OSPF WEIGHT SETTING OPTIMIZATION FOR SINGLE LINK FAILURES

Transcription

1 OSP WEIGH SEIG OPIMIZAIO OR SIGLE LIK AILURES Mohmmed H. Sqlli, Sdiq M. Sit, nd Syed Asdullh Computer Engineering Deprtment King hd University of Petroleum & Minerls Dhhrn 31261, Sudi Arbi ABSRAC In opertionl networks, nodes re connected vi multiple links for lod shring nd redundncy. his is done to mke sure tht filure of link does not disconnect or isolte some prts of the network. However, link filures hve n effect on routing, s the routers find lternte pths for the trffic originlly flowing through the link which hs filed. his effect is severe in cse of filure of criticl link in the network, such s bckbone links or the links crrying higher trffic lods. When routing is done using the Open Shortest Pth irst (OSP) routing protocol, the originl weight selection for the norml stte topology my not be s efficient for the filure stte. In this pper, we investigte the single link filure issue with n objective to find weight setting which results in efficient routing in norml nd filure sttes. We engineer bu Serch Itertive heuristic using two different implementtion strtegies to solve the OSP weight setting problem for link filure scenrios. We evlute these heuristics nd show through experimentl results tht both heuristics efficiently hndle weight setting for the filure stte. A comprison of both strtegies is lso presented. KEYWORDS Routing, Open Shortest Pth irst (OSP), OSP Weight Setting Problem, Itertive Heuristics, Link ure, bu Serch. 1. IRODUCIO OSP is n intr-domin routing protocol tht uses link weights to mke routing decisions nd compute the shortest pths. Different weight ssignment strtegies hve been discussed in the literture [11] including the Unit OSP, Inverse Cpcity OSP, Rndom OSP etc. A better selection of the OSP link weights cn led to efficient network utiliztion [1, 2]. Itertive heuristics hve been extensively used [10, 3, 4] nd implemented using different strtegies to chieve this gol. Ericsson et l. [4] proposed Genetic Algorithm nd used the set of test problems considered in [11]. A hybrid GA ws lso proposed by them [5] which mkes use of the dynmic shortest pth lgorithm to recompute shortest pths fter the modifiction of link weights. Sridhrn et l. [6] developed nother heuristic for slightly different version of the problem, in which the flow is split mong subset of the outgoing links on the shortest pths to the destintion IP ddress. In this work, we hve used bu Serch (S) lgorithm [7] to solve the OSPWS problem. bu serch is n itertive heuristic tht hs been pplied for solving rnge of combintoril optimiztion problems in different fields. he detiled description nd relted references for tbu serch cn be found in [7]. DOI : /ijcnc

