Effect of Slice Priority for admission control in 5G Wireless Networks: A comparison study for two Fuzzy-based systems considering Software-Defined-Networks

Abstract

The Fifth Generation (5G) networks are expected to be flexible to satisfy demands of high-quality services such as high speed, low latencies and enhanced reliability from customers. Also, the rapidly increasing amount of user devices and high user’s requests becomes a problem. Thus, the Software-Defined Network (SDN) will be the key function for efficient management and control. To deal with these problems, we propose a Fuzzy-based SDN approach. This paper presents and compares two Fuzzy-based Systems for Admission Control (FBSAC) in 5G wireless networks: FBSAC1 and FBSAC2. The FBSAC1 considers for admission control decision three parameters: Grade of Service (GS), User Request Delay Time (URDT) and Network Slice Size (NSS). In FBSAC2, we consider as an additional parameter the Slice Priority (SP). So, FBSAC2 has four input parameters. The simulation results show that the FBSAC2 is more complex than FBSAC1, but it has a better performance for admission control.

Keywords

5G SDN admission control Network Slicing

1. Introduction

Recently, the growth of wireless technologies and user’s demand of services are increasing rapidly. Especially in 5G networks, there will be billions of new devices with unpredictable traffic patterns which provide high data rates. With the appearance of Internet of Things (IoT), these devices will generate Big Data to the Internet, which will cause network congestion and deteriorate the Quality of Service (QoS) [20,22].

Three different usage scenarios for developing 5G have been identified as enhanced mobile broadband (eMBB), ultra-reliable & low latency communications (URLLC) and massive type communication (mMTC) [1,24,27]. The eMBB is related to human-essential and has enhanced access to multi-media content and services by increasing seamless Quality of Experience (QoE). The URLLC can improve the latency and reliability. The mMTC can support massive connected devices with long battery lifetime. Thus, the 5G network is expected to be better than 4G. The peak data for 5G is expected to be beyond 20 Gbps [7].

In addition, the 5G network will provide users with new experiences such as Ultra High Definition Television (UHDT) on Internet [21] and support a lot of IoT devices with long battery life and high data rates on hotspot areas with high user density. The routing and switching technologies have also been subjected to changes and 5G technology’s coverage area is shorter than 4G in order to provide a good experience for users [6,8,12]. Also, the 5G network can support a variety of industries such as healthcare, automotive, media and entertainment.

In order to meet new network challenges, network administrators have to identify and create new methodologies to enhance the network performance for the new era and SDN is one of them. The SDN can enhance system management efficiency and processing performance [13]. As an example, the mobile handover mechanism with SDN can be used for reducing the delay in handover processing and improve the quality-of-service (QoS) [14,18,26].

The Network Slicing is a new technology that uses SDN and Network Function Virtualization (NFV) for new services over the same physical network [28]. It can provide on-demand customized reliable service in network with limited resource by slicing a physical network into several logical networks [28].

In [2], we presented a Fuzzy-based system for admission control in 5G Wireless Networks considering three parameters: Grade of Service (GS), User Request Delay Time (URDT) and Slice Utility (SU). In this paper, we present and compare two systems: FBSAC1 and FBSAC2. The FBSAC1 considers three parameters and FBSAC2 considers four parameters by adding Slice Priority (SP) as a new parameter.

The rest of the paper is organized as follows. In Section 2 is presented an overview of SDN. In Section 3 is presented the Network Slicing enabling SDN technology. In Section 4, we present application of Fuzzy Logic for admission control. In Section 5, we describe the proposed fuzzy-based systems and their implementation. In Section 6, we explain the simulation results. Finally, conclusions and future work are presented in Section 7.

2. Software-Defined Networks (SDNs)

The SDN is a new networking paradigm that decouples the data plane from control plane in the network. This separation can be flexible and centralized management with a global view of entire network. In Fig. 1 is shown the traditional network and SDN approaches. The traditional networks are hard to manage and control since they rely on physical infrastructure. Network devices must stay connected all the time when user wants to connect to other networks. The processes must be based on the setting of each device, making controlling and operation of the network difficult. In contrast, SDN creates virtualized control plane with intelligent management decisions. Thus, the SDN is easy to manage and provide network software-based services from a centralized control plane. The SDN control plane is managed by SDN controller or cooperating group of SDN controllers. The SDN structure is shown in Fig. 2 [4,15,16,19].

