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LOAD BALANCING IN SOFTWARE-DEFINED NETWORKS USING ADAPTIVE

GENERIC MASTER AND SLAVE ARCHITECTURE

AKRITI JASWAL1 & Dr. SANDEEP KANG

2

1Research Scholar, Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, India,

2Professor, Department of Computer Science and Engineering, Chandigarh University, Gharuan, Mohali, India

ABSTRACT

Fault Tolerance is a major and integral parameter of network strength and flexibility. The systems and mechanisms that

follow fault tolerance are expected to make sure the reliability, availability and flexibility of a network at a very high level

at several platforms. Introductions of Software-Defined Networking (SDN) has open new ways to develop new layouts,

standards, parameters and architectures in the favour of fault tolerance. In this paper, the two architectures are

represented and Fault Tolerance is carried out on these two respective architectures: (1) Centralized master controller

consisting four slave controllers. (2) several slave controllers. The model proposed is called Adaptive Load Balancing

Controller (AGCALB) It balances the load among slave controllers using heuristic algorithm. Tool used for simulation

phase is mininet. Controller taken into account is floodlight controller. Jitter, delay, throughput and response time are

used to check the performance. AGCALB is compared with two existing models : (1) Hyperflow (Kreutz et. al., 2012) and

(2) ECFT (Aly and Al-anasi, 2018). The results obtained are quite promising, the AGCALB throughput is increased by

16%, jitter and delay decreased by 14%, and 15% respectively, and their is a better response of 13%, when compared to

Hyperflow and when compared to ECFT throughput increased by 19%, jitter and delay decreased by 10% and 17%

respectively and response time is better by 15%.

KEYWORDS: SDN, Load Balancer, Load Balancing Algorithms, Advance Generic Controller Adaptive Load Balancing

(AGCALB), Switches & Controller

Received: May 27, 2020; Accepted: Jun 17, 2020; Published: Jun 30, 2020; Paper Id.: IJMPERDJUN2020328

1. INTRODUCTION

These days the biggest problem that web world undergoes is not having better programmability in terms of software

and therefore it is a biggest threat when it comes to update the networks. The problem with previous networks was that

there was no provision for the underlying programming capability, also the algorithms used for the same does not

provide and promise any consistent outcome. With respect to provide network with programmability feature,

software-defined networks (SDN) decouples the data and control plane. However, SDN is a vast network and huge

amount of research has been already carried out in this domain. But bulk of research only focuses on traversing SDN as

a technology that is based on programmability as compared to the aspects of Fault-Tolerance [1-4]. There’s no doubt

that SDN as a research topic is very interesting, but there are always some issues and hustle when it comes to several

concepts of SDN like it’s architecture , planes as well as it’s dealing and communication with its layers with the help of

interfaces

1.1 The Basic Model of Load Balancing Based On Sdn

Conventional server load adjusting system model as appeared in Figure 1, the customer interfaces load adjusting server

Orig

ina

l Article

International Journal of Mechanical and Production

Engineering Research and Development (IJMPERD)

ISSN (P): 2249–6890; ISSN (E): 2249–8001

Vol. 10, Issue 3, Jun 2020, 3439–3454

© TJPRC Pvt. Ltd.

3440 Akriti Jaswal & Dr. Sandeep Kang

Impact Factor (JCC): 8.8746 SCOPUS Indexed Journal NAAS Rating: 3.11

through the virtual IP (VIP), select the comparing load, adjusting calculation to redistribute the entrance of outer customer to

various backend server. Burden adjusting server must be able to keep the meeting, to be specific all the parcels with a similar

TCP association must be sent to the equivalent backend servers. Conventional burden balancer must have the capacity of

system address interpretation (NAT), which can be acknowledged by adjusting the TCP bundle source IP, source port, goal IP

port just as the goal MAC address, etc, along these lines the customary burden balancer is typically rewarded as layer 2/3

switch hardware

.

