Traffic-aware adaptive server load balancing for softwaredefined networks

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 3, June 2021, pp. 2211~2218
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i3.pp2211-2218  2211
Journal homepage: http://ijece.iaescore.com
Traffic-aware adaptive server load balancing for software
defined networks
C. Fancy1
, M. Pushpalatha2
1
Department of Information Technology, SRM Institute of Science and Technology, Kattankulathur, India
2
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, India
Article Info ABSTRACT
Article history:
Received Mar 25, 2020
Revised Sep 15, 2020
Accepted Oct 9, 2020
Servers in data center networks handle heterogeneous bulk loads. Load
balancing, therefore, plays an important role in optimizing network
bandwidth and minimizing response time. A complete knowledge of the
current network status is needed to provide a stable load in the network. The
process of network status catalog in a traditional network needs additional
processing which increases complexity, whereas, in software defined
networking, the control plane monitors the overall working of the network
continuously. Hence it is decided to propose an efficient load balancing
algorithm that adapts SDN. This paper proposes an efficient algorithm TA-
ASLB-traffic-aware adaptive server load balancing to balance the flows to
the servers in a data center network. It works based on two parameters,
residual bandwidth, and server capacity. It detects the elephant flows and
forwards them towards the optimal server where it can be processed quickly.
It has been tested with the Mininet simulator and gave considerably better
results compared to the existing server load balancing algorithms in the
floodlight controller. After experimentation and analysis, it is understood that
the method provides comparatively better results than the existing load
balancing algorithms.
Keywords:
Data center networks
Floodlight controller
SDN
Server load balancing
This is an open access article under the CC BY-SA license.
Corresponding Author:
C. Fancy
Department of Information Technology
SRM Institute of Science and Technology
Potheri, Kancheepuram, Tamilnadu, 603203-India
Email: fancyc@srmist.edu.in
1. INTRODUCTION
Data center networks (DCN) faces different types of workload especially in 3 aspects like business,
consumer, and entertainment. DCNs usually support multiple paths between any end hosts. DCNs should
handle both the categories of flows-elephant and mice flows [1]. The main aim of load balancing is to
improve the throughput avoiding processing delays in optimal path selection to balance the load. The various
factors influencing load balancing in DCN include the energy of nodes, residual bandwidth, scalability of the
network, types of flows. With the increasing number of devices in the network, managing the network traffic
is becoming very difficult [2]. Avoiding buffer overflows is another major concern. Also, the most important
node may be chosen repeatedly, which leads to its quicker degradation.
Balancing the incoming load among the servers in data center networks can be seen in various
aspects like link load balancing, server load balancing. Few of the delay-sensitive applications include real-
time video and voice. Delay sensitive applications need to maintain the QoS parameters also. The load
balancing algorithm must also improvise the metrics such as scalability, robustness, energy efficiency, etc.

