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The ever-increasing traffic demand is pushing network operators to find new cost-efficient solutions towards the deployment of future 5G mobile networks. The network sharing paradigm was explored in the past and partially deployed. Nowadays, advanced mobile network multi-tenancy approaches are increasingly gaining momentum paving the way towards further decreasing Capital Expenditures and Operational Expenditures (CAPEX/OPEX) costs, while enabling new business opportunities. This paper provides an overview of the 3GPP standard evolution from network sharing principles, mechanisms and architectures to future on-demand multi-tenant systems. In particular, it introduces the concept of the 5G Network Slice Broker in 5G systems, which enables mobile virtual network operators, over-the-top providers and industry vertical market players to request and lease resources from infrastructure providers dynamically via signaling means. Finally, it reviews the latest standardization efforts considering remaining open issues for enabling advanced network slicing solutions taking into account the allocation of virtualized network functions based on ETSI NFV, the introduction of shared network functions and flexible service chaining.
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From Network Sharing to Multi-tenancy:
The 5G Network Slice Broker
Konstantinos Samdanis, Xavier Costa-Perez, Vincenzo Sciancalepore
NEC Europe Ltd, Germany
samdanis@neclab.eu, xavier.costa@neclab.eu, vincenzo.sciancalepore@neclab.eu
Abstract
The ever-increasing traffic demand is pushing network operators to find new cost-efficient solutions
towards the deployment of future 5G mobile networks. The network sharing paradigm was explored in
the past and partially deployed. Nowadays, advanced mobile network multi-tenancy approaches are
increasingly gaining momentum paving the way towards further decreasing Capital Expenditures and
Operational Expenditures (CAPEX/OPEX) costs, while enabling new business opportunities. This
paper provides an overview of the 3GPP standard evolution from network sharing principles,
mechanisms and architectures to future on-demand multi-tenant systems. In particular, it introduces
the concept of the 5G Network Slice Broker in 5G systems, which enables mobile virtual network
operators, over-the-top providers and industry vertical market players to request and lease resources
from infrastructure providers dynamically via signaling means. Finally, it reviews the latest
standardization efforts considering remaining open issues for enabling advanced network slicing
solutions taking into account the allocation of virtualized network functions based on ETSI NFV, the
introduction of shared network functions and flexible service chaining.
1. Introduction
Network sharing has evolved from a novel concept a few years back with the arrival of 3G networks to
a fundamental feature of the emerging 5G systems. Mobile operators are facing tremendous traffic
increases with the introduction of smartphones and tablets, especially due to content rich multimedia
and cloud applications, and the upcoming vertical market services in automotive, e-health, etc. [1].
The challenge for mobile operators is to accommodate such traffic volumes without significantly
increasing operational and infrastructure costs. The trend toward network densification for increasing
network capacity and the practice of overprovisioning to accommodate peak demands including future
traffic volumes adds further burden into the operational complexity and cost, diminishing the Return
of Investment (RoI).
Indeed, 50% of the radio access sites carry traffic that yields less than 10% of the revenue [3].
Consequently, there is a need for mobile operators to exploit new revenue sources and break the
traditional business model of a single network infrastructure ownership. Network sharing can recover
up to 20% of operational costs for a typical European mobile network operator and can at least half the
infrastructure cost of passive Radio Access Network (RAN) components, which make up to 50% of
the total network cost [2]. An overview of network sharing CAPEX/OPEX saving on different parts of
the network is depicted in Fig.1, considering the RAN, backhaul and core networks1.
1 Source of CAPEX/OPEX savings: Mobile Network infrastructure sharing Industry Overview & Coleago’s Approach,
Coleago Consulting, Feb. 2015.
Figure 1: Overview of CAPEX/OPEX saving of network sharing
Mobile operators can share network infrastructures accelerating network roll-outs and offer services to
customers with reduced costs. In urban areas network sharing can help avoiding complex and lengthy
processes for site acquisition due to regulation issues, especially in highly populated regions where
dense deployments restrict the available space, while for rural areas sharing can reduce the network
investment payback period. Two different roles can be defined for network sharing solutions:
(i) Infrastructure provider (InP) responsible for the physical network deployment and
maintenance. Mobile Network Operators (MNOs) or third parties that interact with other
“players” but not with end users directly can take the InP role.
(ii) Mobile Virtual Network Operator (MVNO) lacks network infrastructure or has limited
capacity and/or coverage, and leases resources from an existing InP.
Future multi-tenant systems are envisioned to expand the aforementioned roles to also include:
(iii) Over-The-Top (OTT) service providers operating on top of a network infrastructure
belonging to an MNO and based on a pre-defined Service Level Agreement (SLA) set of
requirements.
(iv) Vertical industries exploiting an MNO network infrastructure for services complementary to
the telecommunication industry.
In both cases the allocated network slices can be provided on a permanent basis or on-demand, i.e.
opportunistically or periodically.
This paper provides an overview of the 3GPP standardization activities on network sharing, focusing
on the business requirements, architectures and network management framework. In addition, it
introduces the main enablers for realizing future flexible, on-demand multi-tenant networks. The main
contribution of this paper is the analysis and design of a signaling-based, i.e. with no human
intervention, on-demand multi-tenant network building on the top of the 3GPP network sharing
management architecture. In the core of our proposed on-demand multi-tenant network architecture
lies a logically centralized monitoring and control entity defined as 5G Network Slice Broker
providing admission control for incoming requests (placed by MVNOs, OTTs and Verticals) and
resource assignment by means of an enhancement of the 3GPP network sharing management
architecture interfaces and Service Exposure Capability Function (SECF).
The remaining of this paper is organized as follows. Section 2 presents the fundamental network
sharing scenarios and their corresponding business requirements. Section 3 describes the 3GPP
network sharing standardization efforts and architectures. Section 4 introduces the network sharing
management architecture for supporting mobile virtual operators along with the ongoing
enhancements for supporting vertical industries. Section 5 analyses the proposed 5G Network Slice
Broker architecture. Section 6 presents the 3GPP efforts on evolved network slicing towards the
realization of full multi-tenancy considering open standardization issues. Finally, Section 7 provides
the conclusions.
2. Network Sharing Scenarios and Business Requirements
The adoption of network sharing and multi-tenancy from the business perspective aims to address
different strategic and commercial targets for each participant player. For InPs, network sharing results
in additional revenue sources and thus better return on CAPEX/OPEX investments. MVNOs exploit
network sharing as a mean to enhance service provisioning in regions with low or no network
footprint, where the payback period is estimated greater than the expected business targets.
In general, the adoption of network sharing in mature markets concentrates on increasing RoI and
capacity enhancement. In developing markets network sharing usually focuses on coverage expansion.
