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Ant-based Integrated Traffic Control andManagement in ATM Networks

Sunil V K Gaddam, D K Lobiyal & Manohar Lal

To cite this article: Sunil V K Gaddam, D K Lobiyal & Manohar Lal (2015): Ant-based

Integrated Traffic Control and Management in ATM Networks, IETE Journal of Research, DOI:10.1080/03772063.2014.961978

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Ant-based Integrated Traffic Control andManagement in ATM Networks

Sunil V K Gaddam1

, D K Lobiyal2

and Manohar Lal3

1Department of Computer Science & Engineering, ACET, Allagadda, af liated to Jawaharlal Nehru Technological University Anatapur (JNTUA),Andhra Pradesh, India, 2School of Computer & Systems Sciences (SC & SS), Jawaharlal Nehru University (JNU), New Delhi, India, 3School of

Computer & Information Sciences (SOCIS), Indira Gandhi National Open University (IGNOU), New Delhi, India

ABSTRACT

Asynchronous transfer mode (ATM) is a cell-switching and multiplexing technology that combines the benets of cir-

cuit switching with those of packet switching. Traf c management in ATM is concerned with ensuring that users get

their desired quality of service (QoS). The problem of traf c management is especially dif cult during the periods of

heavy load particularly if the traf c demands cannot be predicted in advance. The issue of traf c control and band-

width management in ATM-based networks is complex due to a mixture of different connection traf c types, QoS

requirements, and time scales. In this paper, we provide an ant-based integrated technique to control the occurrence

of congestion and allocate bandwidth for various traf c services in a prioritized manner. The forward ants collect the

QoS information of the nodes regarding the available bandwidth and the buffer size. Then based on the QoS require-

ments of the various traf c classes, the optimum path is selected based on the observed QoS statistics collected by

the ant agents. Therefore, the ant-based integrated technique provides the network to ef ciently use the traf c, thusproviding an ef cient traf c management for the ATM network. By simulation results, we show that the proposed ant-

based technique outperforms the existing architecture in terms of throughput and delay.

Keywords:

ATM, Asynchronous transfer mode, Traf c management, Ant colony optimization, Attractiveness, Trail update.

1 . INT RODUCT ION

1.1 ATM Network

Asynchronous transfer mode (ATM) is a technology,which has its roots in the advancement of broadbandintegrated services digital network (B-ISDN) from1970s and 1980s. ATM is a high-performance technol-ogy, which provides bandwidth on-demand for seam-less transport of full-motion data, audio, video,animations, and still images in local as well as widerarea environments. More particularly, it is a cell-oriented switching and multiplexing technology thatutilizes xed-length packets to carry diverse traf cservices [1]. According to [2], ATM is dened as “a mul-tiplexing technique in which a transmission capabilityis organized in undedicated slots lled with cells with

respect to each application ’s instantaneous real need

”.That is, ATM is composed of cell-switching and multi-

plexing technology, which provides a combined effectof circuit switching (guaranteed capacity and constanttransmission delay) with those of packet switching(exibility and ef ciency for intermittent traf c).

ATM is intended to accommodate any form of informa-tion including data, voice, video, facsimile, multimedia,and image whether in case of it being in compressed

or uncompressed state. Moreover, every data can besupported along with a very small set of network pro-tocols, regardless of whether the network is metropoli-tan, local or wide area in nature [3]. In ATM, before any

transmission takes place, a virtual circuit sets up theintermediate switches to provide the requested qualityof service (QoS). Thus, it acts as a connection-orientedtechnology. The allocation of bandwidth occurs in a perchannel basis simultaneously allowing other connec-tion to take place through the same virtual path (VP)[4]. ATM technology has been put into operation in avery broad range of networking devices like ATMswitches in workgroup and campus to PC, workstation and server network interface cards, in various multi-plexers, edge switches, backbone switches and all [5].

1.2 Traf c Management

Traf c management is concerned with ensuring thatusers get their desired QoS. The problem is especiallydif cult during periods of heavy load particularly if thetraf c demands cannot be predicted in advance. ATMtechnology supports diverse applications along with their QoS requirements. Also there may be instances orsituations where the network cannot negotiate networkperformances even for their previously established con-nections. These are analysed and provided by the traf c

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control and congestion control techniques, collectivelytermed as traf c management. In traf c management,the traf c control are sets of actions used by the net-work to evade congestion condition and congestion control which refers to the set of actions taken by thenetwork to diminish the intensity, extension, and dura-tion of congestion [6, 7].

