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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Volume. 6 Issue. 3

6J

1 Capacity-aware Key Partitioning Scheme for Heterogeneous Big Data Analytic Engines

999

Muhammad Hanif, Choonhwa Lee
Division of Computer Science and Engineering, Hanyang University, Seoul, Republic of Korea

2 Radio access and Transmission models for universal service

1018

Idriss Saleh BACHAR*, Ahmed Dooguy KORA**, Roger Marcelin FAYE***, Christelle Aupetit-Berthelemot***** Autorité de Régulation des Communications Electroniques et Postes, Ndjamena, TCHAD**É cole Supérieure Multinationale des Télécommunications, Dakar, SENEGAL*** É cole Supérieure Polytechnique, Dakar-Fann, SENEGAL**** Xlim/SRI/Resyst, UMR-CNRS 7252, University of Limoges, FRANCE

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 2017

999

Capacity-aware Key Partitioning Scheme for
Heterogeneous Big Data Analytic Engines
Muhammad Hanif, Choonhwa Lee
Division of Computer Science and Engineering, Hanyang University, Seoul, Republic of Korea
honeykhan@hanyang.ac.kr , lee@hanyang.ac.kr
Abstract—Big data and cloud computing became the centre ofinterest for the past decade. With the increase of data size anddifferent cloud application, the idea of big data analytics become
very popular both in industry and academia. The researchcommunities in industry and academia never stopped trying tocome up with the fast, robust, and fault tolerant analytic
engines. MapReduce becomes one of the popular big data
analytic engine over the past few years. Hadoop is a standard
implementation of MapReduce framework for running
data-intensive applications on the clusters of commodity servers.
By thoroughly studying the framework we find out that the
shuffle phase, all-to-all input data fetching phase in reduce tasksignificantly affect the application performance. There is aproblem of variance in both the intermediate key’s frequencies
and their distribution among data nodes throughout the cluster
in Hadoop’s MapReduce system. This variance in system causes
network overhead which leads to unfairness on the reduce input
among different data nodes in the cluster. Because of the above
problems, applications experience performance degradation due
to shuffle phase of MapReduce applications. We develop a newnovel algorithm; unlike previous systems our algorithm
considers each node’s capabilities as heuristics to decide a better
available trade-off for the locality and fairness in the system. By
comparing with the default Hadoop’s partitioning algorithmand Leen partitioning algorithm: a). In case of 2 million
key-value pairs to process, on the average our approach achieve
better resource utilization by about 19%, and 9%, in that order;b). In case of 3 million key-value pairs to process, our approachachieve near optimal resource utilization by about 15%, and
7%, respectively.
Keyword—Cloud and Distributed Computing, Context-aware
Partitioning, Hadoop MapReduce, Heterogeneous Systems

I. INTRODUCTIONB

IG DATA [1]is getting bigger day by day with theinformation coming from instrumented, steady supply———————————————————————
Manuscript received April 26, 2017. This research work is follow-up of
the invited journal to an accepted eminent conference paper of the 18 thInternational Conference on Advance Communication Technology(ICACT2016). This work is supported by the Basic Science Research
Program through the National Research Foundation of Korea (NRF) fundedby the Ministry of Science, ICT & Future Planning (No.
2017R1A2B4010395).
Muhammad Hanif is with the Department of Computer and Software
Engineering, Hanyang University, Wangsimni-ro 222, Seoul, Republic ofKorea. (E-mail: honeykhan@ hanyang.ac.kr).
Choonhwa Lee is with the Division of Computer Science andEngineering, Hanyang University, Wangsimni-ro 222, Seoul, Republic ofKorea.
(Corresponding author, Phone: +82-2-2220-1268; fax:
82-2-2220-1723; e-mail: lee@ hanyang.ac.kr).

chains transmitting real-time data about the variabilities ineverything from e-trading to weather. Furthermore, cautious
information has in full swing through amorphous digitalchannels like social media, smart phones applications anddifferent IoT devices. This big amount of data has challenges
involve with it: data is big, it is fast, unstructured, hasenormous amount of sources, and contains graphics. Cloudcomputing [2] becomes the interest point for both industry
and academia due to its scalable, distributed and fault tolerant
storage services and applications which have the aptitude tohandle the challenges associated with big data. The dataprocessing of big data in cloud and distributed computing
environment is one of the core delinquents and under the spot
light in research community for a while. MapReduce has
proven to be the most popular implementation ofcomputational processing framework which has the capabilityof supporting distributed storage holding large scale data overthe distributed infrastructure like cloud computing.
Google’s MapReduce [3] programming model is an emerging
data intensive programming model for large scale data
parallel applications including data mining, web indexing,
multilinear subspace learning, business intelligence, andscientific simulations. MapReduce facilitates users with aneasy parallel programming interface in distributed computing
paradigm. It is used for distributed fault tolerance forsupervision of multiple processing nodes in the clusters. Oneof the most significant feature of MapReduce is its highscalability that permits users to process massive amount ofdata in short time. There are numerous fields that benefit from
MapReduce including bioinformatics [4], scientific analysis[5], web data analytics, security [6], and machine learning [7].
Hadoop [8] [9]is a standard open-source implementation of
Google’s MapReduce programming model for processing
large amount of data in parallel. Hadoop was developed
predominantly by Yahoo; where it processes petabyte scaledata on tens of thousands of nodes [10] [11], and has beensuccessfully adopted by several companies including
Amazon, AOL, Facebook, and New York Times. For
example, AOL uses it for running behavioural patternanalytics application which analyses the behavioural patternof their users so as to targeted services on the basis of theirlocation, interest and so on.
The Hadoop system runs on top of the Hadoop Distributed
File System [12], within which data is loaded, partitioned into
splits, and each split replicated across multiple nodes. Data
processing is co-located with data storage: when a file needs
to be processed, the Resource Manager consults a storage

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 2017

metadata service to get the host node for each split, and thensystem schedules a task on that node, so that data locality is
exploited efficiently. The map task processes a data split into
key/value pairs, on which hash partitioning function is
performed, on the appearance of each intermediate key
produced by any running map within the MapReduce system:
hash (Hash code (Intermediate-key) Modulo Reduce-ID)
These hashing results are stored in memory buffers. In the
reduce stage, a reducer takes a partition as input and performs
the user defined reduce function on the partition. The storage
distribution of the hash partition among the nodes affects the
network traffic and the balance of the hash partition size playa significant role in the load-balancing among the reducer
nodes.
This work scrutinizes the problem of variance in both theintermediate key’s frequencies and their distribution among
data nodes throughout the cluster in Hadoop’s MapReduce
system. This variance in system causes network overhead
which leads to unfairness on the reduce input among different
nodes in the cluster. Because of the above problems,applications experience performance degradation due to
network overhead in the shuffle phase of MapReduce kindapplications. The current Hadoop’s default hash partitioning
and Leen [13] partitioning work well in case of the uniform
distribution of the data throughout the cluster in homogeneous
systems. But in case of heterogeneous machines cluster, thesystem performance degrades due to the lack of considerationof heterogeneity of nodes and also the random-ness(non-uniform) in data distribution of data set throughout thecluster.
To alleviate the problems of partitioning and computation
skew, we develop an algorithm which considers the node
heterogeneity (i.e. the capacity of each node in the cluster) as
heuristics to manage the data locality and fairness trade-off inthe system by load-balancing according to the capabilities ofnodes in the cluster. Our algorithm saves the network
bandwidth overindulgence during the copying phase ofintermediate data of MapReduce job along with balancing the
reducers input. It improves the data locality in the individual
nodes by decoupling mappers and reducer tasks, in thismanner having more control on keys dissemination in eachdata node of the cluster.
Contribution of this work includes, Extension of node/locality/fairness-aware execution
model for the partitioning scheme for Hadoop.
 A node-aware or capacity-aware algorithm toascertain data locality and fair key distribution toachieve load-balancing in cluster according to thecapabilities of nodes.
 Automatize the suboptimal trade-off between locality,
load balancing, and fairness.
 Mitigate the partitioning and computation skew andachieve reduction in network overhead in the clusterin comparison to default state of the art partitioning
schemes in heterogeneous environment.
The rest of the paper is organized as follows. Section 2
discusses the motivational background. Section 3 illuminates
the system architecture, while the proposed scheme isdiscussed in section 4. The performance is evaluated in

1000

section 5. Section 6 discusses the related work and the paper
is concluded in section 7.