A significant aspect that influences MNOs’ decision of whether enabling network sharing is beneficial
for their business relies on the purpose of sharing and on the risk of reducing their competitive
advantage. For instance allowing coverage enhancements of their competitors is sensitive for emerging
mobile markets where coverage is a significant service attribute, but it becomes more relaxed in cases
where QoS provision and service innovation is the key business differentiator. The 3GPP Services
Work Group SA1 specified five main business scenarios for network sharing in [5] summarized here:
Multiple core networks sharing a common RAN: An early scenario considered in 3GPP
Release 99, where operators share RAN elements, but not the spectrum. In this case operators
connect directly to their own dedicated carrier layer in the shared Radio Network Controller
(RNC) in the shared RAN.
Operator collaboration to enhance coverage: In this scenario two or more operators with
individual frequency licenses and respective RANs that cover different parts of a country,
provide together coverage for the entire country.
Sharing coverage on specific regions: In this scenario one operator provides shared coverage
in a specific geographical area, with other operators allowed to use it for their subscribers.
Outside such area, coverage is provided by each operator independently.
Common spectrum sharing: This scenario corresponds to common spectrum RAN sharing
considering the following two variations: i) One operator has a frequency license and shares
the spectrum with other operators, ii) A number of operators decide to pool their allocated
spectrum and share the total.
Multiple RANs share a common core network: In this scenario multiple RANs share a
common core network, with each RAN belonging to different network operator.
Challenging the traditional mobile communication paradigm by considering the evolution towards
multi-tenancy on an open regulation environment provides the opportunity for commercial and
operational separation of the mobile infrastructure from service layers. In this way they can evolve
independently according to different business needs and performance characteristics.
3. Early Network Sharing Standardization and Architectures
In the early GSM and UMTS stage, network sharing support was not included; the mobile network
design was concentrating on a single MNO. 3GPP Rel.99 introduced in the UMTS network the first
generation of network sharing concentrating on simple solutions in terms of the commercial
exploitation, with passive sharing and network roaming being the two main pillars. Passive sharing is
defined as the sharing of site locations or physical supporting infrastructure of radio equipment, such
as masts. Site sharing allows mobile operators to share space and optionally share certain support
equipment such as shelters or power supply, but with separate installations of masts, antennas,
cabinets and backhaul equipment. Such approaches did not gain significant interest from the industry
until the early 2000s.
Figure 2: Passive network sharing
A step further was accomplished with mast sharing, where mobile operators can co-locate their sites
and even share the antenna frame, but still install their own radio equipment, maintaining separate
coverage. An overview of passive network sharing is illustrated in Figure 2 highlighting the main
components. As for network roaming, certain mobile subscribers can use the network of other MNOs
based on contractual agreements without imposing any particular network sharing requirements, so in
that sense it is not exactly a form of infrastructure sharing. With 3GPP Rel-6 (UMTS), Rel-8 (LTE)
and Rel-10 (LTE-A), new requirements were needed to shed the light on the potential of network
sharing.
Active RAN sharing followed the first generation of network sharing, which focused on sharing access
network equipment including base stations, antennas and even mobile backhaul equipment. In active
RAN sharing MNOs can pool spectrum resources, which are shared alongside other RAN equipment
based on fixed, contractual agreements. The interest in sharing the resources dynamically introduced
new requirements, beyond the original RAN sharing concepts, where MNOs share core transmission
equipment, billing platforms and core network equipment.
(a) (b)
Figure 3: 3GPP architectures for network sharing (a) MOCN and (b) GWCN.
3GPP specified two distinct active RAN sharing architectures as illustrated in Figure 3 in the
Architecture Working Group SA2 in Rel.11 – Rel.12 as documented in [4]:
Multi-Operator Core Network (MOCN), where each operator has its own Enhanced Packet
Core (EPC) providing a strict separation amongst the core network and RAN. Shared base
stations, i.e. evolved Node Bs (eNBs), are connected to core network elements of each
different operator, i.e. Mobility Management Entity (MME) and Serving/Packet-Gateway
(S/P-GW), using a separate S1 interface. This enables customization, for example allowing
load balancing policies to be provided within each operator’s core network. MOCN offers
benefits regarding service differentiation and interworking with legacy networks.
Gateway Core Network (GWCN), where operators share additionally the MME; an
approach that further enables cost savings compared to MOCN, but at the price of reduced
flexibility, i.e. restricting mobility for inter-Radio Access Technology (RAT) scenarios and
circuit switching fallback for voice traffic.
In general, MOCN requires a higher investment but is considered to be more flexible, addressing
easier conventional MNOs’ needs. The User Equipment (UE) behavior in both MOCN and GWCN is
identical with resource sharing being transparent. In both cases, UEs can distinguish up to six different
MNOs that share the RAN infrastructure based on broadcast information, i.e. Public Land Mobile
Network (PLMN)-id, and can signal to obtain connectivity or perform a handover irrespective of the
underlying RAN sharing arrangement. Specifically, the S1 interface supports the exchange of PLMN-
ids between eNBs and MMEs in order to assist the selection of the corresponding core network, as
documented in TS 36.413, while the X2 interface supports a similar PLMN-id exchange among
neighboring eNBs for handover purposes, as per TS 36.423. Regarding broadcasting, the Uu interface
supports the PLMN-ids enabling the UEs to perform the network selection as specified in TS 36.331.
4. Incorporating Virtual Operators & Verticals in 3GPP Networks
The 3GPP Telecom Management Working Group SA5 has extended the legacy network management
architecture to accommodate network sharing based on long term contractual agreements [6]. Such
network sharing paradigm considers that an InP, referred to as Master Operator in the 3GPP
terminology, facilitates resource sharing to Participant MVNOs or otherwise Sharing Operators
through the InP network manager system, using the Type 5 interface.
Type 5 interface is established upon an agreement between MNOs to provide connectivity among the
network manager systems across different organizations, e.g. for roaming purposes. The Master
Operator can then forward performance monitoring information through the network manager system,
referred in 3GPP as Master Operator-Network Manager (MO-NM), to the participant Sharing
Operator-Network Manager (SO-NM). For monitoring and configuration operations on network
elements the MO-NM can use:
(i) Type 2 interface or Itf-N between the MO-NM and network element manager. In LTE the
element manager is co-located at the eNB, while in UTRAN it is located on the Master
Operator-Sharing RAN Domain Manager at the RNC. This interface is used for performance
monitoring, reporting and control of network elements to the network manager system.
(ii) Type 1 interface or Itf-B between the Master Operator-Shared RAN Domain Manager and a
NodeB. Typically, the Master Operator-Shared RAN Domain Manager serves a number of
Shared RAN NodeBs. This interface is also used for network management purposes.
