Slice-aware Service Chaining
Zoltán Zsóka, Khalil Mebarkia
Department of Networked Systems and Services,
Budapest University of Technology and Economics
{zsoka, mebarkia}@hit.bme.hu
Abstract—Among many other key features, the 5G technology
introduced the concept of Network Slicing. It supports that different services or even different operators use the same network
resources in a dedicated or shared way. On the other hand,
QoS for the traffic is mostly provided on the network resources
by weighted or strict scheduling. In this paper, we introduce
heuristic service chaining solutions that consider shared slicing
and apply a kind of preservation of network resources for other
slices to hold the QoS expectations. Our numerical results show
the advantages of them from the aspects of loads and overloads
of links.
Index Terms—Network Slicing, Service Function Chaining
(SFC), NFV, VNF, 5G Networks
I. I NTRODUCTION
Emerging technologies such as software defined networks
(SDN) and network function virtualization (NFV) along with
the concept of Network Slicing in Service Function Chaining
(SFC) are playing an enabler key for the 5G mobile networks. The connectivity of massive devices characterizes this
technology, enabling and supporting a wide range of novel
applications such as Autonomous Driving, Augmented Reality,
and Internet of Things (IoT).
In recent years, Software Defined Networking (SDN) and
Network Function Virtualization (NFV) has been playing a
key role in the 5G network architecture, also offering new and
different ways to design, implement and manage the network
and its services. Moreover, they both can significantly enhance
network performance in order to enable flexibility, including
Virtual Network Virtualized (VNF), Service Chaining (SFC),
separated control plane and Network Slicing, etc.
NFV is considered the primary entity that provides an
architecture to share resources between Virtual Network Virtualized (VNF) and the core network. Meanwhile, Internet
Service Providers (ISPs) maintain network connectivity and
offer additional services and network functions (NFs), like
Network Address Translation (NAT), Firewalls (FWs), and
Domain Name Service (DNS). These functions are deployed
at various locations, Data Centers (DCs), network nodes, or
customers’ premises. Moreover, these emerging technologies
SDN and NFV promote the opportunity to design and implement scalable, elastic, and programmability of networks.
The Service Chain (SC), also known as Service Function
Chain (SFC), aims to compose an ordered set of VNFs by
selecting and connecting these functions based on network
applications’ requirements and dependencies. At the same
978-1-7281-7705-2/21/$31.00 ©2021 IEEE
time, orchestration includes the management of the devices
that host the VNFs.
Network Slicing is defined as an approach to network
operations that builds and manages a network to provide programmability and enables the ISPs to maximize the network
resource usage and service flexibility. It supports different
services and may be applied for resource management. Using
this concept, the network infrastructure operator can dedicate
resources to a specific slice of the network according to the
network operator’s request, e.g., assigning a firewall to a
particular virtual network instead of using a shared firewall
[1], [2].
The concept of the 5G mobile networks includes slicing [3],
i.e., the organization of network and computation resources
into soft- or hard-separated sets according to particular offered
services that might require different series of VNFs, and the
network infrastructure and VNF resources might be or not be
shared among them. SFC and orchestration for the traffic of
a service have to be performing inside the assigned resource
slice. Hence, network slicing along with NFV allows the ISPs
on the one hand to provide dedicated virtual networks with
functionalities specific to the services over a common network
infrastructure in the 5G Core (5GC) [4].
Moreover, slicing cannot be limited only on managing and
processing resources, but SFC has to consider the transporting
resources in the network, which can also improve the service
quality. It implies the need for high flexibility in the configuration of network devices. An enabling technology such as SDN,
but due to switching performance issues it is not supported in
a large part of the transport and mobile backhaul networks [5].
On the other hand, ISPs are introducing and using network
slicing to allow multiple virtual networks to be created on
top of a common shared virtualized infrastructure. Besides,
they gain customization according to the specific needs of
applications, services, devices, customers, or operators. Many
service providers and research institutes operate a single
network infrastructure to support an ever-increasing number
of services, thus the ability to fit transport customized to
application needs is critically important. This includes creating
network slices with different characteristics, which can coexist
on top of the shared network infrastructure [6].