II. MOTIVATIONAL BACKGROUND
There are different aspects of the Hadoop scheduler whichshould be manipulated for the improvement of existingschedulers and mitigating the problem with those existingschedulers. Our main motivations of this work are someassumptions made by existing schedulers and situations where
Hadoop’s existing schedulers perform worse. In this section,
we will justify our motivation of the work by going throughthe limitations of the previous state of the art approaches anddemonstrating through motivational example by carrying out
a series of experiments to validate the aforementioned
problems in the current Hadoop implementation.
Hadoop's Limitations
Default Hadoop's system makes several implicit
assumptions:i. All nodes in the cluster can perform work at roughly
the same rate i.e. the cluster is consist of
Homogeneous machines.
ii. Tasks progress at a constant rate throughout time.
iii. A task's progress score is evocative of fraction of its
total work that it has done. Specifically, in a
reduce task, the copy, sort and reduce phases eachtake about 1/3 of the total time. Which is not thecase in real life examples, i.e. jobs and tasks canbe of different types such as CPU intensive, IO
intensive, or Memory intensive.
iv. Tasks incline to finish in waves, so a task with a low
progress score is likely a straggler.
v. Tasks in the same category (map or reduce) requireroughly the same amount of work [3].
As we shall see, assumptions 1 and 2 break down in a
virtualized data centre due to heterogeneity of the resources.
Assumptions 3 and 4 can break down in a homogeneous data
centre as well, and may cause Hadoop to perform poorly there
too. In fact, Yahoo disables speculative execution on some
jobs because it degrades performance, and monitors faulty
machines through other means. Facebook disables
speculation for reduce tasks in order to achieve better
performance [14] [15]. Assumption 5 is intrinsic in the
MapReduce paradigm, so we do not address it in this paper.
Leen's Limitations
Leen works well under some conditions and scenarios,
while there are certain situations where Leen cannot work
properly.
i. Leen assumption of uniform distribution of the keys
throughout the cluster’s nodes does not hold in mostof the real world situations, as the real world input
data set are usually distributed and non-uniform.
ii. It does not consider any heterogeneity, which is not thecase in real world system. Almost all the data centresin the industry consists of heterogeneous machines
such as Amazon EC2 [16], Microsoft Azure [17]iii. It does not consider the numbers of keys throughout thecluster in calculation of the FLK, it just consider the
locality on the basis of average numbers of keys i.e.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 20171001

Fig. 1. Illustrate the current hash partitioning. The keys are ordered by appearance while each value represent the frequency of key in the data node.

Fig. 2. Using Leen partitioning scheme, it increases locality as compared to the default Hadoop’s hash partitioning.

mathematical mean value of it.
a. This hurt the load-balancing in the systemespecially when best locality node is slowerone.
The 2nd point of consideration of only homogeneousmachines degrades the performance in both virtualized andnon-virtualized situations. In a non-virtualized data centre,
there may be multiple generations of hardware at the samedata centre as in case of upgrading some system to the newgeneration whereas other remain intact. In a virtualized data
centre where multiple virtual machines run on each physicalhost, such as Amazon EC2, co-location of VMs may cause
heterogeneity. In EC2, co-located VMs use a host’s fullbandwidth when there is no contention and share bandwidth
fairly when there is contention [16].
Motivational Example
As shown in Fig. 1, there are three nodes: Node1, Node2,
and Node3, with nine intermediate keys, ordered by theirinflux during the map tasks execution. For the reference, we
use a similar example of nine keys like Leen [13] algorithm
and use it as a comparative example among the different
partitioning schemes. The sum of the entire nine keys
frequencies is 225 keys, distributed randomly in the cluster ofthree data nodes, which is usually the case in distributed
infrastructure. Also the keys occurrences are wide-ranging
along with the dispersal among the data nodes.
Fig. 1 shows that the key partitioning results using thedefault Hadoop Hash partitioning, which is assigning K1, K4,
and K7 to Node1; K2, K5, and K8 to Node2; whereas K3, K6,
and K9 to Node3. So despite the fact that Node3 has thehighest processing capability, Node1 needs to process 81 outof 225, Node2 needs to process 103 out of 225, and finallyNode3 needs to process 41 out of 225 key-value pairs leading
to non-optimal utilization of the resources. This clarify that it
is scant in case of the partitioning skew in terms of data size
which needs to be shuffled through the system network andbalance distribution of reducer’s input. We discern that thedata size needs to be transmitted through the network in the

shuffle phase is enormous, and the hash partitioning is
inadequate in the presence of partitioning skew. In thisexample, the percentages of keys locally partitioned on eachof the three nodes are 23%, 37% and 33%, respectively. And
the Total Network Traffic is 156 keys out of 225 keys.
According to the processing power of the Nodes in the given
example shown in the Table.1, Node1 process 81 keys-value
pairs in 36 units of time, Node2 process its 103 key-value
pairs in 23 units of time, and Node3 process its 41 assigned
key-value pairs in 6 units of time, which prove the hypothesisof non-optimal utilization resources that Node3 stay idle forabout 30 units of time (36 units for Node1 – 6 units for
Node3). This kind of situation creates different problems likepoor resource utilization and performance degradation
especially in heterogeneous environment.
Leen [13], which is an improvement to the default hashpartitioning of the Hadoop system, performs well in somesituations, specifically, in case of homogeneous cluster. It
performs worse in some situation because it does not consider
the non-uniform distribution of data throughout the data
nodes in the cluster, as well as does not take into account theheterogeneity of the nodes which is the case in most of the realworld scenarios. Continuing the example above, Leen assigns
K5, K6, and K7 to Node1; K1, K4, K8, and K9 to Node2;
whereas K2, and K3 to Node3. Leading to the fact that Node1
needs to process 45 out of 225, Node2 needs to process 94 out
of 225, and finally Node3 needs to process 86 out of 225
key-value pairs leading to a sub-optimal solution which is anenhanced assignment of key-value pairs than the one bydefault hash partitioning scheme. The percentage of keys
locality in the three nodes are 36%, 59%, and 45%,
respectively. The total network traffic is to transfer 150 keys
out of 225 keys, which decreases the numbers of keys transfer
over the network and leads to around 2% improvement overthe hash partitioning as shown in Fig. 2. Bestowing to theprevious calculations, Node1 process 45 keys-value pairs in
20 units of time, Node2 process its 94 key-value pairs in 21
units of time, and Node3 process its 86 assigned key-value
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 2017

1002
TABLE IEXAMPLE OF MEASURING HETEROGENEITY

Node

ExecutionTime

PP-Ratio

Optimal Keys Assignment

Node A 10
1
40%
Node B 20
230%
Node C 30
320%
Node D 40
4
10%*Execution units used are seconds here.