Vertical industries and OTT providers, which do not own a network infrastructure, need to interact
with InPs to request network resources and to negotiate SLAs, a process that is achieved by allowing
exposure of the 3GPP service capabilities to third parties. In this way operators are no longer merely
suppliers of communication services, but business enablers. The 3GPP Service Capability Exposure
Function (SCEF) [7] located at the operator trust domain provides a mean to securely expose selected
service capabilities via network Application Programming Interfaces (APIs). The SCEF abstracts
service capabilities related to the communication type, network elements, policy control and network
resource allocation from the underlying 3GPP network. Effectively, such service capability abstraction
can also assist third parties to issue a network resource request towards an InP. The SCEF plays the
role of the mediator between the third party and the 3GPP InP facilitating the following operations:
(i) Authentication/authorization and secure access of third parties to the 3GPP network ensuring
that the InP is under control of the exposed services,
(ii) Charging based on offered service and quality provision,
(iii) QoS provision and SLA monitoring, allowing third parties to request and set service priorities
in a dynamic manner,
(iv) Provision of user context information, e.g., real-time user location, user connection properties,
average data rate, etc., and network status changes to third parties,
(v) Admission control regarding predictable communication patterns, e.g. considering the time
window and traffic volume, pre-schedule communication timing, etc.
Figure 4: Service Capability Exposure Function (SCEF) architecture.
Such operations support the allocation of network resources with customized capabilities considering
the developer’s or third party’s business requirements, SLA policy and service adaptation. Effectively,
this provides the opportunity for network programmability allowing third parties to efficiently use the
retrieved service capability information and optimally exploit the available network resources. Figure
4 illustrates the SCEF architecture showing how third party’s applications can associate with different
APIs receiving a customized set of service capabilities. The network elements and interfaces within
the trusted domain are under the control of the InP, with the SCEF exposing the capabilities of
network entities to the application layer.
5. 5G Network Slice Broker - Architecture
To enhance the existent RAN sharing flexibility, the authors of this paper introduced2 in the 3GPP
Services Working Group SA1 the concept of the on-demand capacity broker [5]. Differently to SCEF,
which exposes service capabilities, the on-demand capacity broker facilitates on-the-fly resource
allocation by allowing a host RAN provider, i.e. InP, to allocate via signaling means an indicated
portion of network capacity for a particular time period to an MVNO, OTT provider or vertical market
player.
In this paper, we build on top of the 3GPP SA5 network sharing management architecture, introducing
a novel concept of capacity broker with a more generic objective, in order to address dynamic resource
sharing scenarios by establishing network slices. A network slice refers to an isolated amount of
network capacity customized to suit best specific service requirements. The proposed capacity broker,
namely 5G Network Slice Broker, can facilitate on-demand resource allocation and perform admission
control based on traffic monitoring and forecasting including mobility based-on a global network
view. In addition, it configures RAN schedulers to either follow a two-layer paradigm, with the higher
layer operating an inter-slice resource allocation and the lower one enabling tenants to customize
scheduling on the allocated spectrum in isolation or configure policies to enable the selection of
resource blocks from a shared pooled spectrum, taking into account the service SLA and the size of
the network slice across the core network.
2 3GPP S1-122194, On-demand Capacity Brokering, TSG-SA WG1 Meeting #59, NEC/Sprint, Aug. 2012.
To accomplish this task, we propose to co-locate the 5G Network Slice Broker at the MO-NM, which
monitors and controls the shared RAN, while interacting with the Sharing Operator Network Manager
(SO-NM). The 5G Network Slice Broker can gain in this way access to network monitoring
measurements such as load and various Key Performance Indicators (KPIs), e.g. mobility
optimization, failures, SLA violations, etc. as well as obtain network infrastructure capabilities
information. In addition, it can receive on-demand network resource requests from MVNOs, via the
Type 5 interface, for allocating network slices based on SLAs. The 5G Network Slice Broker upon
performing the corresponding admission control decisions, it can then take advantage of the existing
MO-NM interfaces, i.e. Itf-N and Itf-B, to configure the desired network slice on specific RAN
network elements.
Besides MVNO requests, the 5G Network Slice Broker can also handle requests with a specified SLA
from a range of vertical industries and OTT providers, through a close interaction with the SCEF or
exploiting the co-location of the SCEF at the MO-NM. The interface of verticals or OTT providers
towards the SCEF is under discussion, with 3GPP adopting APIs developed in other standardization
bodies, e.g. the Open Mobile Alliance (OMA) API focusing on sensor/machine type applications and
the GSM Association (GSMA) Open API designed for application providers. In this way the SCEF
and the corresponding API is not only exposing information about devices, but can also provide
control to vertical industries and OTT providers through the 5G Network Slice Broker and the MO-
NM interfaces, i.e. Itf-N and Itf-B, to allocate the desired SLAs.
Figure 5: 5G Network slice broker- Management architecture
An overview of the proposed 3GPP-compliant network slice broker management architecture is
illustrated in Figure 5, showing the 5G Network Slice Broker and the SCEF co-located at the MO-NM.
The SCEF provides access to OTT providers, e.g. video, voice and social applications, etc., as well as
to vertical industries, e.g. electricity utility, automotive, e-health, etc. A direct connection through the
network managers, i.e. MO-NM and SO-NM, enables various tenants to easily access RAN resources.
Hence, the 5G Network Slice Broker acts as mediator, mapping the SLA requests of multiple tenants
with the physical network resources through the interfaces provided by the MO-NM.
Interestingly, the proposed 5G Network Slice Broker management architecture supports on-demand
resource allocation operations. This can be achieved by enhancing the existing interfaces. In particular,
enhancements should differentiate tenants in order to handle the corresponding data traffic and provide
performance monitoring information towards each participant operator through Type 5, Itf-N and Itf-B
interfaces. To enable this, a tenant identifier, e.g. PLMN-id, can be included in each data packet
corresponding to different slices as well as in performance measurement reports to enable the MO-NM
to provide feedback towards the corresponding SO-NM. Such performance feedback should involve
only the allocated slice resources for privacy and competition reasons. For supporting verticals and
OTT providers the Itf-N ad Itf-B should also be enhanced to distinct these types of tenants by
introducing a corresponding service identifier to each data packet and performance monitoring report.
