Locating Performance Monitoring Mobile Agents in Scalable Active Networks


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1 Locatig Performace Moitorig Mobile Agets i Scalable Active Networks Amir Hossei Hadad, Mehdi Dehgha, ad Hossei Pedram Amirkabir Uiversity, Computer Sciece Faculty, Tehra, Ira {dehgha, Abstract. The idea of active etworks has bee emerged i recet years to icrease the processig power iside the etwork. The itermediate odes such as routers will be able to host mobile agets ad may maagemet tasks ca be hadled usig autoomous mobile agets iside the etwork. Oe of the importat limitatios, which should be cosidered i active etworks, is the restricted processig power of active odes. I this paper, we defie a optimal locatio problem for moitorig mobile agets i a scalable active etwork as a pmedia problem, which is ideed a kid of facility locatio problem. The agets are resposible to moitor ad maage the performace of all of the etwork odes such that the total moitorig traffic overhead is miimized. The we proposed two methods of fidig a appropriate sub set of itermediate odes for hostig mobile agets. I our first method, we have ot cosidered the limited processig power of active odes, which host mobile agets. I our secod method, we have solved the problem so that the processig loads of host odes do ot exceed a predefied threshold. Sice pmedia problems are NPcomplete ad the search space of these problems is very large, our methods are based o geetic algorithms. We have tested our two methods for fidig mobile agets optimal locatios o four etwork topologies with differet umber of odes ad compared the obtaied locatio. By this compariso, we have show the importace of cosiderig processig load limitatio for active odes as a parameter i choosig them as hosts of mobile agets i a scalable active etwork. The proposed locatios i our secod method elimiates the probability of CPU overload i the active odes hostig the mobile agets ad reduces the processig time required for fidig the optimal locatios of mobile agets. Keywords: Active Networks, mobile agets, Performace Moitorig, PMedia Problem, Geetic Algorithm. 1 Itroductio Due to icreasig eed for data processig power i computer etworks, ed odes processig power does ot seem to be eough. I additio, icreasig umber of odes i largescale etworks has made it difficult to update the commuicatio pro N. Megiddo, Y. Xu, ad B. Zhu (Eds.): AAIM 2005, LNCS 3521, pp , SprigerVerlag Berli Heidelberg 2005
2 Locatig Performace Moitorig Mobile Agets i Scalable Active Networks 473 tocols ad hadlig the complex maagemet processes. For such reasos idea of Active etworks has bee proposed i 1997 [1]. Active etwork is a etwork i which itermediate odes such as routers have processig power to ru applicatios such as ed odes. O the other had, complexities i maagemet tasks demad autoomy i maagemet applicatios. Oe of the ew maagemet techologies to address this problem is Mobile agets. Mobile Agets are autoomous software applicatios, which are able to migrate to differet odes i a heterogeeous etwork. Usig mobile agets i etwork maagemet has may advatages. Some of these advatages are effective resource usage, traffic reductio, ad real time iteractio ( [2] [4]). Additioally a maagemet system, which uses mobile aget for its maagemet tasks, has distributed structure. Such a maagemet system would be highly scalable ad flexible ( [3], [4]). Itroducig Active Networks was a milestoe for effective usage of mobile agets i etwork maagemet. Variety of researches for usig mobile agets i differet maagemet tasks is a evidece for this ( [5] [9]). However, there are some limitatios for usig mobile agets i Active Networks. Oe of these restrictios is limited processig power of active odes ([10][12]). As the result, active odes have limited power for hostig mobile agets ad performig their maagemet tasks. I referece [4] mobile agets have bee used for performace moitorig. Goal of this research was to locate moitorig agets locatios i ed odes of a largescale etwork. Sice the etwork is ot active, o processig power capacity restrictio has bee assumed for ed odes. I this paper, we exted the work that has bee doe i [4] for Active Networks ad we have assumed active odes processig power capacity limitatio i additio to other problem limitatios. I ext sectio, we would preset the mobile agets Locatio problem as a p media problem. The we would explai i detail our solutio, which is based o geetic algorithms i part 3. I 4th part, simulatio results are preseted. The fial part is coclusio, which expresses achieved results ad further works. 