1.3 Issues Related to Traf c Management

Traf c management mechanism brings forth thefollowing challenges [8].

Temporarily overload conditions occurring due tothe statistical uctuation of traf c ows.

Malicious users, who deliberately offer more traf c tothe network for economic gain or for obtaining opera-tional advantage with respect to the other users.

Malfunctioning of terminal equipment, leading tounexpected traf c volumes entering the network.

Fault conditions that prevail inside the network. Mixture of different connection traf c types, QoS

requirements, and time scales.

1.4 Problem Identication and Proposed Solution

From the issues related to ATM and with respect to rel-evant available literature, we come across that there ishardly any recent work for resolving the congestion and bandwidth allocation issues of traf c managementin ATM networks. In this proposal, we propose todesign an ant-based technique to allocate the band-width and resolve the congestion. The forward ants col-lect the QoS information of the nodes regarding the

available bandwidth and the buffer size. Then based on the QoS requirements of the various traf c classes, theoptimum path is selected based on the observed QoSstatistics collected by the ant agents.

Therefore, it can be adaptively used for both static anddynamic (bursty) traf cs.

The paper is organized as follows: Section 2 presentsthe related work in the eld. Section 3 presents the basicconcepts of ant colony optimization (ACO) techniques.The proposed traf c management system is explainedin Section 4 and simulation results are given in Section 5. Section 6 gives the conclusion.

2 . R EL AT ED W OR K

Cidon et al. [9] have elaborated the protocols and mech-anism necessary for network bandwidth managementand congestion control. They drew heavily on the les-sons learned from the design and implementation of the plaNET network. However, they believed that mostof the conclusions are general and could be applied to

other high-speed networks, including ATM-basedsystems.

Hong et al. [10] have described an integrated networkmanagement framework for providing point-to-multi-point reservation service (PMRS) in an ATM network.They proposed a point-to-multipoint routing algorithmcomposed of ordering and backtracking procedures,which can nd the best branch point under the complexnetwork topology and can add more destinations to theexisting point-to-multipoint route. There were twomajor issues confronting the network service providerin relation to this service: one was to rapidly con rmthe acceptability of the subscriber’s reservation at sub-scription time and the other was to punctually activatethe reserved point-to-multipoint service. To meet theserequirements, they developed a service provision model and a network resource model of a bandwidth allocation timetable.

Zaim [11] has proposed a new algorithm to design atraf c ow coming from malfunctioning users. Thecongestion control mechanism is proposed for theSSCOP protocol in signalling. The mechanism outper-forms the existing congestion control mechanism which is limited by the number of calls that would be createdsimultaneously. The system is designed to work on ADSL connections signalling over the UNI.

Ogwu et al. [12] have proposed a technique that backedan analytical model for evaluating the cell loss proba-bility of high and low priority cells. This technique wasused for improving the QoS, which is guaranteed in an

ATM network. Here, rejection of all low priority cellsworks against the objective for which ATM networkswas introduced; hence, cells were only discarded when the buffers are full. It ultimately leads to a signicantimprovement in the ef ciency of the network.

Dziong et al. [13] have proposed a unied frameworkfor traf c control and bandwidth management in ATMnetworks. The central concept of the proposed theme isthe connection admission algorithm that estimates theaggregate equivalent bandwidth required by connec-tions that has to be carried in each output port of theATM switches. The estimation process takes into

account both the traf c source declarations and the con-nection superposition process measurements in theswitch output ports.

El-Madbouly [14] has proposed an ef cient algorithmto compute the minimum capacity required to satisfythe QoS requirements when multiple classes of on offsare multiplexed on to a single VP. In this paper, ATMnetworks in which the VP concept is implemented areconsidered along with the static-time priority in which

Gaddam SVK, et al.: Ant-based Integrated Traf c Control and Management in ATM Networks

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priority is assigned on-per-class basis and the prioritylevel is constant for the same class. All classes are multi-plexed together into a common buffer.

3. ANT COLONY OPTIMIZATION (ACO)

ACO can be dened as a paradigm for designing meta-heuristic algorithms for combinatorial optimization (CO) problems. The important attribute of an ACOalgorithm is using the combination of a-priori informa-tion about the structure of an encouraging result with a-posteriori information about the structure of theresults obtained earlier [15]. The fundamental idea,which is heavily motivated by the behaviour of realants, is that of conducting parallel search over severalconstructive computational threads, each of which ison the basis of local problem data and on a dynamicmemory structure which contains information aboutthe quality of earlier acquired result. The collectivebehaviour originating from the interaction of the differ-

ent search threads has resulted to be fruitful in solvingCO problems [16].