The Type 5 interface as well as the vertical industries/OTT provider APIs should be extended to
accommodate on-demand network slice requests with a particular SLA and timing requirements. The
Itf-N and Itf-B interfaces should also be extended to carry out the configuration of network slices by
introducing a new type of signaling considering MVNOs and vertical industries/OTT providers. Such
interface enhancement and signaling should contain a set of additional information including:
(i) the amount of resources allocated to a network slice, e.g. physical resources or data rate
(ii) timing, e.g. starting time, duration or periodicity of a request, time window
(iii) the type of resources and QoS, e.g., the radio/core bearer type, prioritization, delay, jitter,
loss
(iv) the size of file to be downloaded or data to be communicated to particular device/user or
application server
(v) service related information, e.g. mobility (stationary, low, medium, high), data offloading
policies, service disruption tolerance
Besides the service characteristics of a network slice, the set of cells which should accommodate the
network slice request is an additional parameter that can be considered. Effectively, such a parameter
can be either explicitly provided by the MVNO via Type 5 interface or it can be determined by the InP
considering the location of the corresponding devices/users in combination with tailored service
information provided by the 5G Network Slice Broker. The set of cells that need to accommodate a
network slice should be communicated via the Itf-N interface towards the Master Operator Shared
RAN Domain Manager, which in turn would configure the appropriate cells using the Itf-B interface.
6. The Network Slicing Road towards Full Multi-tenancy
The evolution of network sharing towards full multi-tenancy relies on virtualization mechanisms and
software-based capabilities which are progressively introduced into 3GPP networks, influencing its
standardization roadmap. These capabilities enhance the notion of network slicing for supporting
particular communication services. Such emerging network slicing will be realized by allocating not
only network capacity, but also Virtual Network Functions (VNFs), computing resources, per slice
tailored control/user-plane splits, shared network functions across different slices and RAT settings as
described by NGMN in [8]. Network slicing can further be enriched accommodating particular
applications, which can be located at the network edge to improve end-users’ performance. 3GPP SA1
emphasizes the support for vertical industries via network slicing in Release 14 considering terminal
operations and configuration management via suitable APIs. The main attributes to realize the
aforementioned network slicing extensions in 3GPP towards full multi-tenancy are:
Network Function Virtualization: 3GPP has adopted ETSI NFV MANO [9] shedding light
on the potential impact of virtualized networks on the existing 3GPP SA5 network
management architecture [10], considering partially and entirely VNFs with respect to macro-
base stations and core network elements. The objective is to identify requirements, interfaces
and procedures, which can be re-used or extended for managing virtualized networks. In
Release 14 3GPP has introduced a specification on architecture requirements for virtualized
network management [11], considering complementary specifications on configuration, fault
performance and life-cycle management. An equivalent study focusing on small cells and on
the adoption of flexible Centralized-RAN has been performed at the Small Cell Forum [12].
Dedicated Core Networks (DCNs): In an effort to support devices/customers with different
service characteristics including vertical market players, 3GPP SA2, introduced in Release 13
the support of separate DCNs [13], with different operation features, traffic characteristics,
policies, etc. Each DCN is assigned to serve different types of users based on subscription
information, assuring resource isolation and independent scaling, offering specific services
and network functions including RATs. Effectively, the 5G Network Slice Broker may allocate
a collection of shared network resources and VNFs among particular slices that fulfill the
requirements of certain communication services.
User/Control-plane Separation and Service Chaining: 3GPP has initiated a study on
user/control-plane separation in TR 23.714 analyzing potential architecture enhancements for
core network elements, e.g. Packet Data Network Gateway (P-GW), Traffic Detection
Function (TDF), etc., to further enable flexibility for the network deployment and operation,
enabling a unified network management across different RATs. An equivalent process
focusing on services, e.g. firewall, Deep Packet Inspection (DPI), etc., should also be
considered when establishing network slices. 3GPP has performed in TR 23.718 (Rel.13) a
study on flexible mobile service steering focusing on policy provision and on instantiating
dynamic services in SGi-LAN, a service-oriented network connected to the P-GW. A
comprehensive study on flexible service chaining on mobile networks, considering a range of
different service chaining mechanisms is provided in [14].
Mobile Edge Computing (MEC): Many evolving 5G services are envisioned to be offered
closer to the user at the network edge in order to enhance latency and in general end-user
perceived performance, e.g. adopting the ETSI MEC paradigm [15]. Hence, flexible service
chaining should also be enhanced to establish dynamic services considering edge network
locations and potentially be combined with VNFs, in order to enable a joint optimization of
services and networking. Edge server locations can also be exploited for storage, computation
and dynamic service creation by verticals/OTT providers, introducing in this way another
multi-tenancy dimension.
Figure 6: 5G network slices structure
Figure 6 illustrates an example of different network slices operating on the same infrastructure: i) a
network slice that accommodates mobile broadband services, ii) an automotive network slice wherein
latency and reliability are critical parameters and iii) a massive Internet of Things (IoT) network slice
where scalability is essential for handling efficiently huge amounts of small data. To accommodate
strict latency goals and scalability, network functions can be instantiated at the edge cloud as
necessary, optimizing radio and core networks with respect to particular services. Different RATs
should be associated with distinct types of network slices, since they can serve best the requirements
of particular services.
7. Conclusion
This paper reviewed the path from network sharing towards multi-tenancy describing business
requirements and standardization efforts with a focus on 3GPP. In particular, it analyzed the
evolutionary path from early passive sharing to on-demand multi-tenant networks considering: i)
3GPP network sharing architectures, ii) network management extensions for supporting mobile virtual
operators and iii) the service exposure capability function that allows vertical market players to gather
information about mobile network resources. The notion of the 5G Network Slice Broker has been
introduced, which resides inside the infrastructure provider, detailing the required interfaces and
functional enhancements for supporting on-demand multi-tenant mobile networks based on the latest
3GPP network sharing management architectures. Finally, our work provided an overview of the
3GPP Rel.14 standardization efforts related to multi-service support and network virtualization as well
as other relevant standardization efforts outside 3GPP addressing how to enrich a 5G network slice by
flexibly provisioning virtualized network functions and services.
Acknowledgement
This work has been performed in the framework of the H2020-ICT-2014-2 project 5G NORMA. The
authors would like to acknowledge the contributions of their colleagues. This information reflects the
consortium’s view, but the consortium is not liable for any use that may be made of any of the
information contained therein.
References
[1] GSMA, The Mobile Economy 2016.
[2] GSMA, Mobile Infrastructure Sharing, Sep 2012.
[3] K. Larsen, “Network Sharing Fundamentals,” Technology Business, Jul. 2012.
[4] 3GPP TS 23.251, Network Sharing; Architecture and Functional Description, Rel.12, Mar. 2015.
[5] 3GPP TR 22.852, Study on Radio Access Network (RAN) Sharing enhancements, Rel.13, Sep. 2014.
[6] 3GPP TS 32.130, Telecommunication management; Network Sharing; Concepts and requirements, Rel.12, Jan. 2016.
[7] 3GPP TR 23.708, Architecture enhancement for Service Capability Exposure, Rel.13, Jun. 2015.
[8] NGMN Alliance, NGMN 5G White paper, version 1, Feb 2015.
[9] ETSI GS NFV-002 Architectural Framework, v1.2.1, Dec. 2014.