2 However, ll strtegies work on the ssumption tht the topology is fixed nd there re no filures in the network. A network my experience link filure resulting in chnge in topology due to the loss of link, when the network stte chnges (ure Stte) due to link filure, the routing pths re lso not the sme s in the originl stte (l Stte). he optimized weights for the originl topology nd demnd my no longer be good enough for the new topology with the filed link. he bsence of the filed link cuses the trffic which ws originlly flowing through this link to flow through other vilble links. he fct tht the network ws not optimized for these flows cn result in n inefficient mpping of trffic on to vilble links. his my lso cuse congestion in some prts of the network, especilly in the cse of higher demnds. One solution to this issue is to pply new set of OSP weights to links which optimize the new topology (ure Stte). However, it is cumbersome to chnge the weights on ech link in the entire topology nd lso not very prcticl in cse of lrger networks. One would suppose tht once the set of OSP weights hve been fixed, the opertor would not wnt to chnge these weights in order to dpt for such chnges in the stte of the network. Hence, it is required to dpt the originl heuristic to optimize link weights tking into considertion single link filure scenrios. In other words it is required to find set of weights tht work for both the norml nd filed stte of the network without considerble degrdtion in performnce in both sttes. Link filure scenrios require deling with two sttes of network. he first stte where ll links re functionl is denoted s l stte nd the other stte where link hs filed is denoted s ure stte. In this pper, which is n extension of Sqlli et l. [8], two different strtegies re devised nd implemented to ddress this issue. he first strtegy viz. Linkure- is similr to the pproch dopted by ortz nd horoup [9] with some modifictions. Another new strtegy viz. Linkure-SS is proposed, where the weights re first optimized for the ure stte. Keeping these weights fixed, ll combintions of weights re tried for the dded link to find the best cost for the l stte. Both strtegies re discussed further in this pper. Similr problem hs been ttempted by ortz nd horoup [9]. In their pproch, set of links ws considered s criticl, nd in ech itertion one of these links ws filed bsed on the mximum utiliztion mong criticl links. he cost of norml topology nd the resulting filed topology ws verged nd the serch ws driven to find solution which minimizes the verge cost. One of our implementtions in this work is similr to this pproch but with the modifiction tht the link filed is lwys the one connected between nodes crrying the highest demnd. his simultes the worst cse scenrio. he rest of the pper is orgnized s follows he OSPWS problem sttement nd the cost functions proposed in the literture re presented in Section 2. he two Link ure lgorithms re discussed nd nlyzed in Section 3. his is followed by the experimentl results including the comprison of both lgorithms under l nd ure stte in Section 4. inlly, we conclude in Section PROBLEM SAEME AD COS UCIO he OSP weight setting problem cn be stted s follows: Given directed network of nodes nd rcs G = (, A), demnd mtrix D, nd cpcity C for ech rc A, determine positive integer weight w [1, wmx ] for ech rc A such tht the objective function or cost function Φ is minimized. When routing is done using OSP the ssigned link weights completely determine the shortest pths, nd hence the trffic flows. Bsed on these trffic 169

3 flows the prtil lods on ech rc for given destintion re computed. his is done for ll destintion nodes. he ggregted prtil lods for ll destintions on prticulr rc give the totl lod l on tht rc. he cost of sending trffic through this rc is given by Φ ( l ). he cost vlue depends on the utiliztion of the rc nd is given by the liner function proposed by ortz nd horoup. 1 3 ' 10 Φ ( l) = for0 l/ c for1/3 l/ c for2/3 l/ c for9/10 l/ c for1 l/ c for11/10 l/ c < 1/3, < 2/3, < 9/10, < 1, < 11/10, < infinity (1) he ortz cost function is given in eqution 2. Φ = Φ ( l ) A (2) he objective is to minimize Φ, subject to these constrints: l = ( s, t) X f ( s, t) A, (, t) f s 0 (4) (3) In constrint 3, for trffic between source destintion pir (s,t), trffic flow tht goes over rc. f indictes the mount of ( s, t) he detiled steps showing the formultion of this cost function cn be found in the literture [11, 10]. 3. LIK AILURE Hndling link filure scenrios requires deling with two sttes of network. In the l stte, the topology is sid to hve n + 1 links. here exist set of weights W which optimize the cost for this topology. he cost function for this is denoted by Φ ( n + 1), where stnds for Originl Heuristic nd n + 1 indictes topology with n + 1 links. In the cse of filure, these weights for the new topology will result in nother cost nd is denoted by Φ ([ n + 1] 1). Here, [ n + 1] 1 indictes filure of link nd topology chnge from n + 1 to n links. he bove functions re representtive of the costs when the l stte topology ws optimized using the originl heuristic Linkure In Linkure strtegy, to find optimum weights representing both the norml nd the filed sttes, the ide is not to minimize the cost of ech stte individully but to minimize the combined or verge cost of both sttes. 170