Therefore, network slicing must be built up to ensure the
low latency and guaranteed bandwidth for different services,
which is expected to play a critical role in 5G networks.
In this paper, we consider shared slicing approach and use
them to apply some type of preservation of network resources
for other slices to meet the QoS expectations. We show the
importance of network slicing for building up to ensure the
low latency and guaranteed bandwidth for different services,
which are considered to play a critical role in 5G network.
This paper applies a multi-slice scenario that considers the
topology and current load information of the IP layer below.
We propose heuristic SFC solutions for dynamic calculation
of service chains by considering the load of slices. We analyze
the proposed solutions and show their benefits and drawbacks
by numerical results of a case study.
In Sect. II, we present the related works, including our
antecedent works on the model concept considered in SFC
problems. Sect. III we introduce the motivations on using sliceawareness in service chaining, while the proposed solutions are
presented in Sect. IV. After the description of the heuristics,
we also analyze the behavior of the algorithms on small
examples. The performance is evaluated based on numerical
results from a larger sample network in Sect. V. We conclude
and provide the future work of this paper in Sect.VI
II. R ELATED W ORKS
Different approaches have been proposed by industries,
research institutes, and mobile operators to standardize SFC.
In [7], the European Telecommunications Standards Institute
(ETSI) defines a network service as a chain of VNFs and
emphasizes the demand for a new set of orchestration and
management functions.
Papers [8], [9] address NFV as a promising architecture
proposed to increase the scalability and the functionality of
the network by leveraging virtualization technologies. It is
how telecommunication networks and services are designed
and operated when traditional Network Functions (NFs) are
transformed into VNFs. Functions as Firewall (FW), Load
Balancer (LB), or Network Address Translation (NAT) run
over a distributed, cloud-based infrastructure referred to as
Network Function Virtualization Infrastructure (NFVI). ETSI
defines SDN usage in an NFV as an architectural framework
and proposes a framework with three main components: VNFs
and two subsystems termed respectively Management and
Orchestration (MANO) and NFVI where VNFs are deployed.
While in the RFC 7665 [10], IETF defines the service function
chain as an ordered set of abstract service functions and
ordering constraints. IETF also describes an SDN based SFC
architecture. In this, an SFC classifier in the data plane
performs a classification of end-to-end traffic to determine
which VNF should be chained to process the traffic based
on its requirements.
Some recent papers address similar problems while considering multiple slices in the network. Slice can be defined
as a set of network and VNF resources, which can support
one or more services, each with a prescribed series of VNFs
that the service traffic shall pass. The supported services can
be told to be in the slice. The authors of [11] formulate the
problem of statically embedding service chains into slices
while considering also network link capacities
Various scientific works have been addressed in context
for the placement and chaining of VNFs, in which the SFC
solutions mostly work assuming the network topology and
the demands, with VNF capabilities and VNF requirements,
respectively. For instance, many solutions have been proposed
for optimal placement and chaining of VNFs. Most of these solutions focus on selecting the right nodes for deploying a VNF
for a specific traffic demand. The authors in [12] proposed a
placement algorithm, which takes into consideration hardware
accelerator resources in addition to compute resources. They
aim to optimize the use of resources in NFVI, while placement
algorithms must consider the presence of accelerators in NFVI
nodes. They described an Integer Linear Programming (ILP)
for the accelerator-aware VNF placement problem. In [13],
the authors studied the VNF placement problem in SDN/NFVenabled networks. They formulated the problem as a Binary
Integer Programming (BIP) in which they aim to minimize a
weighted cost, including the VNF placement cost. The authors
proposed a Double Deep Q Network-based VNF Placement
Algorithm (DDQN-VNFPA) using deep reinforcement learning. The proposed solution consists of running trained Double
Deep Q Network models to output the optimization strategies
of VNF placement according to network resource states.