The ant-based routing uses two kinds of agents for-ward and backward agents. The forward agents search the network to gather the network traf c details andare routed on normal priority queues. The forwardagent is replaced by the backward agent when itreaches the destination and the backward agent takesover the stack associated with the forward agent. Thebackward agent is deterministic and is transmitted on aqueue with high priority. The backward agents followthe path of the forward agent and use these details to

update the routing tables (RTs) regularly [17]. Thesemobile agents are very minute and weightless packets.They contain IP addresses of the source and destina-tion, packet ID, node ID, and traveling time.

We will outline two distinctive elements of the ANTSalgorithm within the ACO framework, namely theattractiveness function and the trail updating mecha-nism. The attractiveness represents the inclination of the ant, which selects a new state (i.e. a new direction toward the food) according to its internal evaluations.The trail level, also called pheromone in accordance tothe biological deposit of this chemical, codies the

memory of the ant or better of all the ants which havebeen in the same situation. Loosely speaking, the traillevel is a way, biologically proven in the case of realants, to enable the coordination of a colony of antswithout a direct communication [18].

Trail update: the trail updating procedure evaluateseach solution against the last k solutions globally con-structed by ANTS. As soon as k solutions are available,their moving average z is computed; each new solution

zcurr is compared with z and then used to compute thenew moving average value. If zcurr is lower than z , thetrail level of the last solution ’s moves is increased, oth-erwise it is decreased. Formula (i) species how this isimplemented [15]:

Dt ij D t 0: 1 ¡zcurr ¡ LB

z ¡ LB ; (i)

where, z the average of the last k solutions and LB is alower bound on the optimal problem solution cost. Theuse of a dynamic scaling procedure permits discrimina-tion of a small achievement in the latest stage of search,while avoiding focusing the search only around goodachievement in the earliest stages [15].

4. PROPOSED ANT-BASED TRAFFIC

MANAGEMENT

There is no recent work done to solve the congestion

and bandwidth allocation issues of traf c managementin ATM networks. The authors in [19] have discussed asimilar plot related to QoS in routing of ATM network.The QoS is related to time consumed, packet loss orbandwidth loss. In this proposal, we propose to design an ant-based technique to allocate the bandwidth andresolve the congestion. The forward ants collect theQoS information of the nodes regarding the availablebandwidth and the buffer size. Then based on the QoSrequirements of the various traf c classes, the optimumpath is selected based on the observed QoS statisticscollected by the ant agents. Therefore, it can be adap-tively used for both static and dynamic (bursty) traf cs.

4.1 Traf c Classication

The issue of traf c control and bandwidth managementin ATM-based networks is complex due to a mixture of different connection traf c types, QoS requirements,and time scales. ATM offers ve classes of service [20].Each class is designed to accommodate data burstsaccording to customer needs and to provide the appro-priate QoS for each service class. The ve service cate-gories are: constant bit rate (CBR), real-time variable bitrate (rt-VBR), non-real-time variable bit rate (nrt-VBR),unspecied bit rate (UBR), and available bit rate (ABR).

Constant bit rate (CBR)- provides a continuous rate of ow- supports traf c sensitive to delay and loss- emulates circuit switching- carries uncompressed voice and video

Real-time variable bit rate (rt-VBR)- supports traf c dependent on timing and control

information - carries compressed voice, video, and audio




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Non-real-time variable bit rate (nrt-VBR)- supports traf c at rates that vary with time- unaffected by loss or delay because of time to

recover- carries data and buffered voice and video

Unspecied bit rate (UBR)- provides no assurance that the data will be deliv-

ered (best effort only)- carries le transfers and email

Available bit rate (ABR)- provides no assurance that the data will be deliv-

ered (best effort only)- supports nrt-VBR traf c with ow control

From the above service categories, the rst three catego-ries are non-self-controllable (NSC) whereas the lasttraf c service (ABR) is self-controllable (SC). Accordingto the priority basis, service categories related to NSCare given higher priority with respect to SC. We do notinclude the UBR category as its services have the

smallest priority on the cell layer and do not requireany additional traf c admission control or resourceallocation. We bring in use the priority factor of the traf-c services in our technique to ef ciently allocate band-width and control congestion in order to maintain traf c control for priority-based traf c services.