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 11, No. 3, June 2021 : 2211- 2218
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The link utilization varies in the DCN, also the congestion increases in the bottom layers. The type of flow is
another major component that varies dynamically [3, 4]. There are two possibilities of flow, i.e. mice flow
and elephant flow. Usually, there are a lot of mice flows e.g. hello messages, meta-data requests, etc. The
elephant flow takes huge resources but it happens not as frequently as the mice flows. A statistical report says
that the rate of video traffic in a DCN is increasing in a heavily. An efficient way of controlling network
devices is given in paper [5]. The network is partitioned into various sub-domains. Each sub-domain has an
SDN enabled border router that helps in routing to other domains. Here the network management is adaptive.
Because, if the domain size is large, inter-domain management functions were very less compared to a
smaller domain size. With this scenario, effectiveness is achieved in the scalability of bandwidth allocation,
route determination based on current load, and recovery from failures.
The challenges of wireless network virtualization [6] are highly varying traffic, multidimensional
heterogeneity. Besides these drawbacks, efficiency can be achieved in QoS provisioning, resource sharing,
and verification of new techniques before it can be widely deployed. The resource allocation in inter virtual
network must be dynamic. This is due to the changes in service requirements and new requests for the virtual
network.
Mujiono [7] explains the issues in creating a load-balanced aggregation tree for a wireless sensor
network. He analyzed three related problems and proposed an optimal solution. They have prepared a traffic
matrix that helped in analysing the parameters of the topology. The important parameter discussed in their
model is the transmission success ratio for every link in the network. This ratio defines the successful
delivery of the packet. Another two metrics which are used in the proposed model include potential load and
actual load. The techniques used to solve the identified problem are linear relaxation and random rounding.
Upon analysis, it is found that the proposed idea increases the network lifetime.
The paper [8] proposed a myopic algorithm that uses the cost of each link to decide the path. When
a flow arrives, it sends through the path having the minimum cost. They also tested with varying versions of
myopic algorithm and obtained better network efficiency. Benlalia [9] makes use of the greedy round-robin
algorithm.
The challenges of vehicular ad hoc networks (VANETs) [10] such as mobility and scalability affect
the performance of routing protocol. With the help of SDN, a solution for these issues was found. The
proposed system includes the SDN based connectivity aware geographical routing protocol. In the
simulation, the proposed model provided an optimized routing path while the following parameters are
evaluated; i) Determining the traffic density, ii) Tracking the distance, and iii) Estimating the link lifetime.
Thus, load balancing is an important task in any data center. There will be multiple servers in every
DCN, computing continuously for providing seamless connectivity for applications such as WhatsApp,
Facebook, web search, live programs, etc. Hence it is needed to focus on the server load balancing
algorithms.
This paper aims to focus on the server load balancing in a heterogeneous server environment. It also
focuses on how the controller can adaptively choose the algorithms. This adaptivity is included to avoid
additional complexity to the load balancing module. The proposed paper is designed according to the
following protocol. In section 2, we explain the important similar research aspects, section 3 explains the
proposed model, section and section 4 analyses the experimentation done by the authors.
2. RELATED WORKS
A lot of researchers have contributed to the server load balancing research. The selection of the best
routing path is always an expensive work in (DCN) data center networks [11-13]. The major parameters to be
considered while selecting an optimal path are storage resource, bandwidth consumption, and delay in packet
transmission. To improve the cloud gaming experience, the hierarchy process could be used. The process
assured better results, once the routing path for a game session is chosen based on the game type. DCNs are
multilayered topology. The collaboration of SDN based multipath TCP and segment routing [14, 15]
improves the efficient usage of memory resources. Virtualization supports the existence of several networks
in one substrate network. This work could be done by a centralized controller. By this, the resources which
are utilized can be reduced and the service to several clients can be increased.
This method provides an online approach to serve the cloud client requests. The paper [16] proposed
a myopic algorithm that uses the cost of each link to decide the path. When a flow arrives, it sends through
the path having the minimum cost. They also tested with varying versions of myopic algorithm and obtained
better network efficiency. The authors in [17-19] make use of the greedy round-robin algorithm. The
proposed load balancing is based on the flow size. The algorithm is used only for the long flows, hence
reduce the controller's workload. The performance of a switch can be analysed with the help of queuing
models [20, 21]. The challenges of wireless network virtualization [22] are highly varying traffic,

Int J Elec & Comp Eng ISSN: 2088-8708 
Traffic-aware adaptive server load balancing for software defined networks (C. Fancy)
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multidimensional heterogeneity. Besides these drawbacks, efficiency can be achieved in QoS provisioning,
resource sharing, and verification of new techniques before it can be widely deployed. The resource
allocation in inter virtual network must be dynamic. This is due to the changes in service requirements and
new requests for the virtual networks.
In the floodlight controller [23, 24], there are few traditional load balancing algorithms available.
They are RR approach, weight induced RR method, and statistical method. In the weighted round-robin
method, the possible paths to reach the destination are found. Then based on the weights for each of these
paths, the one having the highest weight will be chosen. The statistical method is based on the remaining
bandwidth used. If the remaining bandwidth of a node is higher, then it will be chosen for the transmission.
The main aim of load balancing [25-28] is to improve the throughput avoiding processing delays in
optimal path selection to balance the load. The various factors influencing load balancing in DCN include the
energy of nodes, residual bandwidth, scalability of the network, types of flows. With the increasing number
of devices in the network, managing the network traffic is becoming very difficult. In this paper, we have
devised a traffic-aware server load balancing that works adaptively. It checks the incoming flow type and
categorizes it as short flow and long flow. Differentiating short flow and long flows is a separate research
topic but we have taken few references for differentiating the flows that helped in focusing on our research.
The proposed load balancing is working based on the flow size. The algorithm is used only for the
long flows. The first step is to distinguish the incoming flows into short and long flows, then the adaptive
algorithm. Hence reducing the controller's computational overhead.
3. RESEARCH METHOD
The proposed method consists of three phases traffic monitoring phase, Adaptive decision phase,
server selection phase. In the traffic monitoring phase, the controller monitors the entire topology. The
proposed method takes three important details which are the size of each flow, residual bandwidth in all the
links to the servers, and computational capacity of all the available servers. The second phase is the Adaptive
decision phase. If the flow size is less than a threshold of 10 kB, then it is decided not to use any complex
load balancing algorithm, hence round-robin load balancing is recommended. If the flow size exceeds the
threshold value, then the TA-ASLB method will be followed. The final phase is the server selection phase
with the help of the TA-ASLB method.
The important parameters needed for server load balancing include residual bandwidth and server
capacity. This is identified with the help of a literature survey among various papers. It aims at finding the
nodes having a higher residual bandwidth. The algorithm is explained in Figure 1.
Algorithm 1: TA-ASLB algorithm
Traffic monitoring phase:
Data: Flow in the Switches F= {f1, f2, ... fn}
Data: Capacity of all possible links to reach server L = {L1, L2, ... Ln}
Data: Capacity of the servers in the server pool S = {S1, S2, ... Sn}
Adaptive decision phase:
begin
while packet(i) generated by clients do
for each fi є F do
if Flow size (fi) > Threshold Ts
follow Traffic aware load balancing
else:
follow Round robin load balancing
Server selection phase:
[α] = max (Li)
find the servers Si which are present in this best links
βi = max (Si)
Optimal Server = βi
Figure 1. Traffic aware-adaptive server load balancing [TA-ASLB] method
The TAALB method works with the combination of two parameters, link capacity towards the
server and server characteristics. It aims at finding the nodes having a higher residual bandwidth. The