Application Layer builds an abstracted view of the network by collecting information from the controller for decision-making purposes. The types of applications are related to network configuration and management, network monitoring, network troubleshooting, network policies and security.

Control Layer receives instructions or requirements from the Application Layer and control the Infrastructure Layer by using intelligent logic.

Infrastructure Layer receives orders from SDN controller and sends data among them.

The SDN can manage network while enabling new services. In congestion traffic situation, management system can be flexible, allowing users to easily control and adapt resources appropriately throughout the control plane. Mobility management is easier and quicker in forwarding across different wireless technologies (e.g.5G, 4G, Wifi and Wimax). Also, the handover procedure is simple and the delay can be decreased.

Fig. 1.

The comparison of traditional network and SDN.

Fig. 2.

Structure of SDN.

3. 5G Network slicing enabling SDN technology

Network Slice is a technology that divides a single virtualized infrastructure into multiple virtual end-to-end networks which can be called as “Slices” that is configured into virtualized function follow the demand of application to respond to the user’s requests. Each slice is logically independent and doesn’t have any effect on other virtual logical networks [3,5,11].

The SDN Network slicing architecture is provided by the Open Network Foundation (ONF). In SDN network environment, the main components of SDN architecture are resources and control. In Fig. 3, the SDN controller dynamically manages network slice by using a set of policies and grouping slices that belong to the same context [23]. The ONF SDN Network Slicing architecture has the following components.

Client context: It has all information that the controller needs to communicate with a client. The components of client context are client support and virtual resources for responding to end user’s requests.

Client support: It contains support information of client operation.

Resource group: It contains the customized view of all resources that the controller offers to client based on service demands and facility.

Server context: It has all information that the controller needs to interact with a set of underlying resources.

Administrator: The administrator configures all controllers, servers and client context including installation of their associated policies.

Application/SDN controller: This controller can control the slice by using server context.

Fig. 3.

ONF SDN network slicing architecture.

4. Outline of Fuzzy Logic

A Fuzzy Logic (FL) system is a nonlinear mapping of an input data vector into a scalar output, which is able to simultaneously handle numerical data and linguistic knowledge. The FL can deal with statements which may be true, false or intermediate truth-value. These statements are impossible to quantify using traditional mathematics. The FL system is used in many controlling applications such as aircraft control (Rockwell Corp.), Sendai subway operation (Hitachi), and TV picture adjustment (Sony) [10,17,25].

Fig. 4.

FLC structure.

In Fig. 4 is shown Fuzzy Logic Controller (FLC) structure, which contains four components: fuzzifier, inference engine, fuzzy rule base and defuzzifier.

Fuzzifier is needed for combining the crisp values with rules which are linguistic variables and have fuzzy sets associated with them.

The Rules may be provided by on expert or can be extracted from numerical data. In engineering case, the rules are expressed as a collection of IF-THEN statements.

The Inference Engine infers fuzzy output by considering fuzzified input values and fuzzy rules.

The Defuzzifier maps output set into crisp numbers.

4.1. Linguistic variables

A concept that plays a central role in the application of FL is that of a linguistic variable. The linguistic variables may be viewed as a form of data compression. One linguistic variable may represent many numerical variables. It is suggestive to refer to this form of data compression as granulation.

The same effect can be achieved by conventional quantization, but in the case of quantization, the values are intervals, whereas in the case of granulation the values are overlapping fuzzy sets. The advantages of granulation over quantization are as follows:

it is more general;

it mimics the way in which humans interpret linguistic values;

the transition from one linguistic value to a contiguous linguistic value is gradual rather than abrupt, resulting in continuity and robustness.

For example, let Temperature (T) be interpreted as a linguistic variable. It can be decomposed into a set of Terms: T (Temperature) = {Freezing, Cold, Warm, Hot, Blazing}. Each term is characterised by fuzzy sets which can be interpreted, for instance, “freezing” as a temperature below 0°C, “Cold” as a temperature close to 10°C.