4.2 Ant Agent Technique

As discussed above in Section 3, the ant technique hastwo agents, namely forward and backward, which can be used for the following purposes.

To detect the best path from source to destination. Detecting the congestion in the network path. Allocating bandwidth according to priority of traf c

services.

During a connection, at regular intervals Dt , every net-work’s source node s transmits a forward agent ant(FAA) to the destination d. The ant agent, who is trans-mitted to destination node d, discovers a feasible andlow-cost path to that node along with investigating theload status of the network. Forward ants share the samequeues as data packets, so that they experience the sametraf c loads. The cycle of an ant agent should be greater

than half the ant’s age. The destinations are locallyselected according to the data traf c patterns generatedby the local workload. We take a prioritized stand in allo-cating bandwidth, which we discuss in Section 4.

While traveling toward their destination nodes, theagents keep memory of their paths and of the traf cconditions on the paths. At each node k, each travelingagent headed towards its destination d, selects the noden to move, choosing among the neighbours which it

had not already visited. The FAA selects its next hopwith a probability P(nd), which is given as [21]

PðndÞ DP 0 ðndÞ C xLn

1 C xð j Nk j ¡ 1Þ (1)

Where Nk denotes the set of neighbours of k, x theweight value, P0ðndÞ the normalized sum of the proba-bilistic entry. Further Ln denotes the instantaneous stateof the node’s queues which is proportional to the length Qn of the queue of the link connecting the node k with its neighbour n, given as [21]

Ln D 1 ¡QnX j Nk j

n 0 D 1Qn 0

: (2)

When FAA reaches the destination node d, it generatesa backward agent, namely, backward ant agent (BAA).It takes the same path as that of its corresponding for-ward ant, but in the opposite direction. At each node kalong the path it pops its stack to know the next hopnode. BAA do not share the same link queues as datapackets; they use higher priority queues, because theirtask is to quickly propagate to the RTs the information accumulated by the forward ants. When BAA reaches anode k coming from a neighbour node f , it updates thetwo main data structures of the node.

The routing table (RT) it consists of the probability

of choosing the best neighbour and the details of theforwarded trip time.

The traf c model table (TMT) the table updates thevalue of the required bandwidth allocation accordingto priority basis.

The BAA updates the two tables till the destination andpresents a congested less, shortest path for the data traf-c to ow.

4.3 Determining the Non-congested Best Shortest Path

As we have discussed above, the next node is chosen

from P(nd). To determine the best path, we provide an increment-based technique to these probabilities. Forevery successful hop to the next node, we provide an increment value a. The value of a is related to trip time(TT) and best TT experienced by the ants travelingtowards the destination d(BTTd).Thus, successful valueis given by [21]

PðndÞ D PðndÞ C a f1 ¡ PðndÞg; (3)




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where,

a DBTTd

TT : (4)

When the network is in a congested state, all the TTs

will score poorly with respect to the times observed in low load situations. Nevertheless, a path with a high TT should be scored as a good path if its TT is signi-cantly lower than the other TTs observed in the samecongested situation. Thus, the increment method willprovide the shortest path, which is relatively being con-gestion free.

4.4 Priority-based Bandwidth Allocation

In the priority-based bandwidth allocation technique,we consider the two types of traf c service NSC and SC(discussed in 4.1) for priority. If the traf c is of type

NSC, the FAA contains information about the declaredtraf c parameters ’ and required equivalent band-width BW. In each node, the traf c control and band-width management algorithm estimates the aggregateequivalent bandwidth, BW’ which should be reservedfor all connections carried on each of the outgoing links.This is carried out using the measurements of thesuperposition cell process parameters Z, in the switch output ports and the source declared traf c parameters’ of the connections already accepted. When a CACcell arrives at the node, the algorithm veries whetherthe requested bandwidth BW is smaller than (or equalto) the residual bandwidth, RBW, given by, [21]

RBW D T ¡ BW 0 ; T D Total bandwidth :

This information of the bandwidth is noted by FAA andupdated in TMT and at the destination, the cell isreturned as BAA. During the process of returning tothe source S, the agent re-checks those bandwidths andthen allocates the bandwidth.

Similarly, in the SC traf c, the FAA updates each nodein the TMT with the desired bandwidth. As SC has an

available bandwidth, thus the values of SC are notdynamic in nature. This provides the agents to pass thevalue for the required bandwidth to each node, simul-taneously allocating the needed bandwidth.