4 or given solution or set of weights W for the l stte, the cost is denoted by Φ (n+1) nd for the ure stte with the sme set of weights minus the filed link (W-) the cost is ` Φ (n). he objective is to find the set of weights which minimizes the new cost function: Φ = 1 / 2 ( Φ (n+1) + ` Φ (n)) (5) Strting with rndom initil solution for the l stte ( ) the sme set of weights, except the weight of the filed link, re trnsferred to the filed stte ( ) nd both topologies find the shortest pths nd the cost of the initil solution. bu Serch is strted on by mking rndom moves, nd every time the sme move is gin trnsferred to. Both topologies find the shortest pth nd the corresponding cost fter move. he cost of the new solution for is denoted s Φ ( n + 1) nd the new cost of is denoted s Φ (n). he cost of the current solution Φ is the verge of Φ ( n + 1) nd Φ (n). Φ = 1 / ( 2 Φ ( n + 1) + Φ (n) ) (6) Here, Φ ( n + 1) nd Φ (n) indicte the cost of nd respectively while optimizing the verge cost function. We continue bu Serch nd compute the verge cost for ech itertion until the termintion criteri is met. he set of weights which gives the lest vlue of Φ is the best solution obtined by the new heuristic. igure 1 shows the structure of the Linkure lgorithm Performnce Evlution of Linkure he performnce of this strtegy cn be evluted by compring the cost obtined for nd using this heuristic with tht of the originl. he difference between the costs of the originl nd the new heuristic would indicte gin or loss in the solution qulity. or, this difference would be: = ( n + 1) Φ - Φ ( n + 1) (7) Optimizing weights using the originl heuristic is expected to give better cost thn optimizing for verge cost. Hence, the vlue of is expected to be negtive, indicting loss in solution qulity in the l stte. A smller vlue or vlue close to zero would indicte tht the heuristic is performing well in the l stte. In the cse of the ure stte the cost difference would be indicted s: Φ - Φ (n) (8) = ([ n + 1] 1) he purpose of optimizing the weights for link filure is to chieve better cost in cse of ure stte thn would hve been chieved with the originl heuristic. Hence, must be 171

5 positive vlue indicting n improvement in the solution qulity. A lrger vlue would indicte tht the new heuristic is performing well in the cse of ure stte. Hence, combintion of smller vlue nd lrger vlue would be n idel cse indicting miniml loss in the cse of the l stte nd significnt improvement in the cse of the ure stte. Algorithm: Linkure- S 0 : initil solution. S : solution. S b : best solution. : filed rc. W : Weight of rc i Linkure SS i : l stte. : ure stte. Begin : 1. Generte S 0 2. rnsfer { S 0 - } to 3. Compute Φ ( n + 1) : 4. Compute Φ (n) do : 5. Move(i, W i ) 6. Compute Φ ( n + 1) : 5b. Move(i, W i ) 6b. Compute Φ (n) 7. Φ = 1 / ( 2 Φ ( n + 1) + Φ (n) While (ermintion criteri is not met) 8. S b = S for min( Φ ) End igure 1: Structure of the Linkure lgorithm. In the previous strtegy, we hve tried to optimize weights for the verge cost of nd. In this section, we propose nother strtegy which optimizes weights for nd finds the best solution for by keeping the weights obtined from unchnged nd trying ll possible weights for the one dditionl link. he test cses nd benchmrk topologies used were the sme s for the previous strtegy. 172