 ISSN: 2088-8708
2214
residual bandwidth of a link is calculated based on the load that is currently handled by the link. The links
which are carrying a load at any time will be utilizing bandwidths, so they will be having lesser residual
bandwidth.
Then, among them, the nodes will be ordered in the descending order of the server's weight. It
means, a node having higher residual bandwidth and higher weight then. Thus, it shares the load among
nodes based on two parameters, bandwidth, and weight. Thus, the load will be optimally shared among the
nodes.
4. RESULTS AND DISCUSSIONS
The algorithm is implemented with the help of a Mininet simulator. Data center networks are
usually maintained by following the fat-tree topology structure. Hence for this testing, a fat-tree topology of
depth 3 is created to depict the data center network. The network has eight client nodes termed from h1 to h8
and seven distribution layer switches s1 to s7. The experimentation is done in virtual having Ubuntu 14.10
operating system. The network is assumed to have heterogeneous servers. The topology is depicted in the
below diagram mentioned in Figure 2.
Figure 2. DCN sample topology in Mininet
Here the assumption is host 1 and host 8 are clients. Hosts 2, 3, 4, 5, 6, 7 is defined as servers.
Servers are assumed to have different processing capabilities such as 20 core, 16 core, 8 core, and dual-core.
Numerous trials are made on a fat-tree topology, using the existing load balancing methods in floodlight
controller and the proposed algorithm traffic aware-adaptive server load balancing [TA-ASLB] method.
After the various experimentation, the conclusion is derived.
The average waiting time in the queue of a server is defined as the formula in (1).
(1)
Here, the variable wt refers to the waiting time of flow in the queue, r refers to the utilization rate, u
refers to the utilization rate.
The number of entries in the queue can be defined by the following (2).
(2)
In the above equation, nq refers to the average count of flows present in the queue, r refers to the rate at which
the flow is been utilized.

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There is another important part of the algorithm, which is the flow classification. For this
experiment, there is a constant threshold defined for the classification of incoming flows. A predefined value
of 10 Mbps is been set. If the flow comes with a size above this value, should be treated by the proposed
algorithm and they are called as the elephant flows. The incoming flows below this value will be termed as
mice flows and they can be sent to servers in round-robin fashion for processing. Round Robin method aims
at allotting flow requests to each of the servers one by one.
The topology is tried under various working conditions such as TCP packets transmission, UDP
packets transmission. With that, it is possible to measure the parameters such as throughput, packet loss,
jitter, response time (latency), etc. Figures 3 and 4 are working examples for the UDP and TCP packet
transmissions done. Thus, the above figure tells clearly that the Mininet emulator provides TCP packets and
its features. It includes the window size, the port number in which it is listening, the throughput value, and
the time series.
Figure 3. Sample performance evaluation after generating TCP packets
Figure 4. Sample performance evaluation after generating UDP packets
The above figure is a screenshot obtained during our experimentation on the simulated topology.
The iperf is an efficient tool to explore the evaluation parameters of the given network. It even depicts the
various analyses summary related to the packet transmission.
4.1. Throughput
Throughput refers to the rate of successful data transmitted between a source and a destination.
Bandwidth is defined as the amount of possible data that can be transmitted in a link. For this
experimentation, traffic is generated between host 1 and host 7. Host 1 is the client and host 8 is defined as a