Figure 1: The Traditional Network Model[7]

Figure 2: The SDN Load Balance Network Model[7]

The server load adjusting system model dependent on SDN is appeared in Figure.2. In this model the server load

adjusting not, at this point legitimately altered TCP bundle source IP, source port, goal IP port nor the goal MAC address, yet

through the method of dispersed stream table by SDN changes to finish the NAT work. SDN load balancer just used to

produce, adjust, or erase rules of stream table, no longer to advance explicit customer any bundles. SDN load balancer stream

table is for the most part dependent on wellbeing review and the comparing load adjusting calculation

2. RELATED SURVEY

There are some aspects that take a shot at load adjusting of SDN controller, a portion of these are referenced here. In Open

Flow portrayals, the switch setup including stream table passages can be modified just by means of ace c-hub proposed in [4].

This ace c-hub is liable for level the progression of approaching and active messages at different number of changes to build

the adaptability.

For load adjusting in SDN-empowered systems, a procedure called BalanceFlow was proposed in [5], in which a

super controller is conveyed among dispersed controllers to deal with lopsided traffic load issue. A chief controller hub

accumulates the data pretty much all other controller hubs and afterward settle a heap adjusting issue by considering the heap

varieties everything being equal. Impediments of this methodology incorporates (I) execution bargains because of trade of

Load Balancing in Software-Defined Networks using Adaptive Generic Master and Slave Architecture 3441


successive control messages and restricted assets like memory, data transfer capacity and CPU power (ii) load data is acquired

with postpones which don't depict the genuine burden conditions, because of two system transmissions (sending orders and

gathering burdens) and (iii) Entire burden adjusting activity can be down if focal controller breakdown

H. Kim et. al in 2012 presented CORONET, a system that recovers from link failures that are multiple in

corresponding data plane. A prototype is described whose implementation is based on NOX controller using Mininet.The

ultimate goal is to implement a fault tolerant architecture that can recover rapidly from faults and scale to large network

size[27].

Liran S. et.al in 2016 proposed a system to overcome fault using backup controller that operates on single domain..

The main elements are the mechanisms that are local. The first one uses VRRP protocol for a virtual controller.The second

mechanism provides information about the controller, mainly about the network and flow decisions. After this a prototype was

implemented based on the Ryu controller using Cbench and Mininet[22].

Tsai. J. et. al in 2016 implemented a protocol for 2D mesh networks using the technique of fault rings. Simulation

results of this showcased that one routing algorithm was implemented that focused on how the fault regions are identified in

the given network on the basis of these routing algorithm[26].

Petroulakis et. al. in 2017 work presented the pattern framework to handle the fault and link failures. It introduces the

pattern in the form of drools in the network. It used the concept of Byzantine Fault Tolerance and Service Function

Chaining[24] for this fault detection[23]

Dynamic and adaptive algorithm (DALB) proposed in [6], empowered all slave SDN controllers for nearby choices

simply like ace controller. This calculation permits adaptability and accessibility of appropriated SDN controllers and need

one system transmission for social event load. Therefore, choice defer diminished on the grounds that all controllers don't

gather the heap data too as often as possible. While thinking about the system assets, coordination of SDN and NFV

acquainted in [7] with upgrade the system convention and capacities programmability. The issues of how to give enough

controllers to fulfill the traffic request, and where to put them, were concentrated in [8, 9, and 10]. The controllers can be

sorted out progressively, where every controller has its own system segments that decide the streams it can serve [11–13], or

in a level way where every controller can serve a wide range of approaching solicitations [14–16]. Regardless, every switch

needs an essential controller (it can likewise have more, as optional/excess). In many systems N >> M, where N is the quantity

of switches and M is the quantity of controllers, every controller has a lot of switches that are connected to it. The dynamic

solicitations rate from switches can make a bottleneck at certain controllers in light of the fact that every controller has

restricted handling capacities. Consequently, if the quantity of switches mentioned is excessively huge, the solicitations

should hold up in the line before being prepared by the controller which will cause long reaction times for the switches. To

forestall the previously mentioned issue, switches are powerfully reassigned to controllers as per their solicitation rates [17,

18]. This accomplishes a harmony between the heaps that the controllers have.

3. PROPOSED LOAD BALANCER ALGORITHM

The proportion is resolved on numerous components, for example, the present outstanding task at hand and the reaction time.