6 We strt with rndom initil solution for nd find the shortest pths nd corresponding cost for this solution. bu Serch is strted on by mking rndom moves nd fter ech move, the shortest pths nd corresponding cost re computed. he cost of the new solution for is denoted s Φ (n) which indictes tht the cost is for the topology with n links optimized using the originl heuristic. Once the termintion criterion is met, we obtin the best solution for nd compute its best cost. he finl n weights re trnsferred to ssigned vlues from 1 to 20. or ech weight. he weight on the dditionl (n+1 ) th link is W i, the cost of the th i solution is computed. he twenty costs obtined re compred to find the best solution for. his is denoted by Φ([ n ] + 1) 20 which indictes tht the cost is for topology with n + 1 links where n links re optimized using the originl heuristic nd one dditionl link is optimized by finding the best solution from the twenty possible combintions. igure 2 shows the structure of the Linkure SS lgorithm Algorithm: Linkure-SS S 0 : initil solution. S : solution. S b : best solution. : filed rc. W : Weight of rc i. i : l stte. : ure stte. Begin : 1. Generte S 0 2. Compute Φ (n) 0 do 3. Move(i, W i ) 4. Compute Φ (n) While (ermintion criteri is not met) 5. S b = S for min( Φ (n) ) 6. rnsfer S b to : 7. Compute Φ ( n + 1) for ={1, 2,..., 20} 8. Φ([ n ] + 1) 20 = min( Φ ( n + 1) ) 9. S b = S for Φ([ n ] + 1) 20 End igure 2:Structure of the Linkure-SS lgorithm. 173

7 3.4. Performnce Evlution of Linkure SS Similr to the pproch, the performnce of this strtegy cn be evluted by compring the cost obtined for nd using the SS heuristic with tht of the originl heuristic (). he difference between costs of the originl nd the new heuristic would indicte gin or loss in the solution qulity. or this difference would be: = ( n + 1) Φ - Φ([ n ] + 1) 20 (9) In the cse of the ure stte, the originl heuristic will end up with cost nd the SS heuristic with cost of Φ (n) = ([ n + 1] 1) Φ ([ n + 1] 1). Hence, the cost difference would be indicted s: Φ - Φ (n) (10) In the SS pproch, the weights re optimized for nd re expected to chieve better cost in the cse of link filure thn would hve been chieved with the originl heuristic. Hence, must be positive vlue indicting n improvement in the solution qulity versus SS In the cse of Linkure-, we simultneously optimize two sttes of network viz. nd, wheres in Linkure-SS we only optimize nd then try the best possible weight for the one dditionl link to optimize. Hence, the SS pproch hs fster convergence when compred to which is mjor fctor when deling with lrger networks nd higher demnds. As discussed erlier, SS is optimized for the ure stte nd hence should not only give better solution when compred to but lso should perform better thn in the ure stte. In the pproch, the weights re selected to optimize the verge cost nd not the best cost for individul sttes. Any heuristic, to be cceptble, must not degrde the performnce of the network in the l stte. In other words it should result in solution qulity s close to the Originl Heuristic () s possible. 4. RESULS In this section, we present the experimentl results for the two heuristics mentioned in the previous section. he benchmrks used for the evlution of the originl heuristic for no filure cse [12, 11] were lso used for the link filure cse. Due to the chnge in topology (different number of links) in the two sttes, the originl test cse would represent only one of the sttes nd modified test cse would represent the other stte. Representing the filed stte with modified test cse would require deletion of the corresponding link entries from the files representing the grph nd cpcity of links. his could lso result in disconnection of the grph. o void this, we represented the ure stte with the originl test cses. o represent the l stte, we dd n dditionl link between two nodes n 1 nd n 2. he nodes selected were the ones with the highest demnd between them in the demnd mtrix. ing this prticulr link which is directly connected between the two nodes hving the highest demnd between them would cuse the worst effect on the network. 174