2 Mobile Aget Locatio Problem Suppose there is a cetral maagemet workstatio, which is goig to sed mobile agets for performace moitorig o the etwork odes. A sub set of active odes is selected for hostig the mobile agets by maagemet workstatio. I this selectio, host odes are chose so that: Performace moitorig traffic of the mobile aget is ear miimal Their Processig usage after hostig the mobile agets would t be beyod tha a predefied threshold Solvig the problem for active odes with processig power limitatio, make it a kid of modified pmedia problem. This ew kid of pmedia problem is capacitated p media problem [13]. We preset a formulatio of pmedia problem i a iteger
3 474 A.H. Hadad, M. Dehgha, ad H. Pedram programmig proposed i [14]. I this presetatio, it is possible to have each vertex of graph as both demad ad facility. I our case, this is useful, because mobile aget host odes (facilities) ad the odes, which are goig to be moitored (demads), are the same i etwork topology. I other word, each of the active odes ca be a mobile aget host. pmedia problem: Subect to the restrictio: Mi i = 1 a d i i= 1 = 1 i x i (1) (2) x = 1, i = 1,2,..., xi y, i = 1,2,..., (3) = 1 y = p, (4) xi, y {0,1}, i, = 1,2,..., (5) Where: = total umber of vertices i the graph, a i = demad of vertex i, d ii = distace from vertex i to vertex, p = umber of facilities used as medias, a, i d ii are positive real umbers,. 1 if vertex iis assiged to facility (6) x i = 0 otherwise 1 if vertex chose as facility = (7) y 0 otherwise The obective fuctio (1) miimizes the sum of the (weighted) distaces betwee the demad vertices ad the media set. The costrait set (2) guaratees that all demad vertices are assiged to exactly oe media. The costrait set (3) prevets that a demad vertex be assiged to a facility that was ot selected as a media. The total umber of media vertices is defied by (4) as equal to p. Costrait (5) esures that the values of the decisio variables x ad y are biary (0 or 1). The mai differece betwee a capacitated pmedia problem ad pmedia problem is two costraits [13]: 1. each facility ca satisfy oly a limited umber of demads (capacity restrictios) 2. all demad poits must be satisfied by respectig the capacities of the facilities selected as medias
4 Locatig Performace Moitorig Mobile Agets i Scalable Active Networks 475 Sice i our work active host odes have capacitated processig power, this versio of the problem is a good cadidate for our case. As it is metioed, goal of this work is to miimize moitorig traffic, which is set from mobile agets to the maagemet workstatio. Moitorig traffic is divided ito three types: Traffic set from moitored odes to the mobile aget, which is resposible to moitor them. It is represeted by Traff. Traffic set from mobile agets to the root. It is represeted by rttraff. Traffic of sedig the mobile agets to host odes. It is represeted by rttraff. Therefore, the iteger programmig formulatio of our problem would be: Mi ( i= 1 = 1 Traff d x Subect the followig costrais: i i i + p = 1 d ( rttraff + MaTraff )) Load i < Threshold i = 1,2,.. < (9) (8) Traff > rttraff > MaTraff (10) i Where: Traff i : is idex of moitored ode, ad i is idex of active ode which is hostig a mobile aget, rttraffi: i is idex of active ode which is hostig a mobile aget, MaTraff i : i is idex of active ode which is hostig a mobile aget, Load i : processig load of i th ode after startig performace moitorig, Threshold: Threshold defied for processig load of active odes. Costrait (9) is equivalet to the costraits (1) ad (2) of capacitated pmedia problem. Costrait (10) is added to make the performace moitorig processes beeficial tha the case of moitorig the performace of etwork without usig mobile agets. I simulatio, we use a performacemoitorig task, which satisfies costrait (10) regardig moitorig traffic. 3 Proposed Method for Optimally Locatig Mobile Agets Hosts I our proposed method, we use a geetic algorithm for fidig ear optimal locatio of mobile aget hosts. I this algorithm, solutio is a bit strig chromosome, which shows locatio of the hosts. Legth of this bit strig is equal to umber of etwork odes. Bits of the chromosome equal to 1 are locatio of the mobile aget hosts. We assume that mobile agets are oly able to moitor their oehob distace odes ad their host. Structure of chromosome codig is the same as [17]. I figure 1 a sample chromosome ad its meaig i the Active Network is show.