[10] 3GPP TR 32.842, Telecommunication management; Study on network management of virtualized networks, Rel.13,
Dec. 2015.
[11] 3GPP TS 28.500, Management Concept, Architecture and Requirements for Mobile Network that include Virtualized
Network Functions, Rel.14, Jan. 2016.
[12] Small Cell Forum, Network Aspects of Virtualized Small Cells, Release 5.1, Document 161.05.1.01, Jun. 2015.
[13] 3GPP TS 23.401, General Packet Radio Service (GPRS) enhancements for Evolved Universal Terrestrial Radio Access
Network (E-UTRAN) access, Rel.13, Dec. 2015.
[14] W. Haeffner, J. Napper, M. Stiemerling, D. Lopez, J. Uttaro, Service Function Chaining Use cases in Mobile Networks,
IETF Draft, Version 5, Oct. 2015.
[15] ETSI, Mobile-Edge Computing Introductory White paper, Sep. 2014.
Biographies
Konstantinos Samdanis (samdanis@neclab.eu) is a Senior Researcher and Backhaul/Broadband
standardization specialist with NEC Europe. He is involved in research for 5G architectures
participating in 5G-NORMA and is active in BBF on 5G and network virtualization. Konstantinos has
provided a number of tutorials in IEEE conferences for Green Communications and is the editor of the
Green Communications: Principles, Concepts and Practice book from Wiley. He received his Ph.D.
and M.Sc. degrees from Kings College London.
Xavier Costa-Pérez (xavier.costa@neclab.eu) is Head of 5G Networks R&D at NEC Laboratories
Europe, where he manages several projects focused on 5G mobile core, backhaul/fronthaul and access
networks. His team contributes to NEC projects for products roadmap evolution as well as to
European Commission R&D collaborative projects and has received several R&D Awards for
successful technology transfers. In addition, the 5G team contributes to related standardization bodies:
3GPP, BBF, ETSI NFV, ETSI MEC and IETF.
Vincenzo Sciancalepore (vincenzo.sciancalepore@neclab.eu) (S’11-M’15) received his M.Sc.
degree in Telecommunications Engineering and Telematics Engineering in 2011 and 2012,
respectively, whereas in 2015, he received a double Ph.D. degree. From 2011 to 2015 he was Research
Assistant at IMDEA Networks, focusing on inter-cell coordinated scheduling for LTE-Advanced
networks and device-to-device communication. Currently, he is a Research Scientist at NEC
Laboratories Europe in Heidelberg, focusing his activity on network virtualization and network slicing
challenges.
... Moving from conventional static sharing towards on-demand/on-the-fly dynamic multitenancy [127] requires a network sharing management architecture that enables capacity brokering. To understand the importance of automating bilateral market business processes, it is necessary to know how the current process works. ...
... In [127], the authors have reviewed the business requirements and standards in the context of multi-tenant mobile networks. They have introduced in detail the architecture of the 5G network slice broker. ...
... "A network slice refers to an isolated amount of network capacity customized to best suit specific service requirements [127]." ...
Thesis
Full-text available
Several parallel trends, including the growing number of Internet reliant devices/services, increasing Internet penetration rates, and the continuing popularity of bandwidth-hungry multimedia content contribute to the exponential surge of Internet traffic. The combination of these trends could imply a considerable increase in network infrastructure investment for the telecom and broadband operators. In addition, the high cost of initial investment could escalate the market barriers to entry for the innovative service providers incapable of deploying their own network infrastructure. In this dissertation, we explore if and how enabling optical access network sharing could cultivate new network ownership and business models that simultaneously keep the end-user subscription fees low and facilitate the market entry for the smaller service providers. We aim to identify and address the technological and economic barriers of optical access network sharing. The broad scope of this dissertation concerns the inter-operator sharing of optical access networks which connect the end-users to the operators' network in the last-mile. The access segment of the communications network is recognized to be the most costly due to its deployment scale. Therefore, a reduction in cost in the access will have a multi-fold impact on the overall capital expenditure for network deployments. The dissertation focuses in particular on PONs as the most widespread type of optical access networks. The central argument of the present research is that network infrastructure/resource sharing has the potential to reduce the capital and operational expenditure of the network operators. This will allow for more competition as the market entrance cost decreases. We first address the lack of tenant operators' adequate control over the shared resources in a multi-tenant PON as a technological barrier. We provide a solution to strengthen the network operators' control over their share of the network in a multi-tenant PON. This is made possible by allowing the operators to schedule the transmission over the network using tailored algorithms to meet their requirements (e.g., latency and throughput). The dissertation argues that providing a virtual (software) instance of the DBA algorithm as opposed to the inflexible hardware implementation first enables the coexistence of various services on the PON and second, improves the overall utilization of the network capacity. While the virtualization of the DBA removes the technical barrier for the inter-operator resource sharing, it does not come with a natural incentive for the operators to share their resources with competitors. Therefore we tackle the lack of incentive for sharing excess network capacity in PON by providing monetary compensation in return for sharing. We model the multi-tenant optical access network with multiple coexisting operators as a market where they can exchange their excess capacity. We propose a sealed-bid multi-item double auction to enable capacity trading between the network operators. Through mathematical proof and market simulation/visualization, we prove that the proposed auction mechanism meets the essential requirements for an economic robust market mechanism (e.g., incentive compatibility, individual rationality, and budget balance). This provides trusted market conduct in the presence of a central authority (e.g., the public infrastructure provider) that all the operators trust.
... During a burst period, each time a node would send a normal packet, with probability p burst it will send n burst packets with periodicity f burst instead. In the first four simulation groups,two burst periods occur in the URLLC slice during the simulation: in intervals [2][3][4] and [7][8][9]. The first burst characterisation is p burst = 0.1 , f burst = 1 ms and n burst = 10 ; the second has the same traffic properties but occurs with a higher probability, p burst = 0.2 . ...
... When observing the amount of scheduled RBs (13b), it can be observed that PASS still provides the required resources for each slice. 7 The line of the eMBB traffic is always on top of its mAR line due to Phase III being disabled. One occurrence worthy of comment is the discrepancy of the resources scheduled to the mMTC slice with LTE and with NR. ...
... Since the simulations are run with a relatively low MCS (10), and the size of one CBG is 7 times as small in the NR setup, the overhead of one CBG starts to have a meaningful impact. When the URLLC slice is scheduled 7 Remember that URLLC MPR is the same as mMTC mAR Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
Article
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Enforcing network slice isolation in 5G Radio Access Networks (RANs) can degrade a network slicing solution’s ability to efficiently support different types of traffic. In addition, competing virtual network operators may share one network infrastructure that forces network slicing solutions to provide fair treatment between them. This work presents Flexible Priority Scheduling (FPS). This RAN network slicing solution provides isolated treatment to slices with different traffic types, further exploring their needs and flexibility to achieve a more efficient solution. It does so by defining a contract interface that allows a flexible representation of different types of traffic, from heavy throughput services to low latency and bursty traffic. Furthermore, the presented contract representation allows a fair treatment of the tenant’s traffic in the Medium Access Control (MAC) scheduler that enforces this slicing solution: Priority Adaptation Slice Scheduler (PASS).
