D.)--University of California, Davis, 2018.

Includes bibliographical references

As Internet traffic grows rapidly, network resources need to be planned and optimized: (1) to save energy/cost; and (2) to improve transmission efficiency. This dissertation targets these objectives in optical cloud networks and 5G Radio Access Networks. Cloud networks deliver cloud services to end users via integrated systems formed by Data Centers (DCs) and optical core networks that interconnect the DCs. At DC level, this research addresses environmental sustainability by replacing brown energy (produced from polluting sources such as coal, oil, natural gas, etc.) by green energy (produced from renewable sources such as wind farms, solar panels, hydroelectric dams, etc.). In Chapter 2, we formulate a DC-placement problem, and propose solutions to minimize brown energy consumption subject to cost budget. After DCs are in operation, for services, especially content-based services, content redundancy plays an important role in saving energy, as energy use is proportional to storage energy consumption. Typical content redundancy schemes are based on Content Replication (CR), resulting in at least a 100% increase in storage energy consumption. In Chapter 3, we investigate a new redundancy scheme, called Content Fragmentation (CF). CF achieves less storage overhead (thus less storage energy consumption) to guarantee the same content resiliency. But it requires additional energy for content reconstruction and data transport in the core network. To determine which scheme is more energy efficient, we formulate, for both schemes, a content-placement problem and propose solutions for it. Radio Access Networks (RANs) serve as the last-mile connections between Internet and end users. And they face cost issues as well in terms of bandwidth shortage, as carrier infrastructure evolves towards 5G. Targeting the Mobile Fronthaul (MF) (connection between BaseBand processing Unit (BBU) pool and Remote Radio Head (RRH) in 5G), we examine a mature and cost-efficient transport technology --- Ethernet for potential solutions. In Chapter 4, we improve MF transmission efficiency utilizing two techniques: (1) differentiating useful traffic from useless traffic using machine-learning algorithms (traffic classification); and (2) sifting out useless traffic (useless-data sifting) for a Time-Division-Multiplexing-Ethernet-Passive-Optical-Network (TDM-EPON)-based MF architecture. We verify the performance improvement of our proposal by implementing a Sifting-based Hybrid Bandwidth Allocation (SHBA) mechanism incorporating both techniques. In Chapter 5, to save cost while maintaining transmission efficiency, we propose to utilize resource sharing at two levels, namely, network-resource sharing (using a single Ethernet network to carry both MF traffic and background traffic) and Baseband Processing Function (BPF) sharing (making BPFs deployable in between RRHs and BBU pool to pre-process MF traffic and sharable among multiple RRHs). The two-level resource-sharing scheme is implemented in a routing and BPF placement problem. We show in a case study that it outperforms standard Cloud-RAN scheme.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9780438846883Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Mobile FronthaulIndex Terms--Genre/Form:

542853
Electronic books.

Resource Planning and Optimization in Cloud and 5G Radio Access Networks.
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500 $a Source: Dissertations Abstracts International, Volume: 80-08, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Mukherjee, Biswanath.
502 $a Thesis (Ph.D.)--University of California, Davis, 2018.
504 $a Includes bibliographical references
520 $a As Internet traffic grows rapidly, network resources need to be planned and optimized: (1) to save energy/cost; and (2) to improve transmission efficiency. This dissertation targets these objectives in optical cloud networks and 5G Radio Access Networks. Cloud networks deliver cloud services to end users via integrated systems formed by Data Centers (DCs) and optical core networks that interconnect the DCs. At DC level, this research addresses environmental sustainability by replacing brown energy (produced from polluting sources such as coal, oil, natural gas, etc.) by green energy (produced from renewable sources such as wind farms, solar panels, hydroelectric dams, etc.). In Chapter 2, we formulate a DC-placement problem, and propose solutions to minimize brown energy consumption subject to cost budget. After DCs are in operation, for services, especially content-based services, content redundancy plays an important role in saving energy, as energy use is proportional to storage energy consumption. Typical content redundancy schemes are based on Content Replication (CR), resulting in at least a 100% increase in storage energy consumption. In Chapter 3, we investigate a new redundancy scheme, called Content Fragmentation (CF). CF achieves less storage overhead (thus less storage energy consumption) to guarantee the same content resiliency. But it requires additional energy for content reconstruction and data transport in the core network. To determine which scheme is more energy efficient, we formulate, for both schemes, a content-placement problem and propose solutions for it. Radio Access Networks (RANs) serve as the last-mile connections between Internet and end users. And they face cost issues as well in terms of bandwidth shortage, as carrier infrastructure evolves towards 5G. Targeting the Mobile Fronthaul (MF) (connection between BaseBand processing Unit (BBU) pool and Remote Radio Head (RRH) in 5G), we examine a mature and cost-efficient transport technology --- Ethernet for potential solutions. In Chapter 4, we improve MF transmission efficiency utilizing two techniques: (1) differentiating useful traffic from useless traffic using machine-learning algorithms (traffic classification); and (2) sifting out useless traffic (useless-data sifting) for a Time-Division-Multiplexing-Ethernet-Passive-Optical-Network (TDM-EPON)-based MF architecture. We verify the performance improvement of our proposal by implementing a Sifting-based Hybrid Bandwidth Allocation (SHBA) mechanism incorporating both techniques. In Chapter 5, to save cost while maintaining transmission efficiency, we propose to utilize resource sharing at two levels, namely, network-resource sharing (using a single Ethernet network to carry both MF traffic and background traffic) and Baseband Processing Function (BPF) sharing (making BPFs deployable in between RRHs and BBU pool to pre-process MF traffic and sharable among multiple RRHs). The two-level resource-sharing scheme is implemented in a routing and BPF placement problem. We show in a case study that it outperforms standard Cloud-RAN scheme.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 523869
653 $a Mobile Fronthaul
653 $a Optical cloud networks
653 $a Transmission efficiency
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a University of California, Davis. $b Computer Science. $3 1671200
773 0 $t Dissertations Abstracts International $g 80-08B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10934922 $z click for full text (PQDT)