Network Slicing is one of the most crucial parts of 5G
core networks. Its definition has never been unique, clear, and
precise. It is varying from different perspectives of the various service providers. For instance, Next Generation Mobile
Networks (NGMN) [14] defines a network slice as a set of
network services that consists of 3 layers, Service Instance
Layer, Network Slice Instance Layer, and Resource layer. The
network slice runs on top of physical resources where both
network services and resources conform to a logical network
to deliver specific requirements. The authors of [15] formulate
the problem of statically embedding service chains into slices
while also considering network link capacities. In [16] another
MILP formulation is given for the problem of optimizing
slices over multiple domains and accepting multiple services
in each slice. The authors also present a heuristic that can
guarantee the QoS requirements for the services by allocating
the needed resources for the slices. Moreover, the authors
in [17] focus on the end-to-end network slice life cycle
management of network slices on different sites using a single
management and orchestration entity with a coherent proof
of concept. They propose algorithms for efficiently activating,
deactivating, and decommissioning the network slices, using
real time status information of network slices from Network
Slice Management Function (NSMF). The results show that
by adopting better strategy in these algorithms in controlling
various phases of slice life cycle, the response time can be
reduced for a user request by 50%.
The authors in [18] present models for sliced networks
to investigate the cost reduction promises of using the NFV
and network slicing technologies. The models aim to allocate
network costs to deploy slices to show the network efficiency
using network slicing. The authors in this paper consider a
network slice to support one specific service, while a slice
consists of a chained VNFs. However, in this work we do not
consider only a slice for a specific service, it can be shared
with other services as well to preserve network resources for
other slices to hold the QoS expectations.
In our previous work [19], we focus on 5G QoS issues like
the end-to-end delay and loss, which might come from the
backhaul segment. We propose a multilayer network model
for the analysis of the effects of migrating the network to
5G, while considering different distribution and placement of
VNFs in the IP network.
In [20], the service chaining problem is considered in a twolayer model, which consists of a Functional Layer (FL) and
a Network Layer (NL). We logically separate the topology
of VNF-capable nodes and functional links allowed among
them, and the topology of network nodes and links. We address
the problem of considering the current load state of both the
functional links and the network below it. We discuss how
to determine the SC according to the required bandwidth and
VNF order while avoiding overloads on the network links. As
a result, we propose heuristic and ILP solutions to formulate
these challenges. These solutions are based on the dynamic
calculation of SC by considering the current network load
to avoid the use of heavily loaded links. The heuristic algorithm OdAASP (Overload Avoiding Augmented Shortest Path)
determines the shortest path between source and destination
with the awareness of considering overload avoidance. As a
comparative solution, we use the algorithm SFC-CSP (SFCConstrained Shortest Path) that finds the shortest path and
satisfies a given SFC constraint and is proposed in [21].
These solutions are improved and studied in [22] by
proposing different bandwidth-aware heuristic solutions for
dynamic creation of ordered service chains in networks with
a predefined set of allowed VNF-VNF connections.
In context of implementing NFV-SFC, we present in [23]
a multilayer network model that allows SFC with the support
of multi-tenant slices. The aim is to execute SFC solutions
proposed in [20] that help to avoid network link overloads
and QoS violations using virtual routers over Amazon Web
Services (AWS) and a variety of technologies such as Segment
Routing (SR) for implementing Service Chains.
III. M OTIVATION ON SLICE AWARENESS IN SFC
In slicing, we often use separation of resources supported
by a strict allocation mechanism. In this case, a part of
VNF hosting nodes and functional or even network links are
dedicated to each slice. The dedicated slicing may lead to
unexploited resources.
It easy to see that exploitation is higher when slices can
share network resources. However, in the case of shared slicing, operators might consider congestion of different service
traffics, leading to unexpected QoS issues if the load gets
higher.