8 Hence, if our heuristic is ble to optimize weights for the worst cse scenrio then it is expected to be robust. he nottions used to denote Cost in the l nd ure stte re shown below: SS Φ ([ ] + 1) 20 ( + 1) vg Φ Φ ( + 1) oh SS Φ () vg Φ () oh Φ ([ + 1] 1) oh 4.1. versus Experimentl results for the two strtegies implemented for the single link filure scenrio re presented in this section. he individul performnce of ech strtegy cn be evluted by compring its results in l nd ure Sttes to the Originl Heuristic. ble 1 shows the Cost vlues obtined using Strtegy nd for five different demnds using the test cse h rom the tble, it cn be seen tht in the l stte the Cost of is mrginlly higher thn, which cn be seen in the column which shows the Cost difference for the two strtegies in the l Stte. egtive vlues indicte loss. As expected, there is some loss in the l Stte. In the ure Stte, for ll demnds except Demnd-9, the Cost is less thn the Cost s indicted by positive vlue in the column. Hence, there is some gin in the ure Stte. he overll gin or loss is indicted in the column. he vlue of is more thn the vlue of for higher demnds D11, D12 which implies tht the mrgin of loss in l stte is more thn the gin in the ure Stte for this cse t higher demnds. Results lso show n overll gin for the two demnds D8 nd D10. ble 1: Cost Comprison versus in l nd ure Stte for h etwork. D D D D D D

9 4.2. SS versus ble 2 shows similr comprison for the SS Strtegy. Even in this cse, the vlues of for SS re mrginlly higher thn those of, nd the vlues of for SS re well below those of for ll five demnds shown in the tble. his shows tht there is slight loss in the l Stte nd significnt improvement in the ure Stte. here is n overll gin s indicted by positive vlues in the lst column. Hence, there is n improvement in performnce due to the use of SS strtegy compred to. ble 2: Cost Comprison SS versus in l nd ure Stte for h etwork. D SS SS D D D D D versus SS We hve seen tht both strtegies re performing better thn the Originl Heuristic in the ure stte while hs slightly better results for the l stte. We now compre the SS nd results to show which of the two heuristics performs better. he comprison is shown in ble 3. In the l stte, for the demnds D8 - D10 both strtegies hve lmost the sme cost vlues with mrginl differences in fvour of SS. or the highest demnd D12, SS clerly performs better thn. Overll, for the l Stte, it cn be sid tht SS performs better thn for this test cse. or the ure Stte, SS clerly outperforms for ll demnds. his is expected s the strtegy is specificlly designed to optimize weights for the ure Stte or in other words to minimize the ure Stte Cost. Hence, SS is lwys expected to produce better results for ure Stte. he overll comprison shows superiority of SS over for this test cse. Comprison of ll three strtegies for this test cse is presented below. ble 3: Cost Comprison versus SS in l nd ure Stte for h etwork. Demnd SS SS D D D D D versus versus SS igure 3 shows the grph with the Cost comprison of ll the three heuristics in the l stte nd in igure 4 for the ure Stte for the h etwork. In igure 3, it cn be seen tht hs the best Cost in the l stte which is very closely mtched by SS. comprtively hs the worst Cost in the l stte. In the ure stte SS 176

10 outperforms both nd s seen in igure 4. Hence, SS hs proved to be hving mrginl loss (negligible in the cse of lower demnds) in the l stte nd significnt gin in the cse of ure, which is the idel requirement for these types of problems. Experiments were conducted for five more test cses nd re presented in the following figures for both the norml nd filure sttes. All these figures provide comprison for ll three lgorithms, i.e.,, SS, nd. A summry of the results obtined is presented t the end of this section. igure 3: Cost Comprison, SS nd in the l stte for h etwork. igure 4: Cost Comprison, SS nd in the ure stte for h etwork. igure 5 shows the grph with the Cost comprison of ll the three heuristics in the l stte nd in igure 6 for the ure Stte for the r50228 etwork. In igure 5, it cn be seen tht both SS nd show comprble results in the l stte. In the ure stte, SS outperforms both nd s seen in igure

11 igure 5: Comprison of, SS, nd in the l stte for r50228 etwork. igure 6: Comprison of, SS, nd in the ure stte for r50228 etwork. igure 7 shows the grph with the Cost comprison of ll the three heuristics in the l stte nd in igure 8 for the ure Stte for the r etwork. In igure 7, it cn be seen tht both SS nd show comprble results in the l stte. In the ure stte, SS outperforms both nd s seen in igure 8. igure 7: Comprison of, SS, nd in the l stte for r etwork. 178