Thus our ant-agent-based technique provides an ef -cient way in allocating bandwidth in a prioritized man-ner. Our technique also provides a congestion freeshortest path to provide a free traf c access in the ATMnetwork.

5. SIMULATION RESULTS

5.1 Simulation Model and Parameters

In this section, we examine the performance of our ant-based integrated traf c control and managementapproach with an extensive simulation study basedupon the NS-2 [22]. We compare our results with the

normal architecture. The topology used in our experi-ments consists of 60 nodes with some egress nodes con-nected with the core nodes to form a mesh of ATMnetworks.

The following section discusses experiments based on CBR and based on exponential traf c. The conclusionsfrom the results of these experiments are given in Sec-tion 6.

5.2 Performance Metrics

Egress bandwidth the maximum and the minimum

upload speed through the network port.

Ingress bandwidth the maximum and the minimumdownload speed through the network port. In ourexperiments, we vary the egress bandwidth and simu-lation time. We measure the following metrics.

Packet loss Throughput in terms of packets End-to-end delay measured in seconds

The results are described in the following sub-sections.

5.3 Experiment Based on CBR

In our rst experiment, we have CBR traf c ow with varying egress bandwidth requirements as 0.25, 0.5,0.75, and 1 Mb. The CBR sending rate is set accordingto the egress bandwidths.

Figures 1 to 3 show that the egress bandwidth variesfrom 0.25 to 1.00 Mb. From the gures, we can see thatthe Received bandwidth is higher, Delay and PacketLoss is lower in our ant-based scheme when compared

Egress Bandwidth Vs ReceivedBandw idth(CBR)

0

1

2

3

0.25 0.50 0.75 1.00

egre ss Bandwidth

R e c e i v e d

B a n d w i d t h

Normal

Ant

Figure 1: Egress bandwidth vs. received bandwidth.




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with the existing normal scheme.

Next we measure the performance metrics in differenttime intervals having CBR traf c ow with egressbandwidth requirement as 1Mb. The CBR sending rateis set according to the egress bandwidth.

Figures 4 to 6 show that the Time interval differencevaries from 1 sec to 4.5 sec. From the gures, we can see that the Received bandwidth is higher, Delay,

Packet Lost is lower in the ant-based scheme when compared with the existing normal scheme.

5.4 Experiment Based on Exponential Traf c

In our second experiment, we have exponential traf cow with varying egress bandwidth requirements as0.25, 0.5, 0.75, and 1 Mb. The exponential traf c sending

rate is set according to the egress bandwidths. The idleand burst time values are set as 0.

Figures 7 to 9 show that the egress bandwidth variesfrom 0.25 to 1.00 Mb. From the gures, we can see thatthe received bandwidth is higher, delay, packet lost islower in our ant-based scheme when compared with the existing normal scheme.

Next, we measure the performance metrics in differenttime intervals having exponential traf c owwith egress bandwidth requirement as 1 Mb. The

Egress Bandwidth Vs Delay(CBR)

0

0.1

0.2

0.3

0.4

0.5

0.25 0.50 0.75 1.00

Egres s Bandwidth

D e l a y ( S e c )

Normal

Ant

Figure 2: Egress bandwidth vs. delay.

Egress Bandwidth Vs Packet Lost (CBR)

0

1000

2000

3000

4000

0.25 0.50 0.75 1.00

Egress Bandwidth

P a c k e t s Normal

Ant

Figure 3: Egress bandwidth vs. packet lost.

Time Vs Received Bandwidth(CBR)

0

0.5

1

1.5

2

2.5

1 1.5 2 2.5 3 3.5 4 4.5

Time(Sec)

R e c e i v e d

B a n d w i d t h

Normal

Ant

Figure 4: Time vs. received bandwidth.

Time Vs Delay(CBR)

0

0.5

1

1.5

2

2.5

1 1.5 2 2.5 3 3.5 4 4.5

Time(Sec)

D e l a y ( S e c )

Normal

Ant

Figure 5: Time vs. delay.

Time Vs PacketLost

0

1000

2000

3000

4000

1 1.5 2 2.5 3 3.5 4 4.5

Time(Sec)


Ant

Figure 6: Time vs. packet lost.

Egress Bandwidth Vs Received Bandwidth

(EXP)

0

1

2

3

0.25 0.5 0.75 1

Egress Bandwidth

R e c e i v e d

B a n d w i d t h

Normal

Ant

Figure 7: Egress bandwidth vs. received bandwidth.