Fig. 3. Proposed scheme system architecture.

pairs in 13 units of time, in which case Node3 stay idle only
for about 8 units of time instead of 30 units as in the case ofdefault hash partitioning (i.e. 21 units for Node2 – 13 units for
Node3). This example shows that the Leen partitioning
algorithm help the system to improve the utilization of thewhole cluster eventually.
By the above reasoning, we have to conclude that theprevious work lack of contemplation of the capacity
awareness of the nodes superintends any opportunities of the
reduction of the network traffic during the shuffle phase of the
MapReduce application execution, in case of heterogeneity inthe cluster. Also the load misbalancing data distribution of
reducer nodes occurs, i.e. 1). Nodes with higher capacity getless amount of data leading to non-optimal utilization of
resources and under loading, 2). Lower capacity nodes getting
more data to process leading to performance degradation,
overloading, and straggler effect.

III. SYSTEM ARCHITECTURE
In this section, we will introduce the system architectureand how the proposed system work in the specified
environment. As mentioned earlier, we decouple the mappers
and reducers in order to achieve more parallelism and keep
track of all the intermediate data keys frequencies anddistribution in the form of capacity-keys frequency table. In
order to meritoriously partition certain input data set of K
keys distributed over N nodes in a cluster, the system need topossiblefind the best available solution in a space ofcombinations. The system achieve it through the proposed
approach which will be explained in the forthcoming section.
The system architecture is consists of a master node and anumber of worker nodes as shown in the Fig. 3, and it works
as subsequent way. The system first run some test tasks on theworker nodes over the cluster which send the results back to
the master node. The master node uses the gather informationof each worker node in the cluster and keep track of theexecution time of each node in the cluster for the jobs run by
the specified node. The master node then estimates theprocessing power ratio using the sample task run results. Thenmaster node constructs node-capacity table which is further
used in the edifice of capacity-keys frequency table. As themaster node already knows the input keys data distribution
over the cluster, the formation of capacity-keys frequency

table take place using the perceptibly known information
required. Then this table is being forwarded to the taskscheduler. The task scheduler schedule different keys todifferent node while taking all the available information into
account, in order to get the sub-optimal trade-off between
fairness, locality and load-balancing. And as a result of thisprocedure, every node in the cluster get the numbers of tasks
and partitions of the input data suitable to their processing
power.

IV. PROPOSED APPROACH
In this section, we will thoroughly explain the proposed
approach in three different subsections. First, we will explain
the details about how to measure the heterogeneity ofdifferent nodes exists within the cluster. Then will move todemonstrating the effectiveness of the proposed approachwith the help of continuing the same example from section 2.
Finally, the details of the mathematical model used in thesystem will be elucidated in the last subsection of this section.
We introduce a new metric NA-FLK, which consider thenode heterogeneity in the cluster. There is always a trade-off
between the locality and fairness in heterogeneous systems, so
we use a weightage model where users can choose the ratio
between the locality and fairness. By default the
locality-fairness ratio will be 60% to 40% i.e. 60% weightage
to locality while 40% weightage to fairness. For this we will
use two new properties, <mapred.fairness.weightage> and<mapred.locality.weightage>. With these properties, wegives the administrator the power to decide which of themetric is more valuable to their organization according totheir SLA with users.
A. Measuring Heterogeneity
Afore implementing our partitioning algorithm, we need tomeasure the heterogeneity of Hadoop cluster in terms of dataprocessing speed. Such processing speed highly depends on
data-intensive
applications.
Thus,
heterogeneity
measurements in the cluster may change while executing
different MapReduce data processing applications. We
introduce a metric “processing power ratio”, to measure eachnode’s processing speed in a heterogeneous cluster upon
execution of new application execution, insertion of newnode, or failure of node in the cluster. Processing power ratios
are determined by a sketching procedure conceded out
through following steps.
 The data processing operations of a given MapReduce
application are separately carrying out in each node.
To fairly compare processing speed, we guaranteethat all the nodes process the same amount of input
data. For example, experiment with the same size
input file of 1GB to process by the specified node.
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 20171003

Fig. 4. Motivational Example: Using our Capacity-Aware partitioning scheme outperform both Hadoop’s hash partitioning and Leen partitioning.

 The response time of each node performing the dataprocessing operations will be recorded in an
Array-List data structure.
 Shortest response time is used as a reference tonormalize the response time measurements.
 The normalized processing powers ratios are
employed by the partitioning algorithm of the system
while taking decision of the trade-off between thefairness, load-balancing, and locality.
Measuring Heterogeneity Example: Suppose that there
are four heterogeneous nodes: Node A, B, C and D, in a
Hadoop MapReduce cluster as shown in table 1. Afterrunning a Hadoop application on each node, one collects the
response time of the application on node A, B, C and D is 10,
20, 30 and 40 seconds, respectively. The response time of theapplication on node A is the shortest. Therefore, theprocessing power ratio of node A with respect to thisapplication is set to 1, which becomes a reference used todetermine processing power ratios of node B, C and D. Thus,
the processing power ratios of node B, C and D are 2, 3 and 4,
respectively. Recall that the processing power capacity ofeach node is quite stable with respect to any specified Hadoop
analytic application. Hence, the processing power ratios arefree of input file sizes. Table I shows the response times,
processing power ratios, and optimal keys assignment
percentage for each node in the cluster. As we can grasp, theoptimal keys assignment percentage with the value of 40% for
Node A is the highest, 30% for Node B, 20% for Node C, and
10% for Node D, so the scheduler with the suboptimal
solution will get the nearest possible values for each node in
the cluster leading to a load-balanced cluster.
B. Partitioning Example
Continuing with section 2-C motivational examples, where
the total network traffic was high and the locality was lower
than expected. Our proposed Capacity-aware scheme is verymuch appropriate for the practical scenarios because it covermost of the drawbacks of previously developed schemes. This
scheme can work in case of diverse non-uniformly distributed
data over the nodes, and also in case of heterogonousmachines in the system. Continuing with the motivational
example, as shown in Fig. 4, the proposed scheme NoLFA
assigns K7 to Node1; K1, K4, K8, and K9 to Node2; whereas
K2, K3, K5, and K6 to Node3. Prominently leading towards
the datum that Node1 needs to process 5 out of 225, Node2
needs to process 94 out of 225, and Node3 needs to process
126 out of 225 key-value pairs, which give us a near-optimal
solution of assignment of key-value pairs. The percentage ofkeys locality in the three nodes are 2%, 59%, and 60%,
respectively. The total network traffic is to transfer 138 keys
out of 225 keys, which decreases the numbers of keys transfer
over the network and leads to around 8% improvement overthe hash partitioning scheme. According to the processing
power of the Nodes in the given example, Node1 process 5
keys-value pairs in 2 units of time, Node2 process its 94
key-value pairs in 21 units of time, and Node3 process its 126
assigned key-value pairs in 18 units of time. Thus, NoLFA
achieves better load-balancing according to the capabilities of
the nodes in the system.
C. Mathematical Model
Our proposed algorithm introduces heterogeneity
awareness into the default Hadoop scheduling system byaltering the hash key partitioning scheme while taking localityof data and fairness into account. For the effective partitioningof data set of K keys distributed over N nodes in cluster, there
will be best possible solution in K^N solutions. To findsuitable solution of all these possible ways, we use processing
power or capabilities of nodes as heuristic in the proposed
scheme. We keep in mind that for the best solution, we need tofind a good trade-off between the locality of keys-value pairs,load-balancing, and fairness to the reduce nodes throughout
the cluster. After estimating the processing power of thenodes, we need to find the optimal load for the reducers
depending on the numbers of reducers according to the SLA
based configuration of the cluster.
To calculate the suboptimal load for each reducer in thecluster, we need to use the optimal load percentage table withtotal data set,
For the locality of keys in a specified node, we use thefrequencies of keys partitioned to that node divided by the
optimal load for the specified node (instead of just theathematic mean of all, which is wrong in most practical world
applications).