Algorithm 1 shows how the controller appropriates the heap among the related four accessible slave controllers dependent on

a given foreordained proportion DRatioA and DRatioB. The ace controller gives these qualities as per the outstanding task at



hand list. Both Algorithm 1 and Algorithm 2 utilize 100 solicitations to test the heap adjusting instrument and ensure the heap

is equitably disseminated by the ideal proportion. A similar calculation despite everything holds for bigger number of

solicitations using five slave controllers. The calculations are tried for 100 and 1,000 solicitations. Broad outcomes with large

number of solicitations are excluded from the paper because of absence of room proportion. The calculation calls a capacity to

get the position of least of three qualities. Calculation 2 figures base worth, which is level of the remaining task at hand at an

offered change to the general outstanding burden. The three different switches are working in a similar manner. The slave

controller that limits the distinction of its proportion with the objective proportion takes the pending remaining task at hand.

This is rehashed until all switches are deployed to the proper slave controller to rebalance. Slave controllers send their

remaining task at hand occasionally to the ace controller each time span T. Ace controller sorts the slave controllers list in a

rising request as indicated by their remaining burdens.

Algorithm 1

void AG()

{

aplusb = a+b;

DRratioB= a/ aplusb;

DRratioA =b /aplusyb;

a=0; b=0;

while(c>0)

{

if |((a+1)/(aplusy+1)-DRratioA|

<|(b+1)/(aplusy+1)-DRratioB|

a++;

else b++;

aplusb=a+b;

printf("\n%d\t%d", a, b);

c--;

} }

Algorithm 2

int AGCALBN (int N)

{ //Assume n is the total no. of choices available

TRatio=∑N i CR



for (i=0; i<choices;i++)

DR =R /TR

ComputedR = [CR +1]/[TotalRatio+1]

For each available choice “i”,

compute the minimum “Value” for

Value=ComputedR –DR

}

Choice=i;//i that gave the min Value

return Choice

3.1 Master-Slave Controller Architecture

Generally, in master slave controller there is only one master controller and it governs the communication of entire network ,

by dispersing the load to its corresponding slave controllers.

It generally reduces the load because it uses various distributed and shortest switching path algorithms and disperses

the load in the network.

When the master controller i.e floodlight faces load that is hard to handle to eventually disperses the load to it’s slave

controllers that are four in number. These four controllers eventually disperse the load between them and handle the network

value without resulting any harm to it’s nodes or switches mainly, there are 500 switches connected to these controllers. The

performance measure of these switches is carried out when the load is distributed. The first experimentation is based on this

architecture only

3.2 Slave-Slave Controller Architecture

In this architecture all the slave controllers are communicating with each other without any master or logically centralized

controller, they interact through message passing with each other. In this model, the load is distributed among the slave

controllers only without any logically centralized controller, they themself behave as logically centralized controllers.

An excellent style manual and source of information for science writers is [9].

3.3 ECFT

This stands for Enhanced Controller Fault Tolerant. It also uses a master-slave architecture that is why used for comparison

with our proposed controller. In case of a network failure, the main role of ECFT is to disperse the load amon it’s neighbouring

master controllers, it uses more than one master controller[20].

3.4 Hyperflow

It is basically a model that is a control plane architecture. It can also be referred as virtual control plane, in SDN when there is

too much load on the controller then some technologies have used a way of this hyperflow, that is to implement a same other

control plane just like one in the network and balance the load via that plane, this is the second comparison system for our



proposed technique[19]

4. RESULTS & DISCUSSION ON PROPOSED ALGORITHM

The experimentation uses NS3, mininet 2.7 along with floodlight controller 2.0, OVS version 2.2 implemented in ubuntu

version 18.04. The comparison is done with ECFT and Hyperflow and results obtained are really promising. Simulation is

carried to test different platforms using the AGCALB algorithm discussed above.

(1) The first scenario uses the topology where there are four slave controllers with one master controller.