12 igure 8: Comprison of, SS, nd in the ure stte for r etwork. igure 9 shows the grph with the Cost comprison of ll the three heuristics in the l stte nd in igure 10 for the ure Stte for the w50169 etwork. In igure 9, it cn be seen tht ll strtegies perform eqully well in the l stte for ll demnds. In the ure stte, similrly ll strtegies perform eqully well for ll demnds s seen in igure 10. his indictes tht link filure does not hve significnt effect on network performnce for this test cse. igure 9: Comprison of, SS, nd in the l stte for w50169 etwork. 179

13 igure 10: Comprison of, SS, nd in the ure stte for w50169 etwork. igure 11 shows the grph with the Cost comprison of ll the three heuristics in the l stte nd in igure 12 for the ure Stte for the w etwork. In igure 11, it cn be seen tht ll strtegies perform eqully well in the l stte for ll demnds. In the ure stte, similrly ll strtegies perform eqully well for ll demnds s seen in igure 12. his indictes tht link filure does not hve significnt effect on network performnce for this test cse. igure 11: Comprison of, SS, nd in the l stte for w etwork. igure 12: Comprison of, SS, nd in the ure stte for w etwork. 180

14 igure 13 shows the grph with the Cost comprison of ll the three heuristics in the l stte nd in igure 14 for the ure Stte for the h50148 etwork. In igure 13, it cn be seen tht hs the best Cost in the l stte which is very closely mtched by SS. comprtively hs the worst Cost in the l stte. In the ure stte SS outperforms both nd s seen in igure 14. igure 13: Comprison of, SS, nd in the l stte for h50148 etwork. igure 14: Comprison of, SS, nd in the ure stte for h50148 etwork Summry of Results In ll the test cses, SS chieves the best results for the ure stte ( ) nd lso for the overll improvement ( ). SS is followed by in the ure stte, which performs better thn. In the l stte, SS performs slightly better thn for the two test cses h50148 nd h nd hs comprble results for the two cses r nd r or the two Wxmn grphs, w50169 nd w100476, ll strtegies perform eqully well in l nd ure stte for ll demnds. his indictes tht link filure does not hve significnt effect on network performnce for these two cses. inlly, it cn lso be observed tht for lower demnds (Demnd-8, Demnd-9), the results re lmost the sme for ll the six test cses. his indictes tht, if the lod on the network is low, there is minimum effect of the link filure 181