Egress Bandwidth Vs Delay(EXP)

0

0.05

0.1

0.25 0.5 0.75 1

Egress Bandwidth

D e l a y ( S e c )

Normal

Ant

Figure 8: Egress bandwidth vs. delay.




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exponential traf c sending rate is set according to theegress bandwidth.

Figures 10 to 12 show that the time interval differencefrom 1 to 4.5 sec. From the gures, we can see that thereceived bandwidth is higher, delay, packet lost islower in the ant-based scheme when compared with the existing normal scheme.

6 . C ON CL US IO N

ATM technology supports variety of application alongwith their QoS requirements. These provide a greatchallenge for proper traf c management among the dif-ferent applications and traf c services. In this paper,we provide an ef cient traf c management for theATM network. In this respect, we

detect the best path from source to destination, detect the congestion in the network path, allocate bandwidth according to priority of traf c

services.

The ant-agent technique is used to ef ciently detect thebest shortest path. We provide an increment type valueto the best path, which provides the network to pro-ciently detect the congestion free best path. For alloca-tion of bandwidth, a priority-based technique is usedamong the traf c services which are divided into NSCand SC traf cs. The higher priority-based traf c of NSCuses the residual bandwidth to calculate the bandwidth allocation whereas the lower priority-based SC allo-cates the needed bandwidth to each node for traf cmanagement. Therefore, the ant-based integrated tech-nique provides the network to ef ciently use the traf c,thus providing an ef cient traf c management for theATM network. Through simulation results, we haveshown that the proposed ant-based technique outper-forms the existing architecture in terms of throughputand delay.

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Egress Bandwidth Vs Packet Lost (EXP)

0

1000

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Figure 9: Egress bandwidth vs. packet lost.

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Figure 12: Time vs. packet lost.




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Authors

Sunil V K Gaddam received his BTech (ECE) fromSri Venkateswara University (SVU), Tirupati, India

in 1993, post graduate diploma in computer engi-

neering (PGDCE) from Jawaharlal Nehru Techno-

logical University (JNTU), Hyderabad, India in

1994, and MTech (computer science & technol-

ogy) from School of Computer and Systems Scien-

ces, (SC & SS) Jawaharlal Nehru University (JNU),

New Delhi, India in 1997. He received PhD in com-

puter science from School of Computer and Information Sciences (SOCIS),

IGNOU in 2012. He has over 16 years of experience both in teaching and

research in the discipline of computer science in various Indian institu-

tions/universities. He is currently working as principal at Alfa College of

Engineering & Technology, Allagadda, Kurnool (Dist.), Andhra Pradesh.

E-mail: [email protected]

D K Lobiyal received his BTech (CST) from the

Institute of Engineering and Technology, Lucknow

University, 1988, his MTech (CST), Jawaharlal

Nehru University, 1991, and his PhD (CST), Jawa-

harlal Nehru University, 1996. Presently, he is

working as an associate professor at School of

Computer and Systems Sciences (SC & SS) at Jawa-

harlal Nehru University (JNU). His areas of interest

include computer networks, mobile ad-hoc net-

works, natural language processing, video on demand, bioinformatics, etc.


Manohar Lal is the former director of the Schoolof Computer & Information Sciences, Indira Gan-

dhi National Open University, New Delhi (India).

He has teaching and research experience of more

than 30 years at various Indian universities includ-

ing University of Delhi and Jawaharlal Nehru Uni-

versity (JNU), New Delhi.

Prof. Manohar Lal is a product of reputed Indian

academic institutions including IIT Kanpur, IIT Delhi and University of Delhi.

He completed his MTech in computer science and engineering from IIT Kan-

pur and pursued his second PhD in computer science and engineering from

IIT, Delhi. Earlier, he completed his master ’s and PhD programmes in math-

ematics from University of Delhi. During 198283, he visited North Caro-lina State University for post-doctoral work. In the context of academic

work, he has visited a number of countries including USA, UK, Germany

and France.

Prof. Lal has long research experience. Earlier, he worked in the area of

‘error-correcting codes’. Currently, he is working in the areas of e-learning,

automation of reasoning and computer networks.


DOI: 10.1080/03772063.2014.961978; Copyright © 2015 by the IETE


http://www.isi.edu/nsnam/nsmailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://www.isi.edu/nsnam/ns

Published Article Sep 22 Issue IETE Journal of Research

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