As we can see in the above equation, the locality of each node
j is the ration of partitioned keys to the Optimal Load from thealready calculated table. The best locality node is usually thenode which contains maximum frequencies of a key, and that
key is partitioned to that node. The fairness in the system
could be calculated as follows,

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 2017

Fig. 5. Percentage of total network traffic generated in the cluster.

points to the frequency of key in the data node
where
, and
represents the optimal load on
reducer according to their computing power. The best locality
indicates partitioning to the data node
which has the
maximum frequencies for the key. The total network traffic in
the cluster can be calculated as,With this formula, we can get an educated guess of the
network overhead in the cluster with the combined effect ofboth the total intermediate data and each node assessed
locality.

V. PERFORMANCE EVALUATION
To evaluate the performance of the proposed algorithm, we
have designed and execute certain set of experiments withdifferent variations of keys and frequencies distribution. The
experimental results shows that the our proposed approach
NoLFA algorithm over-run Leen and Hash partitioning bydecreasing the total network traffic in the cluster as shown in
the Fig. 5. Hash partitioning is the default partitioning schemeof the Hadoop data processing framework, which on the
average generates around 70% of the cluster’s network traffic,
whereas Leen improve it and crafts around 67% of network
traffic in the cluster on the average. And our algorithm
NoLFA outperforms both of the above partitioning scheme byachieving on average better results, and creates around 61%of the total network traffic. This is because of the fact that
NoLFA considers the capacity of each machine/node in the1004cluster while taking the decision about the partitioning ofdifferent keys to different nodes in the cluster.
The second set of experiments focus on the load-balancing
problem in the Hadoop scheduling systems. As Fig. 6 showsthat the processing power of three different nodes, the desired
optimal load-balancing for all three nodes, and the loadbalancing achieved by each partitioning scheme includinghash partitioning, Leen, and NoLFA. From the domino effect,
it is clear that there is a trade-off between load balancing andlocality of key-values pairs throughout the Hadoop’s cluster.
So the outcome shows that the NoLFA perform better inselecting the trade-off between load balancing and locality
because it considers the heterogeneity of nodes in cluster
whereas others does not consider any such heuristics andassign on the basis of static decided values.
Through better load balancing ability of NoLFA using
node computing power as heuristics, it attains decrease in theexecution time of the overall application. Fig. 7 shows thenormalized execution time of each partitioning scheme
designed for this set of experiments i.e. Hadoop’s default hashpartitioning scheme, Leen, and NoLFA. For the normalization
effect, we use NoLFA as base for the calculation. Leen isabout 0.22X slower on average as compare to our algorithm
whereas Hash partitioning takes approximately 0.6X times
extra time as compare to our NoLFA partitioning algorithm’s
execution time. With the elucidation of Fig. 8, we illustrate
the average resource utilization of cluster resources by
Hadoop, Leen, and NoLFA, respectively. X-Axis showsdifferent schemes such as Hadoop, Leen, and NoLFA. Y-Axis
shows the average percentage of cluster resource utilization
by different schemes. Whereas Z-Axis shows the change inthe data size in units of numbers of key-values pairs processedin each case study. As we can surmise from Fig. 8, the blue
bars at the front represents the state of affairs when thenumbers of key-values pairs are 2 million. The red bars in theback represents the results in case of 3 million key-value pairs
been processed by each scheme. We can deduct from thesecase studies that Leen and NoLFA keep the trend ofoutperforming the default Hadoop partitioning scheme inboth cases, as the numbers of key-values pairs increased in theexperiment. The utilization increases with the number of
key-value pair increases until the saturation of data to thenodes in the cluster.
Finally, Fig. 9 signposts the network traffic overhead in all
six instance of the experiments for the Hadoop with number of
key-value pairs to process set to 2 million and three million;

Fig. 6. Trade-off between Load balancing and Fairness.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 20171005

instance of experiment.
With the above reasoning, we claim that it is clear thatcapacity awareness play an important role in selection of thepartitioning different keys to different nodes in the cluster,and has a positive influence on the overall performance andnear optimal utilization of the cluster.
VI. RELATED WORK

Fig. 7. Normalized execution time with NoFLA as base for normalization.

Fig. 8. Average performance gain of Hadoop, Leen, and NoLFA. Results arenormalized according the number of key-value pairs processed by eachscheme accordingly.

Fig. 9. Percent Network Traffic Overhead vs Numbers of Key-value pairs.
The upper red bars shows the value for 2 million key-value pairs while thelower blue bar show it for 3 million key-value pairs to process.

for Leen with the number of key-value pairs set to 2 million
and 3 million; and for NoLFA with the key-value pairs set to 2
million and 3 million, accordingly. X-axis shows the average
percent network traffic overhead caused in each instance ofexperiment. Y-axis shows different partitioning techniques
used in the model experiments. The top bottom blue barshows the case when the number of key-values pairs to beprocessed is 3 million, whereas the top red bar represent thecase of 2 million key-value pairs to be processed, accordingly.
The results shows that the network overhead increases as theamount of data need to be processed increases in eachPrevious work aiming to improve the performance of
MapReduce system achieved the desired goal throughvarious approaches including reduction of network crammingby inserting partial data awareness into the shuffle phase,skew mitigation, replica awareness, and network awareness.
Authors in [18] proposed two schemes of pre-fetching andpre-shuffling for communal MapReduce environments.
Pre-fetching use data locality and assign tasks to nearest node
to the data block, whereas pre-shuffling reduce network
overhead of slouching the key-value pairs. Our scheme
NoLFA decouple the mapper and reducer tasks and scan overthe keys frequency table generated upon execution of map
phase and cross reference it with the capacity table createdafter executing the sample jobs on the nodes in the cluster toachieve the goal of partial balanced reduce tasks throughout
the cluster. ShuffleWatcher [19] proposed a multi-tenant
Hadoop scheduler that tries to curtail the network traffic in
shuffle phase while maintaining the particular fairness
constraints of the system. The working principle of the
ShuffleWatcher is on the basis of the following three steps.
First, it limit the intra-job map shuffle according to the
network traffic load. Second, it auspiciously apportion the
map tasks to localize the intermediate data. Finally, it exploit
the confined intermediate data and delayed shuffle to reduce
the network traffic in shuffle phase by favorably scheduling
reduce tasks in nodes crofting the intermediate data. Unlike
ShuffleWatcher, NoLFA take the capacity information ofeach node in the cluster whereas distributing the tasks which
is very helpful in case of the heterogeneity in the cluster.
EC-Cache [20] introduced a load-balanced, low latency
cluster cache via erasure coding to overawed the inadequacyof selective replication. It employs erasure coding throughtwo principles. First, by splitting and erasure coding
individual objects during writes. Second, late binding. These
led to improving load-balancing in the system. Tang et al.
proposed a sampling evaluation to solve the problems ofpartitioning skew and intermediate data locality for the
reduce tasks called Minimum Transmission Cost Reduce
Task Scheduler [21] (MTCRS). They used communication
cost and waiting time of each reduce task as heuristic whereas
deciding which task to assign to which node in the cluster.
Their scheduling algorithm used Average Reservoir Sampling
for the spawning of parameter sizes, and location ofintermediate data partitions for their rummage-salemathematical estimation model. On the other hand, NoLFA
used Random Sampling.
Transferring data over the network is costly and causes
performance degradation more severely in federated clusters.
Kondikoppa et al. [22] introduced a network-aware
scheduling algorithm for Hadoop system which work infederated clusters, improving the map tasks scheduling and
Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 2017consequently tries to abate the network traffic overhead
leading to improved performance gain. NoLFA has different
approach of decoupling the map and reduce tasks androutinize the keys-capacity frequency table to achieve thespecified goal. Locality Aware Reduce Scheduling (LARS)[23] abate data transfer in their proposed grid-enabled
MapReduce framework. Due to heterogeneity awareness ofnodes in the grid, the map data size varies leading to assigning
map tasks associated with different data size to different
worker nodes according to their computation power. The
LARS algorithm will select the nodes with largest region sizeof the intermediate data to be the destination for the reducetasks. NoLFA achieve the desired goal with thefrequency-capacity table.
Another concern is the partitioning skew that ascends due
to an unstable distribution of map output across nodes,
causing a massive size of data input for some reduce tasks
while lesser for others. Centre-of-Gravity (CoG) [24] reducescheduling add locality and skew cognizance to the scheduler.
They allocates the reduce tasks to nodes nearer to nodes
creating the intermediate data for that listed reduce tasks.
SkewReduce [25] was proposed with the intention to dazed
the computation skew in MapReduce systems where thepartition run time depends on the data values as well as input
size. It uses a user defined cost function based optimizer toregulate the partitioning parameterization of input data tocurtail the computational skew. NoLFA only consider thecase where the computational time of an input partition
depends upon the input data size rather than both. LEEN [13]attenuates the partitioning skew and minimalize the transfer ofdata using network through load balancing of the data
distribution among the nodes in the cluster. It also improvethe data locality of MapReduce tasks in the process. Unlike
LEEN, NoLFA work in heterogeneous environment as wellthrough our capacity awareness algorithm. Chen el al. [26]proposed Dynamic Smart Speculative technique to alleviate
the problems with default speculation implementation likeskew, indecorous phase percentage configuration andasynchronous twitch of certain tasks with the cost ofdegradation of performance for batch jobs. Whereas
FP-Hadoop [27] introduces a new phase called intermediate
reduce (IR) to parallelize the reduce task to efficiently tackle
the reduce data skew problem. IR process the blocks ofintermediate data in parallel. NoLFA has a different approachof decoupling the mappers and reducers tasks as introduced in
our previous work [28].