... An intermediate third-party service is running as a network slice broker to enable this slicing scenario. 5G network slice broker (NSB) allows the dynamic interoperability and resource trading requirements of market players such as infrastructure providers, consumers, and mobile network operators in trading the network and computational resources [5]. Instead of having a centralized network slice broker, offering brokering service as a blockchain-based distributed service will bring higher flexibility and eliminate the single point of failure [6]. ...
... As stated in [5], in addition to facilitating on-demand resource allocation, NSB performs admission control based on traffic monitoring and forecasting, including mobility, based on a global network view. It configures radio access network (RAN) schedulers to support multi-tenancy use cases. ...
... It configures radio access network (RAN) schedulers to support multi-tenancy use cases. According to 3GPP specifications and the initial design in [5], the 5G Network Slice Broker is co-located at the master operator-network manager (MO-NM), which monitors and controls the shared RAN, and interacts with the sharing operator network manager (SO-NM). Chaer et al. [9] presents how the blockchain leverages the 5G networks with potential opportunities for the 5G networks, including infrastructure crowdsourcing, infrastructure sharing. ...
Article
Full-text available
The novel concept of Factory-as-a-Service (FaaS) allows the agility of adapting the manufacturing process by identifying the industry's supply chain and user requirements. To cater to FaaS, flexibility in networking and cloud services is a must. 5G network slice broker is a third-party mediator that caters to networking resource demand from clients to the service providers. Thus, this paper introduces a secure blockchain-based network slice broker to facilitate FaaS. The proposed secure network slice broker (SNSB) provides secure, cognitive, and distributed network services for resource allocation and security service level agreement (SSLA) formation with coordination of slice managers and SSLA managers. In SNSB we introduce a federated slice selection algorithm with Stackelberg game model and Reinforcement Learning (RL) algorithm to compute the real-time and the optimal unit price and demand level. We provide an extensive implementation and performance evaluation of SNSB using the Katana slice manager and a custom SSLA manager.
... This explosive growth in mobile data traffic is mainly driven by the increasing prevalence of smartphones and handheld devices and the consequent migration of applications such as video-conferencing, ecommerce, and interactive multimedia services to mobile platforms. However, it comes at a time when mobile network operators (MNOs) are facing an imminent threat of capacity crunch while aiming for a balance between the amount Recently, a new business model of a third party neutral host responsible for providing access services on behalf of a number of MNOs to a particular location has attracted the attention of many radio access network (RAN) infrastructure providers and venue owners with the desire to provide high user experience without significantly increasing their TCO [5,6]. The need for neutral hosting is further magnified considering the stringent requirements imposed by 5GB UDNs, specifically the high FH capacity, the rising phenomenon of bring your own device (BYOD), and the emergence of new business models such as in-building operators (IBO) and over-the-top (OTT) service providers. ...
... Such desire for multi-tenancy capability stems from the fact that rolling out a single physical network has significantly less economical and technical burdens, such as site acquisition, installation cost, spectrum resources, and power consumption, than in the case when each MNO deploys a stand-alone network. When designed carefully, RAN sharing results in a substantial savings on implementation expenditure (IMPEX), capital expenditure (CAPEX), and operational expenditure (OPEX), as well as enabling additional application scenarios and vertical markets as a new source of revenue allowing for a higher return on investment (ROI) [6]. ...
Article
Full-text available
With the deployment of the fifth generation (5G) mobile network systems and the envisioned heterogeneous ultra-dense networks (UDNs), both small cell (SmC) and distributed antenna system (DAS) technologies are required by mobile network operators (MNOs) and venue owners to support multiple spectrum bands, multiple radio access technologies (RATs), multiple optical central offices (COs), and multiple MNOs. As a result, the neutral host business model representing a third party responsible for managing the network enterprise on behalf of multiple MNOs has emerged as a potential solution, mainly influenced by the desire to provide a high user experience without significantly increasing the total cost of ownership (TCO). However, designing a sustainable business model for a neutral host is a nontrivial task, especially when considered in the context of 5G and beyond (5GB) UDNs. In this paper, under an integrated optical wireless network infrastructure, we review how SmC and DAS technologies are evolving towards the adoption of the neutral host business model and identify key challenges and requirements for 5GB support. Thus, we explore recent candidate advancements in heterogeneous network integration technologies for the realization of an efficient 5GB neutral host business model design capable of accommodating both SmC and DAS. Furthermore, we propose a novel design architecture that relies on virtual radio access network (vRAN) to enable real-time dynamic resource allocation and radio over Ethernet (RoE) for flexible and reconfigurable fronthaul. The results from our simulations using MATLAB over two real-life deployment scenarios validate the feasibility of utilizing switched RoE considering end-to-end delay requirements of 5GB under different switching schemes, as long as the queuing delay is kept to a minimum. Finally, the results show that incorporating RoE and vRAN technologies into the neutral host design results in substantial TCO reduction by about 81% in an indoor scenario and 73% in an outdoor scenario.
... Meanwhile, it will be common for NOs within the emerging 5G business models to lease high-tier edge cloudlet and communication bandwidth resources from an InP through multi-tenancy [37,38]. This allows the NOs to expand the capacity of their edge cloudlets in order to serve the offloaded requests especially in periods of high load, while imposing fees on utilizing the cloudlet and the communication channels. ...