(2) The second scenario uses similar fashion to the first scenario but only five slave controllers communicating through

message passing

4.1 Experiments using Four Slave Controllers

Table 1: Experiments using Four Slave Controllers

Exp A B C D

1 1 2 3 4

2 1 3 5 7

3 2 4 6 8

4 3 6 9 12

Figure 3: Balancing Load via Four Slave Controllers with Ratios 1:2:3:4



Figure 4: Balancing Load via Four Slave Controllers with Ratios 1:3:5:7

Figure 5: Balancing Load via Slave Controllers with Ratios 2:4:6:8

Figure 6: Balancing load via Four Slave Controllers with Ratios 3:6:9:12



4.2 Experiments with Five Slave Controllers

Table 2: Experiments with Five Slave Controllers

Exp A B C D E

1 1 2 3 4 5

2 1 3 5 7 9

3 2 4 6 8 10

4 1 4 7 10

13

5 1 5 8 14

19

Figure 7: Balancing Load via Five Slave Controllers with Ratios 1:2:3:4:5





Figure 10: Balancing load via Five Slave Controllers with Ratios 1:4:7:10:13

Figure 11: Balancing load via Five Slave Controllers with Ratios 1:5:9:14:19



4.3 RESULTS

Figure 12: Average throughput Among Four Controllers

Figure 13: Average Response Time Among Four Controllers

Figure 14: Average Delay Among Four Controllers



Figure 15: Average Jitter Among Four Controllers

Figure 16: Average throughput Among Five Controllers

Figure 17: Average Response time Among Five Controllers



Figure 18: Average jitter among five controllers

Figure 19: Average Delay Among Five Controllers

The next is to calculate the aggregate rates of the parameters so that we can conclude that by what values our

proposed controller is better than ECFT and Hyperflow. This percentage rate is calculated by finding the aggregate mean of

the results which is more clear through the comparison tables below.

Table 3: Comparison Table of Aggregate Rate of Four parameters for Four Slave Controllers

Algorithm

Used Throughput Delay Jitter RST

AGCALB 1.56 0.18 0.41 0.24

ECFT 1.29 0.76 0.82 0.65

Hyperflow 0.91 0.96 1.01 2.9



Table 4: Comparison Table of Aggregate Rate of Four parameters for Five Slave Controllers

Algorithm

Used Throughput Delay Jitter RST

AGCALB 2.108 0.17 0.24 0.48

ECFT 1.094 0.74 0.67 0.69

Hyperflow 0.804 0.90 0.89 0.86

It clearly shows that while comparison of our proposed AGCALB with ECFT and Hyperflow the values of proposed

algorithm increase by 16% and 19% respectively for throughput in AGCALB. For jitter the values are better and decrease by

14% and 10% for ECFT and Hyperflow respectively. With respect to delay the values are better by 15% and 17% for ECFT

and Hyperflow and response time is 13% and 15%

5. CONCLUSIONS

The paper proposes a nonexclusive controller versatile dependent on load adjusting mode. +e proposed model is known as a

Generic Controller Adaptive Load Balancing (AGCALB) model for SDNs. As the quantity of controller expands throughput

increments consecutively when bundle appearance rate more prominent than the limit of floodlight controller throughput

increases significantly. Two calculations are examined in the paper to manage two distinct situations. Ace controller can

appropriate the switches dependent on foreordained proportion as per the outstanding tasks at hand list put away at the ace

controller. Mininet recreation device is used for the experimentation stage. Investigation results were directed utilizing

AGCALB when ace controller is assuming the liability of conveying switches among four controllers as two contextual

analyses with 500 and 1000 switches. Throughput and reaction time measurements are utilized to quantify execution.

AGCALB is contrasted and two reference calculations: (1) HyperFlow [19] and (2) Enhanced Controller Fault Tolerant

(ECFT) [20] and discover improvement. w.r.t each of the four parameters considered including delay and jitter.

APPENDIX

Appendixes, if needed, appear before the acknowledgment.

ACKNOWLEDGEMENT

The preferred spelling of the word “acknowledgment” in American English is without an “e” after the “g.” Use the singular

heading even if you have many acknowledgments. Avoid expressions such as “One of us (S.B.A.) would like to thank ... .”

Instead, write “F. A. Author thanks ... .” Sponsor and financial support acknowledgments are placed in the unnumbered

footnote on the first page.

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