VII. CONCLUSION
Hadoop affords simplified implementation of MapReduce
framework, but its design stances challenges to attain best
performance in application execution due to tightly coupled
shuffle, obstinate scheduling and partitioning skew. In thispaper, we developed an algorithm which takes nodecapabilities as heuristics to achieve better trade-off between
locality and fairness in the Hadoop MapReduce system. It
effectively improves the data locality and by comparing withthe default Hadoop’s partitioning algorithm and Leenpartitioning algorithm, on the average our approach achieves

1006

better performance gain and outperform both of thepreviously mentioned partitioning schemes.

ACKNOWLEDGMENT
This research was supported by Basic Science Research
Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future
Planning (No. 2017R1A2B4010395).
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Syst., vol. 60, pp. 69–84, 2016.
[28] M. Hanif and C. Lee, “An Efficient Key Partitioning Scheme for
Heterogeneous MapReduce Clusters,” Proc. 18th IEEE Int. Conf. Adv.
Commun. Technol., 2016.

Muhammad Hanif was born in Pakistan. He received
his B.S. degrees in computer and software engineeringfrom University of Engineering and Technology(UET), Peshawar, Pakistan in 2012. He is currently
pursuing his MS leading to PhD degree in Computer
Software Engineering at Hanyang University, Seoul,
South Korea. His current research interest includes
Cloud & Distributed Computing, Big Data Analytic
Engines, Stream Processing Frameworks, andDistributed Scheduling.

Choonhwa Lee was born in South Korea. He has
been with the Division of Computer Science andEngineering at Hanyang University, Seoul, South
Korea since 2004, and currently as Professor. He
received his B.S. and M.S. degrees in computerengineering from Seoul National University (SNU),
South Korea, in 1990 and 1992, respectively, and his
Ph.D. degree in computer engineering from the
University of Florida, Gainesville, in 2003. Heworked as senior research engineer at LGIC Ltd from 1992 to 1998. He is amember of IEEE since 2004. His research interests include cloud computing,peer-to-peer and mobile networking and computing, and services computing
technology.

Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 2017

1018

Radio access and Transmission models foruniversal service
Idriss Saleh BACHAR*, Ahmed Dooguy KORA**, Roger Marcelin FAYE***, Christelle
Aupetit-Berthelemot***** Autorité de Régulation des Communications Electroniques et Postes, BP 5808, Ndjamena, TCHAD**École Supérieure Multinationale des Télécommunications, BP 10000, Dakar, SENEGAL*** École Supérieure Polytechnique, BP 5085, Dakar-Fann, SENEGAL**** Xlim/SRI/Resyst, UMR-CNRS 7252, University of Limoges, 16 rue Atlantis 87068 Limoges Cedex, FRANCE
i.bachar@arcep.td, ahmed.kora@esmt.sn, roger.faye@ucad.edu.sn, christelle.aupetit-berthelemot@xlim.fr

Abstract—This paper proposes two mathematical models as adecision tool for the choice of radio access and transmission
solutions adapted to a geographical region through universal
access. The first mathematical model integrates the formalism
related to engineering radio network access in general as well as
financial constraints imposed by the access and universal service
funds. The second mathematical model is the equivalent of thefirst applied to radio transmission systems. Services considered
are voice and data services. This approach has helped to derive
two general expressions set for radio access and radiotransmission technologies. The coverage and capacity
deployment strategy has also been combined to clarify the
optimal implementation based on financial constraints. A casestudy on the Ouaddai and Oura regions in Chad accompanied
by simulations curves for wireless technologies as Wi-Fi,
WiMAX, CDMA, and Open BTS for example has shown theefficiency of such approach.
Keyword— access network model, radio access, radio accessmodel, radio transmission, radio transmission model, universal
access, universal service.

I. INTRODUCTION

I

nformation and Communication Technologies (ICT) are
crucial for the socio-economic development of peoples andcontribute to the emergence of countries.
However, according to geographical membership and/or———————————————————————
Manuscript received on April 11, 2017. This work was supported in partby Chad the Regulatory Authority for Electronic Communications and Posts(ARCEP: Autorité de Régulation des Communications Electroniques et de laPoste) and a follow-up of the invited journal to the outstanding paper of the
18th International Conference on Advanced Communications Technology(ICACT 2016).
Dr. Idriss Saleh BACHAR (corresponding author: +235 66204775;
e-mail: i.bachar@arcep.td) is with the Regulatory Authority for Electronic
Communications and Posts (ARCEP Chad), BP 5808, Ndjamena, Chad. He
is currently the GENERAL DIRECTOR.
Pr Ahmed Dooguy KORA (email: ahmed.kora@esmt.sn) is with Ecole
Superieure Multinationale des Telecommunications, Dakar, Senegal. He is
the Head of Research and Innovation Department.
Pr Roger Marcelin FAYE (email: roger.faye@ucad.edu.sn) is with theElectrical Engineering Department of Ecole Supérieure Polytechnique,
Cheikh Anta Diop University, Dakar, Senegal.
Pr
Christelle
Aupetit-Berthelemot(email:christelle.aupetit-berthelemot@xlim.fr) is with XLIM Laboratory, SRI axis,
Resyst Team, UMR CNRS 7252, University of Limoges, 16 rue Atlantis
87068 Limoges Cedex, France. She is professor at ENSIL.