4.2. Fuzzy control rules

Fuzzy control rules are usually written in the form “IF x is S THEN y is T” where x and y are linguistic variables that are expressed by S and T, which are fuzzy sets. The x is the control (input) variable and y is the solution (output) variable. This rule is called Fuzzy control rule. The form “IF … THEN” is called a conditional sentence. It consists of “IF” which is called the antecedent and “THEN” is called the consequent.

4.3. Defuzzification methods

There are many defuzzification methods as follows:

The Centroid Method;

Tsukamoto’s Defuzzification Method;

The Center of Are (COA) Method;

The Mean of Maximum (MOM) Method;

Defuzzification when Output of Rules are Function of Their Inputs.

5. Proposed Fuzzy-based Systems

In this work, we use FL to implement the proposed systems. In Fig. 5, we show the overview of our proposed approach. Each evolve Base Station (eBS) will receive controlling orders from SDN controller and they can communicate and send data with User Equipment (UE). On the other hand, the SDN controller will collect all the data about network traffic status and controlling eBS by using the proposed fuzzy-based approach. The SDN controller will be a communicating bridge between eBS and 5G core network. The proposed system is called Fuzzy-based System for Admission Control (FBSAC). We present two systems: FBSAC1 and FBSAC2. The structures of FBSAC1 and FBSAC2 are shown in Fig. 6(a) and Fig. 6(b), respectively. The FBSAC1 considers three input parameters: Grade of Service (GS), User Request Delay Time (URDT), Network Slice Size (NSS) and the output parameter is Admission Decision (AD). In FBSAC2, we consider Slice Priority (SP) as a new parameter.

Fig. 5.

Proposed system overview.

Fig. 6.

Proposed system structures.

Grade of Service (GS): The GS parameter is related with the user priority. When the user priority is high, the possibility to accept the request of a mobile device will increase.

User Request Delay Time (URDT): The URDT is the waiting time of a user request in a buffer. When the user waiting time is long, the user’s request will have to be served fast. Otherwise, the QoE will decrease.

Network Slice Size (NSS): The NSS is the bandwidth of a slice. When NSS value is high, the possibility to accept a connection request is high.

Slice Priority (SP): Each slice has its own priority which is defined by the agreement between the slice owner and the network provider. When slice priority is low, the acceptance possibility will decrease and the new device should be admitted to a new slice.

Admission Decision (AD): By AD is decided whether to accept or reject a connection request.

Fig. 7.

Membership functions.

Fig. 8.

Triangular and trapezoidal membership functions.

The membership functions are shown in Fig. 7. We use triangular and trapezoidal membership functions as shown in Fig. 8 because they are more suitable for real-time operations. We explain the design of FLC in following.

We use three input parameters for FBSAC1 and four input parameters for FBSAC2:

Grade of Service (GS);

User Request Delay Time (URDT);

Network Slice Size(NSS);

Slice Priority (SP).

The term sets for each input linguistic parameter are defined respectively as shown in Table 1.

\begin{array}{l} T (GS) = Bad (Ba), Moderate (Mo), Good (Gd) \\ T (URDT) = Short (Sh), Medium (Me), Long (Lo) \\ T (NSS) = Low (Lw), Medium (Md), High (Hg) \\ T (SP) = Low (L), Medium (M), High (H) \end{array}

Table 1

Parameter and their term sets for FBSAC1 and FBSAC2

Parameters	Term set
Grade of Service (GS)	Bad (Ba), Moderate (Mo), Good (Gd)
User Request Delay Time (URDT)	Short (Sh), Medium (Me), Long (Lo)
Network Slice Size (NSS)	Low (Lw), Medium (Md), High (Hg)
Slice Priority (SP)	Low (L), Medium (M), High (H)
Admission Decision (AD)	AD1, AD2, AD3, AD4, AD5, AD6, AD7