Thesis
Full-text available
Empowered by recent technological advances and driven by the ever-growing population density and needs, the conception of 5G has opened up the expectations of what mobile networks are capable of to heights never seen before, promising to unleash a myriad of new business practices and paving the way for a surging number of user equipments to carry out novel service operations. The advent of 5G and networks beyond will hence enable the vision of Internet of Things (IoT) and smart city with its ubiquitous and heterogeneous use cases belonging to various verticals operating on a common underlying infrastructure, such as smart healthcare, autonomous driving, and smart manufacturing, while imposing extreme unprecedented Quality of Service (QoS) requirements in terms of latency and reliability among others. Due to the necessity of those modern services such as traffic coordination, industrial processes, and mission critical applications to perform heavy workload computations on the collected input, IoT devices such as cameras, sensors, and Cyber-Physical Systems (CPSs), which have limited energy and processing capabilities are put under an unusual strain to seamlessly carry out the required service computations. While offloading the devices' workload to cloud data centers with Mobile Cloud Computing (MCC) remains a possible alternative which also brings about a high computation reliability, the latency incurred from this approach would prevent from satisfying the services' QoS requirements, in addition to elevating the load in the network core and backhaul, rendering MCC an inadequate solution for handling the 5G services' required computations. In light of this development, Multi-access Edge Computing (MEC) has been proposed as a cutting edge technology for realizing a low-latency computation offloading by bringing the cloud to the vicinity of end-user devices as processing units co-located within base stations leveraging the virtualization technique. Although it promises to satisfy the stringent latency service requirements, realizing the edge-cloud solution is coupled with various challenges, such as the edge servers' restricted capacity, their reduced processing reliability, the IoT devices' limited offloading energy, the wireless offloading channels' often weak quality, the difficulty to adapt to dynamic environment changes and to under-served networks, and the Network Operators (NOs)' cost-efficiency concerns. In light of those conditions, the NOs are consequently looking to devise efficient innovative computation offloading schemes through leveraging novel technologies and architectures for guaranteeing the seamless provisioning of modern services with their stringent latency and reliability QoS requirements, while ensuring the effective utilization of the various network and devices' available resources. Leveraging a hierarchical arrangement of MEC with second-tier edge servers co-located within aggregation nodes and macro-cells can expand the edge network's capability, while utilizing Unmanned Aerial Vehicles (UAVs) to provision the MEC service via UAV-mounted cloudlets can increase the availability, flexibility, and scalability of the computation offloading solution. Moreover, aiding the MEC system with UAVs and Intelligent Reflecting Surfaces (IRSs) can improve the computation offloading performance by enhancing the wireless communication channels' conditions. By effectively leveraging those novel technologies while tackling their challenges, the edge-cloud paradigm will bring about a tremendous advancement to 5G networks and beyond, opening the door to enabling all sorts of modern and futuristic services. In this dissertation, we attempt to address key challenges linked to realizing the vision of a low-latency and high-reliability edge computation offloading in modern networks while exploring the aid of multiple 5G network technologies. Towards that end, we provide novel contributions related to the allocation of network and devices' resources as well as the optimization of other offloading parameters, and thereby efficiently utilizing the underlying infrastructure such as to enable energy and cost-efficient computation offloading schemes, by leveraging several customized solutions and optimization techniques. In particular, we first tackle the computation offloading problem considering a multi-tier MEC with a deployed second-tier edge-cloud, where we optimize its use through proposed low-complexity algorithms, such as to achieve an energy and cost-efficient solution that guarantees the services' latency requirements. Due to the significant advantage of operating MEC in heterogeneous networks, we extend the scenario to a network of small-cells with the second-tier edge server being co-located within the macro-cell which can be reached through a wireless backhaul, where we optimize the macro-cell server use along with the other offloading parameters through a proposed customized algorithm based on the Successive Convex Approximation (SCA) technique. Then, given the UAVs' considerable ability in expanding the capabilities of cellular networks and MEC systems, we study the latency and reliability aware optimized positioning and use of UAV-mounted cloudlets for computation offloading through two planning and operational problems while considering tasks redundancy, and propose customized solutions for solving those problems. Finally, given the IRSs' ability to also enhance the channel conditions through the tuning of their passive reflecting elements, we extend the latency and reliability aware study to a scenario of an IRS-aided MEC system considering both a single-user and multi-user OFDMA cases, where we explore the optimized IRSs' use in order to reveal their role in reducing the UEs' offloading consumption energy and saving the network resources, through proposed customized solutions based on the SCA approach and the SDR technique.
... Moreover, the paper forecasted the development trend of mobile network control, management, and arrangement, also gave an in-depth introduction of future development route of 3GPP EPS and defined relevant standards. Reference [12] summarized the development process of 3GPP from the principle of network sharing, mechanisms, and architecture to the future multitenancy on-demand systems. The paper also introduced the concept of network slicing agents in 5G systems, enabling virtual network operators, top providers, and industry vertical market participants to dynamically request and lease resources from infrastructure providers by signaling. ...
Article
Full-text available
Aiming at improving the user’s driving safety and travel efficiency and satisfying their future needs of highly autonomous driving services and based on the features of 5G’s ultralow latency, ultrahigh reliability, and ultrawide broadband, this paper proposes an overall solution for 5G-V2X, including industry ecological solutions and technical solutions. The industry ecological solutions include the analysis of the Internet of Vehicles industry chain architecture, future business models, and innovative ecosystems. The technical solutions include the overall solution for V2X, the overall blueprint of the planning function for V2X, core technology scenarios, and key technical capabilities. These solutions can integrate users, vehicles, and roads completely and effectively, which helps the government to achieve effective monitoring and management of intersection of user-vehicle-road and realizes the 5G-based V2X pan-transportation and logistics ecology. The validity and feasibility of these solutions are verified by some applications in the port park.
... Note that, from an InP perspective, [55] introduces the so-called 5G network slice broker, hosted in the NSSMF of the InP, that gathers global network load measurements and configures the RAN scheduler policies based on the negotiated SLA and the size of the network slice. Moreover, the openness of the mobile network may lead to an adversarial behavior of MSPs consisting of maximizing the acquired share of resources. ...
Thesis
Future mobile networks envision unprecedented innovation opportunities and disruptive use cases. As a matter of fact, the 5G and beyond networks' pledge to deliver mission-critical applications mandates a versatile, scalable, efficient, and cost-effective network capable of accommodating its resource allocation to meet the services' heterogeneous requirements. To face these challenges, network slicing has emerged as one of the fundamental concepts proposed to raise the 5G mobile networks' efficiency and provide the required plasticity. The idea is to provide resources for different vertical industries by building multiple end-to-end logical networks over a shared virtualized infrastructure. Each network slice is customized to deliver a specific service and adapts its architecture and radio access technologies.Precisely, applications such as industrial automation or vehicular communications pose stringent latency and reliability requirements on cellular networks. Given that the current mobile network cannot meet these requirements, ultra-reliable low-latency communications (URLLC) embodies a vital research topic that has gathered substantial momentum from academia and industrial alliances. To reach URLLC requirements, employing multi-connectivity (MC), i.e., exploiting multiple radio links as communication paths at once, is a promising approach.Therefore, the objective of the present manuscript is to investigate dynamic scheduling techniques, exploiting redundant coverage of users, guaranteed in numerous 5G radio access network scenarios. We first review the evolution of mobile networks and discuss various considerations for network slicing architecture and its impact on resource allocation design. Then, we use tools from queuing theory to model a system in which a set of URLLC users are connected simultaneously to two base stations having the same bandwidth; we refer to this scenario as the homogenous case. We introduce suitable scheduling policies and evaluate their respective performances by assessing their reliability. Next, we extend the homogenous case's results to a more general setting where the physical interfaces manage different bandwidths, referred to as the heterogeneous case. Finally, we merge the above elements to validate the choice of resource allocation schemes considering the deployed architecture.