To illustrate this issue, let us analyze a multi-slice scenario
with a simple network considering a one-to-one mapping
of Functional Layer links on the Network Layer links. The
topology is shown in Fig.1.
The slices accord to different 5G services with different
required VNF series and QoS requirements. Due to the latter,
we consider weights wi assigned to each slice Si . These
weights are used in the packet scheduling applied for the traffic
on the network links. They should determine the planned usage
weights of these resources, i.e., the link loads coming from the
different slices.
E
v1
A
K
B
M
v2
v2
L
C
N
D
v1
F
Fig. 1. Toplogy with service chains
We consider the VNF v1 available on the nodes K and N ,
and v2 on the nodes L and M . Let us suppose slices S1 and
S2 with weights w1 =w2 =0.5, and with needs of VNFs v1 and
v2 , respectively. The capacity of each link is 1Mbps.
The bandwidth of requests (r1 , r2 and r5 ) from slice S1
is 0.25 Mbps, and requests (r3 and r4 ) from slice S2 is 0.3
Mbps. The requests arrive in the indexes’ order, and we use a
simple SFC resulting in the chains shown in the third column
of Table III.
Requests
r1 (A − C)
r2 (B − D)
r3 (E − F )
r4 (E − F )
r5 (A − C)
Slice
S1
S1
S2
S2
S1
SC
A-K-L-C
B-M -N -D
E-K-L-N -F
E-K-L-N -F
A-K-L-C
Load on link K-L (Mbps)
0.25
0.25
0.55
0.85
1.1
The chains of requests from slice S1 and S2 are shown
in Fig.1 with solid blue and dotted red lines, respectively.
Regarding the fourth column of Table III we observe the
overload of link K-L after the chaining of r5 . Taking this
situation under a flow level analysis, we find that the traffic
of slice S1 will get 0.5 Mbps according to the scheduling
weights, i.e., it will not suffer losses. Whereas in the case of
the traffic of slice S2 we get a packet loss of more than 0.18.
Since some links in the network are lightly loaded, the
overload can be avoided by using a kind of load balancing, i.e.,
applying alternative SCs for some requests. The classic way
for it is to choose a different SC for request r5 that does not
contain the link K-L, e.g., A-K-M -N -L-C. However, note
that this way, the traffic of S1 had to use a longer chain and
put more load on further links because the traffic of slice S2
was too greedy.
Another way to avoid this situation is to do a kind of
resource preservation for the traffic that might arrive later
and use the resource. To implement this, we could choose an
alternate SC for request r4 and leaving enough bandwidth free
for request r5 . Even if at the arrival of r4 the use of the original
(red dotted) SC would not yet cause an overload on K-L, we
take the SC E-K-M -N -F shown with a dashed-brown line
in Fig.III. Therefore, link K-L will not get overloaded, and
the load of the slices on it is also more similar to the planned
rates reflected in the weights w1 and w2 .
IV. S LICE L IMITATION A LGORITHMS
We defined an allocation based service chaining solution
set with the aim to avoid the issue presented above. We apply
a mechanism that limits the traffic load of the slice on the
used links. It should reduce the big difference between the
relative load loadrel (S, l) coming from slice S on link l, and
the weight wQoS (S, l) set for QoS-based serving of the packets
of slice S on link l.
In Table I, we summarize all the key notations used throughout the article.
TABLE I
S UMMARY OF NOTATIONS AND SYMBOLS .
Notation
S
l
lF
lN
loadrel (S, l)
wQoS (S, l)
SL(S, l)
SL(S, lF )
b(r)
B(lF )
c(lF )
B(lN )
Representation of the symbol or symbol
Slice
Link
Functional Link
Network Link
Relative load coming from S on l
Packets’ weight of S on l
Slice Limit given for link l of slice S
Slice Limit given for link lF of slice S
Bandwidth of request r
Capacity of link lF
Cost of each link lF
Capacity of link lN
Beyond this original aim, we find that in the case of using
appropriate limits, the limitation mechanism can also help
avoid link overloads. The numerical results in this paper
concentrate on this second advantage.