5 476 A.H. Hadad, M. Dehgha, ad H. Pedram Proposed solutio has bee preseted for two cases: Case i which goal is to miimize moitorig traffic ad case i which goal is to miimize ad satisfy processig load costrait. Assumed coditios for this problem are as follows: Each mobile aget is oly able to moitor oehob distace odes i the Active Network. Mobile Agets should moitor all the odes Solutio Chromosome MA 3 1 Way of Locatig Mobile agets 4 2 MA Fig. 1. way of locatig mobile agets by a chromosome The Geetic Algorithm Parameters Followig adustmet is used for the geetic algorithm parameters: Mutatio Several simulatios with differet mutatio rates have bee performed ad the best results belog to 0.03 mutatio rate. Simulatios results are geerated usig this mutatio rate. Crossover Differet methods of crossover are used i our simulatios. Best results are obtaied for twopoit crossover. Usig Migratio Migratio is used i solvig this problem. Usig migratio would icrease performace of algorithm for searchig problem space i our case, which result i better solutios. Fitess Fuctio I this paper, there are defied two differet fitess fuctios. The aim of the geetic algorithm here is to miimize these fuctios. Oe of them is defied without regardig the (9) costrai (we refer to this fitess fuctio type 1). The other oe is defied regardig the (9) costrai (we refer to this fitess fuctio type 2).
6 Locatig Performace Moitorig Mobile Agets i Scalable Active Networks 477 Fitess fuctio type 1 Ft1 = Ma2RootTraff / MaxTraff + (11) Nodes2MaTraff / MaxTraff + OverlapRate Fitess fuctio type 2 Ft2 = Ma2RootTraff / MaxTraff + (12) Nodes2MaTraff / MaxTraff + OverlapRate + OverloadedNodes / N Where: Ma2RootTraff : Traffic set from mobile agets to the root, Nodes2MaTraff : Traffic set from moitored odes to the mobile aget which is resposible to moitor them, OverloadedNodes: Number of overloaded odes, MaxTraff: Traffic set from odes to maagemet workstatio i case there is o mobile aget, OverlapRate: Value of this parameter shows the goodess of mobile agets locatios regardig the moitored odes. If a part of these odes have ot bee moitored or have bee moitored more tha 1 times, the value of this parameter would be icreased. Calculatio of the value of it is as follows: OverlapRat e = exp( 1/ 1 N i= 1 ( visitednodes i / N) ) visitednodes i : Number of odes, which are assiged as facility for moitorig the i'th ode, N: Number of active odes. (13) OverLapRate Mea Visit Number of Nodes Fig. 2. Differet values of OverlapRate for differet mea of assigig mobile agets for moitorig etwork odes. As it could be see i the figure whe this mea is equal to 1 (each ode of the etwork is oly is moitored by oe mobile aget), the value of Overlap rate is miimum
7 478 A.H. Hadad, M. Dehgha, ad H. Pedram Geetic algorithm has bee ru for both these fitess fuctios ad the simulatio results are preseted i the ext part of the paper. 4 Simulatio Results I this part of the paper, geetic algorithm simulatio results for four etworks have bee preseted. These etworks have 15, 25, 35, ad 50 odes. CPU of the computer, which the simulatio has bee ru o, is Cetrio 1.5 GHz with 512Mbytes ram. We defie a performace moitorig process i which the mea of 100 parameters of all active odes of the etwork should be collected. The duratio of this process is assumed 1 hour ad 40 miutes, ad performace iformatio is collect each 1 miute from each ode. Size of each parameter is supposed 16 bytes. This way each ode seds 160 Kbytes of traffic data to mobile aget, which is resposible to moitor it (i whole of the moitorig task duratio). Mobile agets sed 1.6 Kbytes to root for each ode they are moitorig. The reaso for reductio i size of traffic set by mobile agets is that they calculate the mea of 100 parameters ad the sed the result to the root. Size of mobile agets has bee chose two Kbytes, based o Grasshopper framework