15 on the network performnce nd the originl heuristic itself is efficient enough to hndle single link filures. 5. COCLUSIOS he single link filure issue in OSP routing ws ddressed in this work to find weight setting for the links which results in efficient routing in norml nd filure sttes. wo new heuristics bsed on bu Serch were proposed in this pper, nmely Linkure-SS nd Linkure-. Both heuristics were evluted nd they both produced better results when compred to the originl heuristic in the ure stte. In ddition, the SS pproch is found to give better results thn the pproch in both norml nd filure sttes. herefore, it cn be concluded tht the SS pproch is n efficient wy to tckle single link filure issues. It ws lso shown through experimentl results tht t lower demnds nd trffic lods the effect of link filure on network performnce is less nd the originl heuristic cn lso hndle single link filures if the trffic lod on the network is low. ACKOWLEDGEMES Acknowledgement goes to KUPM for supporting this reserch work. his mteril is bsed in prt on work supported by KUPM project under Grnt o. SAB he uthors wish to thnk Bernrd ortz nd Mikkel horup for shring the test problems. REERECES [1] Bernrd ortz, J. Rexford & Mikkel horup (2002) rffic engineering with trditionl IP routing protocols, IEEE Communictions Mgzine, [2] Bernrd ortz & Mikkel horup (2000) Internet rffic Engineering by Optimizing OSP Weights, IEEE IOCOM. [3] Sdiq M. Sit, Mohmmed H. Sqlli & Mohmmed Aijz Mohiuddin (2006) Engineering Evolutionry Algorithm to Solve Multi-objective OSP Weight Setting Problem, Austrlin Conference on Artificil Intelligence, [4] M Resende Ericsson & P Prdlos (2002) A genetic lgorithm for the weight setting problem in OSP routing, Combintoril Optimistion Conference. [5] L.S. Buriol, M.G.C. Resende, C.C. Ribeiro & M. horup (2005) A Hybrid Genetic Algorithm for the Weight Setting Problem in OSP/IS-IS Routing, A& Lbs Reserch echnicl Report, D-55G. [6] A. Sridhrn, R. Guérin & C. Diot (2005) Achieving ner-optiml trffic engineering solutions for current OSP/IS-IS networks, Sprint AL echnicl Report R02-AL , Sprint Lbs. [7] Sdiq M. Sit & Hbib Youssef (1999) Itertive Computer Algorithms nd their Appliction to Engineering, IEEE Computer Society Press. [8] Mohmmed H. Sqlli, Sdiq M. Sit & Syed Asdullh (2010) Optimizing OSP Routing for Link ure Scenrios, he ifth Interntionl Workshop on Advnced Computtion for Engineering Applictions (ACEA'2010), if University, if, Sudi Arbi. [9] Bernrd ortz & Mikkel horup (2003) Robust optimiztion of OSP/IS-IS weights, Proceedings of the Interntionl etwork Optimiztion Conference, [10] Mohmmed H. Sqlli, Sdiq M. Sit & Mohmmed Aijz Mohiuddin (2006) An Enhnced Estimtor to Multi-objective OSP Weight Setting Problem etwork Opertions nd Mngement Symposium, OMS. [11] Bernrd ortz & Mikkel horup (2000) Incresing Internet Cpcity Using Locl Serch, echnicl Report IS-MG. 182

16 [12] W. Zegur (1996) G-IM: Georgi ech internetwork topology models (softwre), Authors Mohmmed H. Sqlli received degree of Ingenieur d Ett in Computer Science from Ecole Mohmmdi d Ingenieurs, Rbt, Morocco in He erned Mster s degree in Computer Science in 1996 nd Ph.D. degree in Engineering - Systems Design in 2002, both from the University of ew Hmpshire, Durhm, H, USA. He is recipient of ulbright Scholrship for the period of Mohmmed H. Sqlli is currently n ssistnt professor in the Computer Engineering Deprtment t KUPM. He is lso n IEEE member. His reserch interests include: etwork Security, Cloud Computing, etwork Design nd Mngement, rffic Engineering, nd Itertive Heuristics. He hs over 25 publictions in relted res. Sdiq M. Sit obtined Bchelor's degree in Electronics from Bnglore University in 1981, nd Mster's nd PhD degrees in Electricl Engineering from King hd University of Petroleum & Minerls (KUPM), Dhhrn, Sudi Arbi in 1983 & 1987 respectively. Sdiq M. Sit is the co-uthor of the book VLSI PHYSICAL DESIG AUOMAIO: heory & Prctice, published by McGrw-Hill Book Co., Europe, (nd lso co-published by IEEE Press), Jnury 1995, nd IERAIVE COMPUER ALGORIHMS with APPLICAIOS in EGIEERIG (Solving Combintoril Optimiztion Problems): published by IEEE Computer Society Press, Cliforni, USA, He ws the Chirmn of Computer Engineering Deprtment, KUPM from Jnury December Presently he is the Director of Informtion echnology Center (IC) t KUPM, since Jnury Syed Asdullh received Bchelor of echnology (B. ech) degree in Electronics nd Communictions Engineering from Jwherll ehru echnologicl University, Hyderbd, Indi in He lso obtined his Mster s degree in Computer etworks from KUPM, Sudi Arbi in