Article
5G has been launched in a few countries of the world, so now all focus shifted towards the development of future 6G networks. 5G has connected all aspects of society. Ubiquitous connectivity has opened the doors for more data sharing. Although 5G is providing low latency, higher data rates, and high-speed yet there are some security-related vulnerabilities. Those security issues need to be mitigated for securing 6G networks from existing challenges. Classical cryptography will not remain enough for securing the 6G network. As all classical cryptography can be disabled with the help of quantum mechanics. Therefore, in the place of traditional security solutions, in this article, we have reviewed all the existing quantum solutions of 5G existing security issues to mitigate them and secure 6G in a Future Quantum World.
Article
Full-text available
Increasing traffic and the number of people using mobile devices are both driving up the energy usage of mobile networks. There must be a focus on energy efficiency in the next generation of mobile networks in order to ensure their long term viability. Increasing the energy efficiency of 5G and beyond networks is addressed in this thesis in two ways, namely by reducing the network's energy usage and designing an energy-efficient network architecture. Base stations (BSs), the most energy-intensive aspect of mobile networks, are the subject of the first section of this thesis. A mobile network provider provides us with a data collection that includes information on network load. It is a challenge to use mobile network traffic data to train ML algorithms for sleep mode management decisions due to the coarse time granularity of data. We propose a method to regenerate mobile network traffic data taking into account the burstiness of arrivals. We propose ML-based algorithms to decide when and how deep to put BSs into sleep. The current literature on using ML in network management lacks of guaranteeing any quality of service. To handle this issue, we combine analytical model based approaches with ML where the former is used for risk analyses in the network. We define a novel metric to quantify risk of decision making. We design a digital twin that can mimic the behavior of a real BS with advanced sleep modes to continuously assess the risk and monitor the performance of ML algorithms. Simulation results show that using proposed methods considerable energy saving is obtained compared to the baselines at cost of negligible number of delayed users. In the second part of the thesis, we study and model end-to-end energy consumption and delay of a cloud native network architecture based on virtualized cloud RAN forming foundations of open RAN. Today large telco players achieved a consensus on an open RAN architecture based on hybrid C-RAN which is studied in this thesis. Migrating from conventional distributed RAN architectures to the network architectures based on hybrid C-RAN is challenging in terms of energy consumption and costs. We model the migration cost, in terms of both OPEX and CAPEX, with economic viability analyses of a virtualized cloud native architecture considering the future traffic forecasts. C-RAN based designs may be more cost-effective than D-RAN in some circumstances, although the infrastructure costs of fronthaul and fibre connections are not obvious. Optimizing the fronthaul using an integer linear programming (ILP) issue reduces the migration expenses. For big problem sizes, we present a heuristic technique based on artificial intelligence to handle the problem optimally. An important challenge in network design and administration is dealing with the tradeoff between energy consumption and latency. As part of a hybrid C-RAN design with many layers, we devise an ILP issue to reduce the network's energy usage while also enhancing latency by storing popular content near the edge. In addition, we look at the trade-off between network bandwidth use and total energy consumption. By finding a middle ground between several performance indicators, we show that intelligent content placement not only cuts down on latency, but also conserves energy. By creating logical networks that are specifically designed and configured for each service, we hope to achieve the same goal of cutting down on network energy usage. There has been a significant reduction in overall energy usage by network slicing, according to the scientific literature. In most research, only radio access network resources are included. When the RAN component of the network is taken into account, energy usage decreases as more bandwidth is supplied to consumers. A new model for energy consumption in the cloud and the fronthaul section of a network, described in this thesis, shows that increasing bandwidth allotment also increases processing energy consumption in both cloud and fronthaul. A non-convex optimization problem is developed to address this issue, and the network's energy consumption is reduced while the quality of service (QoS) of the slices is ensured. The issue is transformed into a second-order cone programming problem, and the optimum solution is found. End-to-end network slicing may reduce the overall energy consumption of the network compared to radio access network slicing, according to our study.
Thesis
The adoption of disruptive technologies is a very critical decision affecting the survival and continuity of businesses, startups to multinationals. Many researchers and decision makers consider technology adoption as a decision each organization should evaluate and decide upon. But are organizations really free to decide whether to adopt a specific technology or not? Many businesses do not have the privilege to dictate its needs to vendors, its services to customers or its business models to competitors.In this work we study the organization's decision on disruptive technology adoption at two levels. In the first level, we consider the economic forces (external forces) affecting the organization's decision. More thoroughly, we look at the market's response to disruptive technologies. In the second level, we consider the organizational forces (internal forces) affecting the organization's decision. More thoroughly, we look at the organization's needs and readiness for a disruptive technology.This work considers cloud computing as the studied disruptive technology and focuses its contribution on cloud computing adoption. In the organizational level, a Decision Support Solution is designed using collaborative filtering recommender systems to help decision makers evaluate their organizational needs and decide on the optimal technology mix suitable for their organization. In the economic level, the economic factors are modeled using Agent-Based Modeling. We consider cloud computing adoption in the 5G mobile networks as a specific case of cloud adoption. This specification is needed to allow accurate modeling of the economic factors. Two scenarios are considered, the first assumes perfect competition in the 5G market and the second assumes an oligopolistic market.
Service Function Chaining Use cases in Mobile Networks
  • W Haeffner
  • J Napper
  • M Stiemerling
  • D Lopez
  • J Uttaro
W. Haeffner, J. Napper, M. Stiemerling, D. Lopez, J. Uttaro, Service Function Chaining Use cases in Mobile Networks, IETF Draft, Version 5, Oct. 2015.
Mobile-Edge Computing-Introductory White paper
ETSI, Mobile-Edge Computing-Introductory White paper, Sep. 2014.
Network Sharing Fundamentals
  • K Larsen
K. Larsen, "Network Sharing Fundamentals," Technology Business, Jul. 2012.
Network Sharing; Architecture and Functional Description
3GPP TS 23.251, Network Sharing; Architecture and Functional Description, Rel.12, Mar. 2015.
Telecommunication management; Network Sharing; Concepts and requirements
3GPP TS 32.130, Telecommunication management; Network Sharing; Concepts and requirements, Rel.12, Jan. 2016.
NGMN 5G White paper, version 1
  • Ngmn Alliance
NGMN Alliance, NGMN 5G White paper, version 1, Feb 2015.
Telecommunication management; Study on network management of virtualized networks
3GPP TR 32.842, Telecommunication management; Study on network management of virtualized networks, Rel.13, Dec. 2015.
Management Concept, Architecture and Requirements for Mobile Network that include Virtualized Network Functions
3GPP TS 28.500, Management Concept, Architecture and Requirements for Mobile Network that include Virtualized Network Functions, Rel.14, Jan. 2016.