The main idea in the load limitation based service chaining
is the use of high costs for those links, where loadrel (S, l)
would override the slice limit SL(S, l) given for link l if
the current request of slice S would use link l. We introduce
two algorithms, which differ in the layer of links, where the
limitation is considered.
A. Slice Limitation on the Functional Layer (SLF)
As first, we take into account only the link loads in the
Functional Layer. The algorithm SLF uses a set of limit values
SL(S, lF ) between [0, 1], for each slice S and functional link
lF .
SL(S, lF ) values can be different for different links lF ,
although it is more convenient to use the same value. The sum
of the slice limit values for every slice can be even higher than
1 on link lF .
The algorithm is based on the lowest cost chain solution
SFC-SP [21]. That finds the lowest cost chain from the source
S
s to the destination d of the service request rsd
considering
S
the series of VNFs prescribed for rsd .
In SLF, first of all c(lF ) gets modified to a rather high value
like 106 times greater than its original cost, if:
X
S
b(rsd
)+
b(r) > SL(S, lF ) × B(lF )
(1)
r∈S
In our two-layer network model, this capacity is calculated
as the bottleneck capacity on the network link path to that lF
is mapped.
After modifying costs, the lowest cost chaining finds a chain
excluding the links where the slice traffic would be over the
limit. Note that the Slice Limitation concept could be applied
with any other service chaining algorithm.
It is worth to mention that the performance of Slice Limitation based algorithms can depend on how the limit values
are determined. We assume to have the right constellation by
choosing values reflecting the QoS weights and the priorities
of the slices. Another essential property of the algorithm is
that the load and limit of only one slice are taken into account
during the cost modification step.
B. Slice Limitation on the Network Layer (SLN)
The algorithm SLN works very similarly to SLF, but the
relative load limitation is taken into account on the network
links of NL. Since more than one functional link can be
mapped on a network link, their load cannot be considered
independent. The QoS-based prioritization is done on the
Network Layer’s resources; thus, from SLN we can expect
service chaining that is more adjusted to packet serving.
The load limitation with SLN is based on the values
SL(S, lN ) set for each slice S and network link lN . These
values have the same properties as the SL(S, lF ) values in
SLF.
The algorithm SLN starts with the modification of the cost
c(lF ) of each functional link lF , where the mapping of lF
contains at least one network link lN with
X
S
b(rsd
)+
b(r) > SL(S, lN ) × B(lN )
(2)
r∈S
The functional links that violate the limitation get a high
cost leading to the use of network links, which are not so
much loaded by the network slice S.
Similarly to SLF, the cost modification is followed by the
selection of the lowest cost service chain. Note that both SLF
and SLN can lead to the use of more resources for holding the
limitations.
C. Analysis of the algorithms
To clarify the differences between the algorithms SLF and
SLN, let us analyze a simple scenario where they lead to
different service chains.
Consider the functional topology in Fig. 2, in which the
VNFs v1 and v2 are placed in node d2 , and only v1 in node
d3 . The other nodes remain without any VNF. The functional
links are mapped to the network topology in Fig. 3. Simply
the lowest cost path is used, and the cost of the functional link
is determined as the total cost of this path, as they are shown
on the right side of the figures. Each network links, and thus,
each functional links as well, have 1Mbps capacity.
We assume to accept two traffic requests r1 and r2 from
the source s to destination d1 and d2 , both from slice S1 and
with 0.3 Mbps required bandwidth. Request r3 is from slice
S2 and requires 0.5 Mbps bandwidth between source s and
destination d3. Slice S1 requests need to pass through VNF
v1 , and slice S2 requests need to pass through VNF v2 .
Let us consider the algorithm SLF and SLN to use the
following limitations for each functional and network links,
respectively.