The membership function for input parameters are defined as follows. $\begin{array}{l} μ_{Ba} (GS) = g (GS; {Ba}_{0}, {Ba}_{1}, {Ba}_{w 0}, {Ba}_{w 1}) \\ μ_{Mo} (GS) = f (GS; {Mo}_{0}, {Mo}_{w 0}, {Mo}_{w 1}) \\ μ_{Gd} (GS) = g (GS; {Gd}_{0}, {Gd}_{1}, {Gd}_{w 0}, {Gd}_{w 1}) \\ μ_{Sh} (URDT) = g (URDT; {Sh}_{0}, {Sh}_{1}, {Sh}_{w 0}, {Sh}_{w 1}) \\ μ_{Me} (URDT) = f (URDT; {Me}_{0}, {Me}_{w 0}, {Me}_{w 1}) \\ μ_{Lo} (URDT) = g (UDRT; {Lo}_{0}, {Lo}_{1}, {Lo}_{w 0}, {Lo}_{w 1}) \\ μ_{Lw} (NSS) = g (NSS; {Lw}_{0}, {Lw}_{1}, {Lw}_{w 0}, {Lw}_{w 1}) \\ μ_{Md} (NSS) = f (NSS; {Md}_{0}, {Md}_{w 0}, {Md}_{w 1}) \\ μ_{Hg} (NSS) = g (NSS; {Hg}_{0}, {Hg}_{1}, {Hg}_{w 0}, {Hg}_{w 1}) \\ μ_{L} (SP) = g (SP; L_{0}, L_{1}, L_{w 0}, L_{w 1}) \\ μ_{M} (SP) = f (SP; M_{0}, M_{w 0}, M_{w 1}) \\ μ_{H} (SP) = g (SP; H_{0}, H_{1}, H_{w 0}, H_{w 1}) \end{array}$

The output linguistic parameter is Admission Decision (AD).The term set for the output parameter AD is defined as follows. $\begin{array}{l} AD = (\begin{matrix} Admission Decision L 1 \\ Admission Decision L 2 \\ Admission Decision L 3 \\ Admission Decision L 4 \\ Admission Decision L 5 \\ Admission Decision L 6 \\ Admission Decision L 7 \end{matrix}) = (\begin{matrix} AD 1 \\ AD 2 \\ AD 3 \\ AD 4 \\ AD 5 \\ AD 6 \\ AD 7 \end{matrix}) \end{array}$

The membership functions for the output parameter AD are defined as follows. $\begin{array}{l} μ_{AD 1} (AD) = g (AD; AD 1_{0}, AD 1_{1}, AD 1_{w 0}, AD 1_{w 1}) \\ μ_{AD 2} (AD) = f (AD; AD 2_{0}, AD 2_{w 0}, AD 2_{w 1}) \\ μ_{AD 3} (AD) = f (AD; AD 3_{0}, AD 3_{w 0}, AD 3_{w 1}) \\ μ_{AD 4} (AD) = f (AD; AD 4_{0}, AD 4_{w 0}, AD 4_{w 1}) \\ μ_{AD 5} (AD) = f (AD; AD 5_{0}, AD 5_{w 0}, AD 5_{w 1}) \\ μ_{AD 6} (AD) = f (AD; AD 6_{0}, AD 6_{w 0}, AD 6_{w 1}) \\ μ_{AD 7} (AD) = g (AD; AD 7_{0}, AD 7_{1}, AD 7_{w 0}, AD 7_{w 1}) \end{array}$

The Fuzzy Rule Base (FRB) for FBSAC1 and FASAC2 is shown in Table 2 and Table 3, respectively. The FRB is formed by a fuzzy set of dimensions (

| T (AD |) = | T (GS) | \times | T (SP) | \times | T (URDT) | \times | T (NSS) |

), where

| T (x) |

is the number of terms on

T (x)

. The control rules have the form: IF “condition” THEN “control action”. For example, for Rule 1 of FBSAC2: “IF GS is Ba, URDT is Sh, NSS is Lw and SP is L, THEN AD is AD1”.

