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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
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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
3442 Akriti Jaswal & Dr. Sandeep Kang
Impact Factor (JCC): 8.8746 SCOPUS Indexed Journal NAAS Rating: 3.11
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
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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
3444 Akriti Jaswal & Dr. Sandeep Kang
Impact Factor (JCC): 8.8746 SCOPUS Indexed Journal NAAS Rating: 3.11
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
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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
3446 Akriti Jaswal & Dr. Sandeep Kang
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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 8: Balancing Load via Five Slave Controllers with Ratios 1:3:5:7:9
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Figure 9: Balancing Load via Five Slave Controllers with Ratios 2:4:6:8:10
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
3448 Akriti Jaswal & Dr. Sandeep Kang
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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
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Figure 15: Average Jitter Among Four Controllers
Figure 16: Average throughput Among Five Controllers
Figure 17: Average Response time Among Five Controllers
3450 Akriti Jaswal & Dr. Sandeep Kang
Impact Factor (JCC): 8.8746 SCOPUS Indexed Journal NAAS Rating: 3.11
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
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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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