(
SL(S1 , lN ) = 0.4
(3)
SL(S1 , lN ) = 0.4
This means the exclusion of having both S1 requests on
the same functional link when using SLF, and the same holds
for network links when using SLN. For slice S2 we use the
limitations:
(
SL(S2 , lN ) = 0.6
(4)
SL(S2 , lN ) = 0.6
s
a
b
s
2
d2 v1 , v2
4
d3
d2 v1 , v2
3
d3
d1
v1
d3
1
d1
SLF
SLN
The difference between SLF and SLN can be seen in
both the Functional and Networking Layers. Request r2 takes
a different service chain to avoid the violation of traffic
limitation on the network link between nodes s and a.
V. P ERFORMANCE E VALUATION
In this section, we evaluate the proposed solutions using a
mid-size case study. We analyze two network characteristics
to compare the solutions:
• Average relative load on used network links,
• Number of overloaded links in the network,
• Average relative load on used network links per slice,
2
1
d3
d1
SLN
A. Topology
The network under the scope is the hypothetical backbone
network of Algeria, which is used in [20] and [22]. Fig.4
presents the topology of the IP layer, which contains 10 Core
Routers (black-filled nodes) and 17 Edge Routers (grey-filled
nodes) each at different sites.
v2
v1 , v2
v3
v3
v1
Fig. 4. IP layer topology and VNF placement
One type of IP connection was used; 10Gbps Connections
are attached to Core Routers and Edge Routers. In addition,
27 eNodeBs are distributed on the 27 sites, and three different VNFs are placed by random placement resulting in the
following Core nodes:
•
•
•
Fig. 2. Functional Layer paths
d2
Fig. 3. Network Layer paths
•
v1
d1
SLF
2
3
1
3
b
1
2
a
d2
b
1
1
The calculated service chains use the paths in the functional
and network layer as shown in Fig. 2 and Fig. 3 respectively.
We get the dashed-line paths and the dotted line paths when
using SLF and SLN algorithms, respectively.
s
s
b
•
v1
v1
v3
v2
v3
and v2 are placed in the capital Algiers,
is placed in Boussada city,
is placed in Constantine city,
is placed in Tenes city,
is placed in Oran city.
There are three simplex traffic demand types to be
routed, Interactive Video, Best Effort, and New Services.
Each traffic demand requires to pass through the following
VNFs(v3 , v2 , v1 ). The demands of New Services type start
from one eNodeB in Algiers to all eNodeBs. For the other
types, the traffic demand starts and ends in eNodeBs. We
aim to study a scenario where the bandwidth of the type of
new services (NS) grows linearly from 0 Mbps up to 1500
Mbps. These traffic demands arrive in a randomized order.
The numbers of demands and the required bandwidth and slice
limitation rate are summarized for each traffic type in Table.
II.
TABLE II
T RAFFIC TYPES CHARACTERISTICS
Bandwidth
3, 98 Mbps
0 − 1500 Mbps
Number
702
27
Slice Limitation
0.4
0.6
B. Numerical Results
In the section, we analyze two topologies due to significant
differences observed during the analysis of IP links. Therefore,
we made a separated analysis for core and edge links, as the
topology of both link types is different, and the VNFs are
placed only in core nodes.
Note that the links with zero load are not considered in the
calculation of the average since links without load do not have
any effect on the QoS.
Besides the proposed solutions, we use other SCs
implemented algorithms as a comparative study presented
in [20]. On the one hand, the algorithm CSP consists of
finding the shortest path that satisfies a given SFC constraint.
While the algorithm OdAASP finds another shortest path to a
destination by taking into account the overload avoidance on
the links.
1) Analysis of Edge links:
The Fig.5 and Fig.6 show the relative load and number of
overloaded edge links.
SLN
SLF
CSP
OdAASP
0.20
SLN
SLF
CSP
OdAASP
2.5
2.0
1.5
1.0
0.5
0.0
0
200
400 600 800 1000 1200 1400
Bandwidth of New Services (Mbps)
Fig. 6. Number of overloaded Edge IP links.