Table 2

Fuzzy Rule base for FBSAC1

Rule	GS	URDT	NSS	AD
1	Ba	Sh	Lw	AD1
2	Ba	Sh	Md	AD1
3	Ba	Sh	Hg	AD2
4	Ba	Me	Lw	AD1
5	Ba	Me	Md	AD2
6	Ba	Me	Hg	AD4
7	Ba	Lo	Lw	AD2
8	Ba	Lo	Md	AD3
9	Ba	Lo	Hg	AD5
10	Mo	Sh	Lw	AD1
11	Mo	Sh	Md	AD2
12	Mo	Sh	Hg	AD3
13	Mo	Me	Lw	AD2
14	Mo	Me	Md	AD4
15	Mo	Me	Hg	AD5
16	Mo	Lo	Lw	AD3
17	Mo	Lo	Md	AD5
18	Mo	Lo	Hg	AD6
19	Gd	Sh	Lw	AD2
20	Gd	Sh	Md	AD3
21	Gd	Sh	Hg	AD5
22	Gd	Me	Lw	AD3
23	Gd	Me	Md	AD5
24	Gd	Me	Hg	AD6
25	Gd	Lo	Lw	AD4
26	Gd	Lo	Md	AD6
27	Gd	Lo	Hg	AD7

Table 3

Fuzzy Rule base for FBSAC2

Rule	GS	URDT	NSS	SP	AD	Rule	GS	URDT	NSS	SP	AD
1	Ba	Sh	Lw	L	AD1	41	Mo	Me	Md	M	AD4
2	Ba	Sh	Lw	M	AD1	42	Mo	Me	Md	H	AD5
3	Ba	Sh	Lw	H	AD2	43	Mo	Me	Hg	L	AD4
4	Ba	Sh	Md	L	AD1	44	Mo	Me	Hg	M	AD5
5	Ba	Sh	Md	M	AD2	45	Mo	Me	Hg	H	AD6
6	Ba	Sh	Md	H	AD3	46	Mo	Lo	Lw	L	AD3
7	Ba	Sh	Hg	L	AD2	47	Mo	Lo	Lw	M	AD4
8	Ba	Sh	Hg	M	AD3	48	Mo	Lo	Lw	H	AD5
9	Ba	Sh	Hg	H	AD4	49	Mo	Lo	Md	L	AD4
10	Ba	Me	Lw	L	AD1	50	Mo	Lo	Md	M	AD5
11	Ba	Me	Lw	M	AD2	51	Mo	Lo	Md	H	AD6
12	Ba	Me	Lw	H	AD3	52	Mo	Lo	Hg	L	AD5
13	Ba	Me	Md	L	AD2	53	Mo	Lo	Hg	M	AD6
14	Ba	Me	Md	M	AD3	54	Mo	Lo	Hg	H	AD7
15	Ba	Me	Md	H	AD4	55	Gd	Sh	Lw	L	AD2
16	Ba	Me	Hg	L	AD3	56	Gd	Sh	Lw	M	AD3
17	Ba	Me	Hg	M	AD4	57	Gd	Sh	Lw	H	AD4
18	Ba	Me	Hg	H	AD5	58	Gd	Sh	Md	L	AD3
19	Ba	Lo	Lw	L	AD2	59	Gd	Sh	Md	M	AD4
20	Ba	Lo	Lw	M	AD3	60	Gd	Sh	Md	H	AD5
21	Ba	Lo	Lw	H	AD4	61	Gd	Sh	Hg	L	AD4
22	Ba	Lo	Md	L	AD3	62	Gd	Sh	Hg	M	AD5
23	Ba	Lo	Md	M	AD4	63	Gd	Sh	Hg	H	AD6
24	Ba	Lo	Md	H	AD5	64	Gd	Me	Lw	L	AD3
25	Ba	Lo	Hg	L	AD4	65	Gd	Me	Lw	M	AD4
26	Ba	Lo	Hg	M	AD5	66	Gd	Me	Lw	H	AD5
27	Ba	Lo	Hg	H	AD6	67	Gd	Me	Md	L	AD4
28	Mo	Sh	Lw	L	AD1	68	Gd	Me	Md	M	AD5
29	Mo	Sh	Lw	M	AD2	69	Gd	Me	Md	H	AD6
30	Mo	Sh	Lw	H	AD3	70	Gd	Me	Hg	L	AD5
31	Mo	Sh	Md	L	AD2	71	Gd	Me	Hg	M	AD6
32	Mo	Sh	Md	M	AD3	72	Gd	Me	Hg	H	AD7
33	Mo	Sh	Md	H	AD4	73	Gd	Lo	Lw	L	AD4
34	Mo	Sh	Hg	L	AD3	74	Gd	Lo	Lw	M	AD5
35	Mo	Sh	Hg	M	AD4	75	Gd	Lo	Lw	H	AD6
36	Mo	Sh	Hg	H	AD5	76	Gd	Lo	Md	L	AD5
37	Mo	Me	Lw	L	AD2	77	Gd	Lo	Md	M	AD6
38	Mo	Me	Lw	M	AD3	78	Gd	Lo	Md	H	AD7
39	Mo	Me	Lw	H	AD4	79	Gd	Lo	Hg	L	AD6
40	Mo	Me	Md	L	AD3	80	Gd	Lo	Hg	M	AD7
						81	Gd	Lo	Hg	H	AD7