socio-economic category, the access to ICT is achieved in a
non-egalitarian and disparate manner between peoples. This
technological gap is explained by the fact that neither theneeds for services, nor the purchasing power are the same.
This leads governments to define an investment policy
fostering rural areas in order to fill the gap with dense andprofitable areas. In order to raise tele-density and reduce thedisparities of access, the authorities and regulatory agencies
have introduced the concept of universal access and services[1-4] by dedicating a fund called Universal Service Fund
(FSU).
Considering the convergence and neutrality oftechnologies, communications infrastructures and theassociated services now have a central role in economic
organization [5-9]. In fact, access to these infrastructures andservices depend on the ability of countries and citizens toparticipate in the flow of communications. For that reason,
they must put in place policies which will help to avoid theexclusion of some categories of users or geographic areas
deemed to be of non-profitable nature by the operators.
In this context access and universal service could beconsidered as a major problem. This is to ensure that quality
and price for consumer satisfaction as well as profitability
from the point of view of operators might be taken into
account. In order to respond to this problem, the solution
should come from pairing technical/economic approaches andtaking into account geographical constraints inherent to eachcountry.
Gasmi & Recuero Virto [8] have reviewed the provision
policies of telecommunications services in rural areas fordeveloping countries. They have shown that these policies
differ from those usually applied in advanced countries intheir basic objectives, technological strategies deployment,
the role of the market and institutional environments. Falch &Henten [10] have studied the appropriate policy measures to
put in place in Europe to achieve universal access. They haveshown that it is interesting to implement a combination ofdifferent technologies and strategies. Xavier [11] provides an
overview of the principal issues related to universal accessand the provision of universal service.
Access and universal service are influenced by accessibility
parameters, cost and feasibility of services. To this end, theUAS policy should consider technological innovation as a
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 2017

suitable solution for UAS, covering rural areas and / orserving low-income populations.
In this paper, access radio and transmission technologies
model for universal service are proposed. We haveestablished a model of access network design common toradio technologies as well as transmission which have been
adapted to universal service funds limitations. The number ofbase stations based on the project duration for each radio
technology has been derived. This has made it possible todecide the optimal strategy deployment coupling the radio
coverage for data and voice services. The efficiency of thisapproach is shown thanks to the case study applied to Ouaddai
and Oura regions in Chad.
In the rest of this article Section 2 presents the modelling
approach for determining the different elements of the accessnetwork and profitability model. Section 3 presents the radiomodelling approach for determining the different elements ofthe transmission network and profitability model. Section 4
offers a case study that will highlight on the usefulness of theapproach. Finally, Section 4 concludes this communication.
II.

RADIO ACCESS MODELLING APPROACH

The optimal network modelling approach proposedconsists in determining the optimal network architecture forthe universal access and service in an area. The
methodological approach to obtain this model is based on theaccess network model and that of the transmission network.
The determination of the access network model of an area iscarried out based on the telecommunications engineeringparameters, notably those related to the demographic andfinancial aspects. Thus, through this area data, the population
addressable to the universal access and the bandwidth and thenecessary number of base stations are determined, then theinvestment and operation costs (CAPEX and OPEX) are
assessed in addition the energy related expenses and liabilities
are defined.
The population addressable to the universal service isgiven by the expression:(1)
where P is the population of the target area, PS the
population having access to the universal service and TN theservice penetration rate.
The bandwidth brought back to the addressable population
is determined based on the data rate/subscriber need, the data
rate by user supplied by an access technology and thecontention rate. It is expressed by the following relationship:(2)
where DA represents the data rate per subscriber, PS the
population having access to the universal service and T thecontention rate.
It is worth noting that the data rate/subscriber
simultaneously takes the voice and data into considerationand that the contention rate has to be taken so that, even in themost unfavorable case, the voice minimum output is kept for
the user, which is translated by constraint expressing that theaccess technology has to easily bear the cost of the subscribers
data rate needs. The determination of the necessary number ofbase stations of the access area allies a coverage approachwith a capacity approach in order to avoid the network

1019

oversizing and under-sizing problems. Thus, this number isgiven by the following relationship:
(3)
where

and

with SC the surface to cover and SBSC the surface coveredby a base station, CapBSX the bandwidth base station capacity
this station use rate, mbrought back to a technology X andand n are the coefficients related to the coverage and capacity
needs. From equation (3), the BSX is expressed in function of
m by the following expression:(4)
The total cost of the investment expenses (CAPEX) of atechnology X is given by the expression:
(5)
where
is the investment unit cost of a given
technology X.
As for the total cost of the operation expenses (OPEX) of atechnology X, taking into consideration its reducing
percentage , it is determined by the expression:
(6)
with
, and
where
is the operation unit cost of a base station
at year N brought back to technology X.
As a matter of fact, the operation decreases each year with apositive constant reducing percentage . This decrease is dueto the fact that a piece of equipment is used, some mastery is
acquired which reduces the operation cost as the years go.
The access total cost of a technology X is the sum of total
CAPEX, OPEX total operation over the whole duration of the
project and allied expenses. The allied expenses include theinvestment and operation expenses of the liability
infrastructures as well as those of energy workshops. Taking
into account, on the one hand, that the OPEX cost represents
5% of the CAPEX costs [12] and, on the other hand, that theenergy OPEX cost represents 25% of the CAPEX in energy[12], the investment and operation costs of the liability
infrastructures as well as those of the energy workshops,
while simultaneously integrating the OPEX and CAPEX costs,
are determined by the following expressions:(7)(8)
with ci the liability infrastructure unit cost and ce the energy
unit cost.
Thus, the allied expenses CC are given by the expression:(9)
whereas the access total cost is given by the following
relationship:(10)
Expressing the OPEX summation of the access technology
and using equation (5), it is possible to write:(11)

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 2017

where

and

The following expression is obtained:(12)
Thus, the total cost of the access network is determined by
the following global expression:(13)
In practice, a region or an area is the object of a commercial
launching when the total set of stations are deployed. This
simplifies the expression (13) which becomes:
(14)

(15)
For a given technology, it is possible to establish aconnection between the opex(BSX0) and the capex(BSX ).
Suppose in this case that this connection is α, then expression(16) becomes:(16)
Then, we can finally get BSX expression as follows:1020

where the number of active relay stations NXT is determinedby the ratio of the distance between the operator network (D)over the range of an XT (DXT) radio transmission technology.
Let’s consider that the cost of an active relay station is thesame as that of the main station. The verification of thecapacity provided by XT transmission technology consists inreassuring that the throughput offered by the transmission
technology can withstand the throughput that will be
submitted to it during the entire study period of the project.
This is done based on the following inequality:(19)
The (19) inequality above means that the capacity of thetransmission technology is greater than the sum of the total
off-net voice rate (A5voiceT * α) in addition to the total data
rate in the previous project year of study (A5dataT) to ensurethat the dimensioning is adapted throughout the duration of
study T of the project. The total CAPEX cost of the XT
transmission technology is given by the following formula:(20)
The total CAPEX cost of transmission technology (B2XT) is
calculated by multiplying the number of active relay stations
plus the main station by the CAPEX of a transmission
technology station. The total cost of the OPEX of the XT
transmission technology is given by the following relation:(21)
As for access technology, the OPEX transmission
technology is clean every year. Indeed, the latter is subject toa decrease over the years due to the reduction coefficient ‘‘r’’of the OPEX in transmission. One gets:
(22)