 ISSN: 2088-8708
2216
server in our experiment. The throughput of the above-mentioned algorithms is noted for the given topology.
The iperf command is used to obtain the throughput. TCP packets are sent carrying 1024 bytes. The
experiment is repeated with a varying number of parallel requests such as 100, 200, 300, 400, and 500. The
below is a sample screenshot that depicts our experiments. The results are depicted as a graph that is given
below Figure 5.
Figure 5. Throughput result analysis
By the results, it is inferred that the proposed method is giving higher throughput compared to the
traditional algorithms. When number of packets increase, their throughput decreases. The proposed algorithm
outperforms among the other existing load balancing algorithms.
4.1.1. Latency
Latency is defined as the time taken for the data to get transmitted from source to destination and the
receiver processing it. It can also be called as round-trip time. The latency of the traditional, as well as
algorithms, is noted for the given topology. The experiment is repeated with a varying number of packets
such as 100, 200, 300, 400, and 500. This is obtained with the help of the ping command. By the results, it is
inferred that the proposed method is showing lesser delay than the existing methods. The results are depicted
as a graph that is given as Figure 6.
Figure 6. Latency result analysis
5. CONCLUSION AND FUTURE ENHANCEMENTS
The focus of this paper is to perform an efficient server load balancing. The proposed novel
algorithm provided an optimal server selection whenever long flows arrive. The experimentation was done to
understand the importance of the load regulating process in data centers. The first step was to identify the
parameters that influence the delay in data transmission. Next, the type of flows is been considered, because
there can be short-lived flows and long-lived flows in the DCNs. Based on the various types of flows i.e.

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video traffic, message, or image transfer, the delay in data transmission will vary. It is observed from the
distance between the nodes and the influence of a node in the path to destination plays a major role in the
efficient load balancing of flows in an SDN environment. Hence the adaptive load balancing scheme is very
good in reducing the delay and improving the throughput of the network. Also, the adaptive nature of the
algorithm helps the controller to avoid computational complexity in case of short flows.
In the future, this server selection process can be made as an intelligent application working
separately on top of the centralized controller. Because to propose the server selection activities quickly and
also to overcome any failure, the neural network aspect proposes optimal and suboptimal solutions. This
makes the neural network solution as a better option for the future. Another aspect is in the flow
classification. In this paper, we have used it as a static way to split the flows. We have planned to include a
convolutional neural network, to split the incoming flows into a five-scale meter. This can be used to arrive at
a detailing of flows and it could help in the generic priority assignment. Also, dynamicity in threshold setting
could be arrived based on the current situation in the network. With this, a precise model that can generate
that focus on the current state of the machines like, number of flows currently waiting to be processed, the
number of flows handled already, the server capacity, the residual bandwidth in the links connecting to the
servers, the queue length, arrival rate, etc. Focus on these could, even more, improvise the efficiency of the
current load balancing systems.
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BIOGRAPHIES OF AUTHORS
C. Fancy is currently pursuing Doctoral research in the Computer Science Engineering
department, SRM Institute of Science and Technology, Kattankulathur. She is also a Teaching
faculty in the same university. Her research field is Software Defined Networking. She had
accomplished UG and PG courses in the years 2009 and 2011 respectively. She is currently
working with other research aspects such as Cryptography, TCP/IP services, and the Internet of
Things.
M. Pushpalatha is working in SRM Institute of Science and Technology, Kattankulathur. She
holds the Professor designation in Computer Science and Engineering department. She had
completed UG and PG courses in the years 1994 and 2001 respectively. She completed Doctoral
research in the Computer Science Engineering domain, in the year 2014. Her area of interests
include Software Defined Data Center Networks, Middleware services, and Wireless Networks,
Internet of Things, 5G and future networks. She has several publications in reputed journals. She
had presented papers in various national and international conferences. She is currently guiding
research scholars in various domains. She is a professional member of various reputed standard
bodies such as ACM etc. She has guided several UG and PG level student projects.

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