Fig. 9.

Simulation results of FBSAC1.

6. Simulation results

In this section, we present the simulation results. The simulations are carried out in a Linux Ubuntu OS computer with these specifications: RAM (8 GB), CPU i5 (3.2 GHz × 4) and SSD (650 GB). For simulation, we used our implemented FuzzyC system. The FuzzyC is a simulation system written in C language and equipped with Fuzzy library [9].

The simulation results for FBSAC1 are shown in Fig. 9. We show the relation between AD and NSS for different URDT and GS values. In Fig. 9(a), we consider the GS value 0.1. We change the NSS from 0 to 1 and the URDT value from 0.1 to 0.9. That AD is increasing when the NSS is increased. When the URDT is 0.9, the AD is higher compared with other UDRT values. In Fig. 9(b), we increase the value of GS to 0.5. We see that all AD values are increased for different URDT values. In Fig. 9(c), we increase the value of GS to 0.9. We see that the AD value is increased much more compared with results in Fig. 9(a) and Fig. 9(b).

The simulation results for FBSAC2 are shown Fig. 10, Fig. 11 and Fig. 12. In Fig. 10(a), when NSS is 0.9, we see that AD is increased 15%, when URDT increases from 0.1 to 0.5 and from 0.5 to 0.9, respectively. In Fig. 10(b), we increased the SP to 0.5. We see that AD is increased with the increase of the SP value. In Fig. 10(c), the SP value is increased to 0.9. We found that the AD value is increased much more. We compare Fig. 10(a) with Fig. 10(c) to see how SP has affected AD. When URDT = 0.1 and NSS = 0.9, AD is increased 30% by increasing SP from 0.1 to 0.9. This is because higher NSS values mean higher values of slice bandwidth, so the acceptance possibility is increased.

When we increase GS value form 0.1 to 0.5 in Fig. 10 to Fig. 11, the AD values are increased. We see that in Fig. 11(b) for URDT = 0.9, AD values are higher than 0.5. This means that the system has enough bandwidth for accepting new device even delay time value is high. In Fig. 12, we increase the GS value to 0.9. We see that the AD value is increased much more compared with results in Fig. 10 and Fig. 11. We see that when the values of NSS, URDT and SP parameters are increased, the AD also increases.

Fig. 10.

Simulation results for FBSAC2 (GS = 0.1).

Fig. 11.

Simulation results for FBSAC2 (GS = 0.5).

Fig. 12.

Simulation results for FBSAC2 (GS = 0.9).

7. Conclusions and future work

In this paper, we proposed and implemented two admission control systems for 5G Wireless Networks by considering Fuzzy Logic and SDN approaches. For making the admission control FBSAC1 considers three input parameters: GS, URDT and NSS. These parameters combined with Slice Priority were considered as input parameters for FBSAC2. The proposed systems are evaluated by computer simulations. From the simulations results, we conclude that NSS, GS, URDT and SP parameters are increased, the AD parameter is increased, This means that the acceptance possibility will be high. Comparing FBSAC1 and FBSAC2, we conclude that FBSAC2 is more complex than FBSAC1, but it has a better performance for admission control because consider also the SP parameter.

In the future, we would like to consider other parameters and make extensive simulations to evaluate the proposed approach.

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