In the high-load range (over 950Mbps), on the one hand,
we observe decreasing in case of SLN because the solution
finds a chain excluding the links where the slice traffic would
be over the limit. On the other hand, SLF is showing up
to 3 overloaded edge links while SLN does not show any
overloaded during the whole experiment as the selected paths
may be longer (Fig.6). In addition, OdAASP increases until
1300 Mbps where we see a slight decrease in loads that
comes from starting to use further functional links quite early
during the chaining of all the demands.
2) Analysis of Core links:
The Fig.7 and Fig.8 present the relative load and number of
overloaded core links.
For low-load range (below 500 Mbps) as the first observation, all algorithms are almost performing the same. Obviously
SLF, SLN and OdAASP perform better than CSP that shows
slightly high load as a consequence few overloaded links start
to appear in Fig.8.
0.15
0.10
0.05
0.00
0
200
400 600 800 1000 1200 1400
Bandwidth of New Services (Mbps)
Fig. 5. Average relative load of Edge IP links.
For low-load range (below 450 Mbps), the algorithms SLF
and OdAASP are performing the same, while SLN is showing
a slight difference in average loads due to choosing the other
paths that may be longer but lighter loaded. We observe no
overloaded IP links within this particular load range in Fig.6.
In the mid-load range (450 − 900 Mbps), we observe that
edge links is getting more traffic as many paths are being
selected by SLN as a result of some edge links are getting
overloaded. Moreover, the edge links are still getting load
in SLF and OdAASP. On the other hand, we observe that
OdAASP is showing a single overloaded connection.
Relative Loads| Core Links
Relative Loads | Edge Links
0.25
# of overloaded Edge links
Type
Best Effort
New Serv.
3.0
SLN
SLF
CSP
OdAASP
0.8
0.6
0.4
0.2
0.0
0
200
400 600 800 1000 1200 1400
Bandwidth of New Services (Mbps)
Fig. 7. Average relative load of Core IP links.
10
# of overloaded Core links
might be used more by one and less used by the other slices.
Furthermore, we will evaluate QoS properties such as the
packet delay and loss of those scenarios.
SLN
SLF
CSP
OdAASP
8
R EFERENCES
6
4
2
0
0
200
400 600 800 1000 1200 1400
Bandwidth of New Services (Mbps)
Fig. 8. Number of overloaded Core IP links.
In the low-load range (500−900 Mbps), the average relative
loads of SLN and SLF show little differences. The interesting
property here is that the values show some bouncing and do
not monotonously grow with the network load as in CSP
and OdAASP where we observe many links get overloaded.
It comes from the slice limitation methods that, after finding
low-loaded links in the corresponding slice, use them until
they get overloaded.
The network resources get exhausted in the high-load
range (over 900 Mbps). Thus all the algorithms cannot avoid
overloads. However, slice limitation algorithms perform better
than CSP and OdAASP again in terms of finding a chain
excluding the links where the slice traffic would be over the
limit.
Thus, we can summarize that the traffic flows are using the
core part of the network rather. All the SCs pass through the
functional connections, which connect the traffic ending points
to the nearest VNF capable nodes and then pass through other
functional connections using core links.
VI. C ONCLUSION
In this paper we have proposed heuristic service chaining
solutions that consider shared slicing and apply a kind of
preservation of network resources for other slices to hold the
QoS expectations. We have consider shared slicing approach
and used them to apply some type of preservation of network
resources for other slices to meet the QoS expectations. We
have shown the importance of network slicing for building
up to ensure the low latency and guaranteed bandwidth for
different services, which are considered to play a critical
role in 5G network. Finally, we have analyzed the proposed
solutions and show their benefits by the numerical results of
a case study. The comparative results have shown that the
proposed solutions perform better when the network load is
moderate.
As further work, we will be focusing on more complex
scenarios by improving the proposed solutions by considering
different allocation mechanisms on each IP link as some links
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