(17)
In a given locality, the access technology X to choose willbe the one that will have the lowest access total cost CTAX
among all the technologies submitted to this algorithm.
This profitability makes it possible to evaluate the total
amount of the subsidy necessary to achieve the project but
also the adequate purchasing power (ARPU) needed to ensure
the population accessibility to the service. The analysis of themodel shows that the whole set of the expenses from which
the subsidy on the project duration is subtracted has to belower than what the operator will earn over the same duration(number of subscribers over the project study duration
multiplied by the ARPU.
III. RADIO TRANSMISSION MODELLING APPROACH
The radio transmission model presents all the equations
that ensure the transmission of information between covered
area and the core of the network through the national
backbone. As with the access model, the elaboration of thetransmission model follows several steps. These steps arepresented through the following lines:
Determining the number of active relay stations to reachthe backbone is estimated with the following formula:(18)

which represents the cost of the OPEX of a transmission
technology XT to year N; the number of transmission stations
is multiplied by the cost of OPEXs in year N. However,
OPEXs are reduced each year due to the progressive controlof the technology. The cost of the OPEX B3XTN is therefore ageometric sequence based on (1-r) with r ≠ 1 and r ≠ 0.
Thus, one can easily determine the sum of the OPEX of theradio transmission technology XT over the duration of the
project as follows:(23)This justifies the expression of the total cost of the OPEXB3XT over the study duration T of the project.
In order for the operator to have access to the Internet, he must
pay an annual cost according to the total data throughput of a
specific year. To actually calculate this cost over the durationof study T of the project, the following expression (24) can be
used:
(24)
where the total cost of Internet access (B3data) is
calculated by multiplying the total data rate over the entire
duration of the project () by the cost per Mbps
which is set by the legislation of a given country (B6data).

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 2017

To allow network users to communicate with subscribers ofanother operator, the operator must pay a cost ofvoice-over-net traffic. The total cost of voice over-net traffic
can be written as :(25)
The total cost of off-net voice traffic is determined by first
calculating off-net voice traffic which is a portion of overall
voice traffic. Then one multiplies the overall voice traffic overthe entire duration of the projectby thepercentage of voice-over-net traffic ( ). The off-net voicetraffic thus obtained is multiplied by the cost per Mbps
(B6voice) which is set by the legislation of a given country.
The total associated transmission cost representing thepassive infrastructure costs (Mat, Shelter, Pylon) as in theaccess model, is given by:(26)
The method applied to determine this associated costs intransmission is similar to that described in access. Actually,
the total cost in passive infrastructure of transmission
technology (CTIPXT) is calculated by multiplying the numberof transmission stations (NXT + 1) by the infrastructure cost ofa transmission station (CUIP) and by [1 + 0.05 * T] to be able
to directly cover the cost of OPEX and CAPEX in passive
infrastructure simultaneously as OPEX in infrastructure
represents 5% of CAPEX in passive infrastructure (CUIP) andthe number of transmission equipment remains the same overtime T study project.

In order to apply and assess the proposed approach, as anillustration, the Ouaddaï (Chad) region is considered. It has anarea of 36,685 km2 including a population of 1 367 166
inhabitants. This area includes three localities with identicalneeds in data rate/inhabitant but the needs in bandwidth differ
from a locality to another. Tables 1 and 2 below sum up the setof collected data.
Table 1 represents the actual data of the Ouaddaï region(Chad) with locality1 situated in city centre, locality 2 situatedin periphery and locality3 situated in rural zone. This
distribution corresponds to the administrative division: thezone corresponds to the region; the locality corresponds to thethree departments which are distributed into communities.
Data

TABLE IOUADDAÏ AREA BASIC DATA (SOURCE ARCEP CHAD)
Locality 1
Locality 2
Locality 3

Area (km2)
Population
Penetration rate
Need inoutput/subscriber(Kbps)
Population Ps

 the population data (geographical area, number of
inhabitants);
 the subscribers park of the set of electronic
communications services; ICT penetration rate; needs in output/subscriber and necessary
bandwidth (BPA).
The analysis of table 1 reveals that according to IUT’s
definition of universal access, it is important to have an output
of 256Kbps to supply the necessary services. For the set of
considered localities, a penetration rate inferior to 45% isrecorded. The penetration of services related to the data is low,which justifies the significant need in bandwidth of thelocalities.
Table 2 below gives the outputs supplied by the different
technologies, the surfaces covered by a base station (BS) of
technology X as well as the maximum capacity of BS’s. It also
shows the CAPEX, OPEX of the base stations by technology
and the cost of liability infrastructures as well as thoseenergy-related.
TABLE IIDATA RELATED TO TECHNOLOGIES
WIFI
WIMAX

Data
Data rate (Surface covered byBS (BScapacity()
CAPEX (
Euro

in
in

IV. CASE STUDY

11 61115 2299 845

852 389

388 448

126 329

43.66

43.85

44.40

256

256

256

371 547

170 755

56 078Addressed
Population (PA)

209 647

95 35031 121

Appropriate
bandwidth() by Mbt/s

53 669 660.67

24 409 480.70

79 67 025.41

1021

Euro
CTA (Euro)

CDMA

54

70

3.1

0.13

200.96

5024

100

30

64

13.71

1 494

68 597.561

2.06

224.0810 289.63

1 524 390.24

1 524 390.24

1 524 390.24

Ci

1

2 003

10 015Ce

7 621.95

25 914,63

85 129.57

After analysing tables 1 and 2, the number of base stations bytechnology is determined following equation (17) andaccording to the following conditions:
 variation of duration T of the project as well as that of
the OPEX coefficient reduction;

 only at the variation of the reducing coefficient ; variation of coefficient m linked to the coverage inaccordance with equation (4).
Figure 1 below represents the number of stations bytechnology according to the variation of the reducing
coefficient and the project duration. This graph makes it
possible to see the evolution of the BSX of the zone, for thesame
and T the number of BSX differs according totechnology X. It is worth noting that if the project duration T
increases, the number of BSX decreases.

This table makes out for each considered locality:
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 2017

Fig. 1. Evolution of the number of base stations by technology according to avariation of duration T and reducing coefficient.

Figure 2 shows the evolution of the base stations of threeaccess technologies (Wi-Fi, WiMAX and CDMA in
accordance with the variation of reducing coefficient r. Basedon the depreciation of the telecommunications equipment, theproject duration T is 5 years. It established that if the duration
T is constant, the variation of the reducing coefficient r has noeffect on the number of BSX. The BSX by technology
practically remains constant even ifvaries.

Fig. 3. Evolution of the number of base stations by technology according them variation.
TABLE IIIOURA AREA BASIC DATA (SOURCE ARCEP CHAD)
Data
Access
Data
Transmission
Space segment
7000
Area (Km2)15 229,0
rental cost for 1
euros/month
Mbps
Local backbone
Rate of growth3,1
147000/Mbps
rental cost
Need inoutput/subscriber
256
Percentage of(Kbps)
off-net voice
5,6
Need for speech rate
traffic (%)(in kbps)
9,1
Population withaccess to the service(voice and data)
Growth rate
subscribers to theservice
Contention rate

Fig. 2. Evolution of the number of base stations by technology in accordancewith coefficient.

Figure 3 shows the evolution of the number of base stations of
three technologies following the deployment approachdirected to coverage and capacity coverage objectives. Whenm varies, it is noted that for a technology X, the needed
number of stations varies significantly. It is worth indicatingthat for the two extreme values (0 and 1) of m, the base station
number decreases very significantly following technology X.
This shows that it is easier to reach the coverage goals thanthose related to the quality of service and capacity. Thus, it isnoted that the CDMA technology allows the coverage
objectives to be reached more easily, the Wi-Fi and WiMAX,
on the other hand, are more efficient for capacity oriented
deployment.

1022

170 756

Distance between
access zone andbackbone (km)

142

12,1

1:97

-

-

To illustrate the combined case of access and transmission,
we present in the last part of this section the results of a realcase of universal access project. The target area is OURA, arural region of Chad which information for the deployment oftechnologies for universal access is presented in Table 3.
The project is studied over a 5-year T-term. Given that it isa rural area, the option considered includes a solar power
equipment as cost effective solution. Passive infrastructures
as Shelter and pylons allow the technologies to reach theirmaximum range.

Fig. 4. Universal service fund grant cost comparison in CFA for a different
access technologies using microwave for transmission.

Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 20171023

constraints of that locality. Also, the simulations showed thatseveral possibilities could be envisaged to remedy theconstraints linked to the development of universal access.
However, the choice of the appropriate solution depends onthe cost C of the technologies to be deployed in an area i
calculated on the basis of its inputs
The results of the simulations of the UAI, orient us towards
combined solutions (access and transmission) thanks to theapplication to the Ouaddaï and Oura areas located in Chadwhich allow the gradual deployment policy, distributed over aduration, of the cheapest cost coefficient network.
Fig. 5. Universal service fund grant cost in CFA comparison for a different
access technologies using Wimax for transmission.

REFERENCES
[1]

[2]

[3]
Fig. 6. Universal service fund grant cost in CFA comparison for a different
access technologies using VSAT system for transmission.

As the penetration rate of service is about 44% with thesefigures, we have planned for this test a projected service
penetration rate of 50%. Several combinations of access andtransmission technologies have been investigated as shown in
Fig.5, 6 and 7. The amount of 655.55 FCFA correspond to
one Euro.
The analysis of these figures shows that the Open
BTS-microwave combination is the most suitable at Ouraregion for this universal access project. The amount of thesubsidy is 10 053 424 Euros. The people of this zone will
have to pay to the operator about 9.12 Euros per year or atleast 0.76 Euro per month to obtain the services. The operator
will make a turnover of 1057810 Euros out of the five (5)years of study of the project.
It should be noted that for the purposes of these tests, it isassumed that the WIMAX and VSAT technologies cantransmit the calculated bandwidths. To enable suchtransmission capacity, new transmission techniques such as
MIMO for WIMAX and large VSAT stations should be used,
given the density of the population, which will lead to evenhigher costs. For VSATs, the case is even more criticalbecause the rental cost of the space segment is immeasurable.
This confirms that VSATs cannot be used for areas with arelatively high population for universal access projects ataffordable cost.
V. CONCLUSION

[4]

[5]
[6]

[7]

[8]

[9]

[10]
[11]
[12]

Idriss Saleh Bachar; Ahmed Dooguy Kora; Roger Marcelin Faye;
Christelle Aupetit-Berthelemot ‘‘Assessing the Radio AccessTechnologies Impact on the Universal Access Index’’, IEEE Access,Vol. 5, 2017, p. 911-917. DOI: 10.1109/ACCESS.2016.2615467.
Zacharia Damoue; Ahmed Dooguy Kora; Ramlatou Diallo; Cheikh
Brahim Rabany; Samuel Ouya, ‘‘Strategies and types of national
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10.1109/IYCE.2015.7180796.
Yu Hsuan Chen; Sherman Lo; Per Enge; Shau Shiun Jan ‘‘Evaluation& comparison of ranging using Universal Access Transceiver (UAT)and 1090 MHz Mode S Extended Squitter (Mode S ES)’’ IEEE/ION
Position, Location and Navigation Symposium - PLANS 2014, p. 915- 925, DOI: 10.1109/PLANS.2014.6851456
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Migration to the Cloud, a Network Performance”, Master Thesis,
School of Information and Communication Technonology, KTH
Royal Institute of Technology, 231 pages, 2012.
Dietrich Hunold; André Noll Barreto; Gerhard P. Fettweis; Michael
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The aim of this paper was to suggest an optimal model todefine the network architecture based on radio technologies
for universal access and services. This approach makes it
possible on the basis of the universal service access fundallocated to each locality to determine the communicationneeds in terms of output, bandwidth and the amount ofequipment needed by technology. To ensure ideal overall
accessibility in an area i, the technical solution envisaged
must integrate the specific economic and geographical
Copyright © 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 3, May 2017
Bachar Saleh Idriss was born in CHAD on
27/09/1984. He got the Ph.D. degree intelecommunication from the University of Limoges,
France, in 2016. He is the GENERAL DIRECTOR of
National School of Information and Communication
Technology from June 05 up to now. From August
31, 2010 to June 05 GENERAL DIRECTOR of theRegulatory Authority for Electronic Communications
and Posts (ARCEP CHAD). From 2010 to May 31,
2011, he was president of the Association of Telecommunications
Regulators of Central Africa (ARTAC) and Vice President of theCommission recast texts Officer (Electronic communications,telecommunications regulation and Postal, cybersecurity, Cybercriminality). His research activities cover low cost access network anduniversal services solution appropriate to Africa.
Dr. BACHAR is member of the “Association pour le Developpement del’Informatique Libre (ADIL) ” and the REPTIL in CHAD.
Ahmed D. KORA was born in Parakou (Benin
Republic) on May 17, 1971 He is graduated in
Physics Sciences in 1998 from “Faculté des Sciences
Techniques” at “Université d’Abomey–Calavi”,
Bénin, where he got his Diplôme d’Etude
Approfondie (DEA) in Material Sciences in 2000. In
2003, he received a Master “Réseaux Télécoms”
degree from “Ecole Superieure Multinationale deTelecommunications” (ESMT) and the Ph.D. degree
in telecommunication from the University of Limoges, France, in 2007.
He is currently with the ESMT and Head of Research and Innovation
Department. His research area covers communications, radio and optical
networks system architecture, universal access, mobile network quality ofservice and quality of experience, low cost IT systems for development, etc.
Prof. KORA is IEEE member and also member of Fiber Optic Association.

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Roger Marcelin FAYE was born in Thiès (Senegal)
on 09/12/1961. He received the engineer degree inElectrical Engineering from ENSUT (Ecole Nationale
Supérieure
UniversitairedeTechnologie)
Dakar-Senegal in 1989. He obtained the MSc in
Control and Modelling Industrial Processes from
Paris 12 Val de Marne University, Paris-France in1994 and his PhD in Automatic Control andOperational Research from University of Toulouse III
Paul Sabatier, Toulouse-France in 1999.
He is currently full Professor at Ecole Supérieure Polytechnique (ESP) andwas the head of Electrical Engineering department at ESP from 2010 to 2012.
He is author and/or co-author of several publications and two books in thefields of Automatic Control. His research activities cover but not limited tomodelling systems and to IT solutions for Africa.
Prof. FAYE is member of the Senegalese society of complex systems.
Christelle Aupetit-Berthelemot was born in
Limoges (France) in 1971. She received the engineer
degree in telecommunication from ENSIL (Ecole
Nationale Supérieure d’Ingénieurs de Limoges
ENSIL-France) in 1995. The same year, she received
the M.S. degree and in 1998, a PhD degree in HighFrequency and Optic Telecommunications from
University of Limoges. She is currently full Professor
and the head of Electronic and Telecommunications
department at ENSIL. Her current research activities concern opticaltelecommunications and microwave photonics. Particularly, her interests arefocused on the study of the impact of the components on the system
performances, integration of digital techniques of signal processing in
optical communication, Radio Over Fiber, and optoelectronic devices
characterization. She has been involved in several Cooperative Projects. She
is author and/or co-author of more than 100 publications. Prof.
AUPETIT-BERTHELEMOT is editor in Chief of J3EA review and memberof Optical French Society (SFO), of french club EEA and GDR SoC²

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