mobile aget s size[18]. I a more formal way: Traff i = 160 Kbytes for all i ad, rttraff i = 1.6 Kbytes for all i, matraff i = 2 Kbytes for all i. The processig load of active odes is supposed to be a percetage betwee 20 ad 65 percet. The processig load of simple ad host odes are calculated usig the followig simple formula: sload = CLoad + p * ratio (14) hload = CLoad + * p * ratio + maload (15) Where: sload: simple ode load after the moitorig task started, hload: host ode load after the moitorig task started, CLoad: Nodeprocessig load before the moitorig task started, : umber of odes moitored by the host, p: parameter umber, ratio: it is a costat value less tha 1, which is the ratio of processig load for moitorig oe parameter of active ode, maload: processig load, this is added to a host ode for ruig a mobile aget o it. Figure 3 shows the processig load of active odes after startig the moitorig process for 15 ad 25 ode etworks for fitess fuctios type oe ad two. It ca be see i this figure that ot cosiderig the processig load ca leads the active odes to overload.
8 Locatig Performace Moitorig Mobile Agets i Scalable Active Networks 479 Iitial Network Traffic Miimizatio Based Moitorig Method CPU Load based Moitorig Method Threshold CPU Loads CPULoads Node Lable (a) Node Lable Fig. 3. (a) etwork with 15 odes. There is oe overload i this etwork usig type oe fitess fuctio. (b) Network with 25 odes. There are 2 overloads i this etwork usig type oe fitess fuctio (b) 4 Best: Mea: Fitess value Geeratio Traffic: KBytes (a) 40 Best: Mea: Fitess value Geeratio Traffic: KBytes (b) Fig. 4.(a) Simulatio results usig fitess type 1 (b) Simulatio results usig fitess type 2. Diamods are fitess mea for the whole populatio of each geeratio ad solid squares are fitess of best chromosome i each populatio
9 480 A.H. Hadad, M. Dehgha, ad H. Pedram Figure 4 shows the covergece of the proposed method for a etwork with 10 odes. Covergece duratio of the geetic algorithm is decreased, whe the fitess fuctio type 2 has bee used. I table 1 the statistical results of 10 times ruig of geetic algorithm for differet etworks is preseted. It ca be uderstad form the table that cosiderig a threshold for processig load of active odes, leads to heavier moitorig traffic i the etwork. However, i the other had this way we ca completely prevet overload i active odes of the etwork. Additioally the covergece time for type two fitess fuctios is lower tha type oe fitess fuctio. The reaso is that, i the type two fitess the search space of etwork is more restricted. This reductio i time is a advatage for type two fitess, because it reduces the total eeded to gather iformatio from active odes. As it ca be see i table 1, total time of geetic algorithm covergece is proper i compariso with total moitorig task time. Table 1. Mea results of 10 time of ruig the algorithm for two fitess fuctios type oe (GA1) ad type two (GA2) Node Number Overloaded Nodes percetage Covergece time Moitorig Traffic Kbytes GA GA GA GA GA GA GA GA Coclusio I deployig mobile agets systems i the Active Networks, we should cosider the processig load costrais of active odes. Because as it is show i this work, supposig ulimited processig power for active odes ca lead to active several odes overloads i usig mobile agets. We could elimiate these overloads by addig overload parameter to our fitess fuctio i our geetic algorithm. I the other had, our simulatio results show that cosiderig this limitatio ca result i fidig the mobile agets locatios faster. The reaso is that the search space is more restricted this way. The total time of moitorig task i compariso with the time of fidig locatios by geetic algorithm was proper i our results. I further work, we would solve the problem for mobile agets, which are able to moitor the whole etwork from their locatio. We have to chage the structure of
10 Locatig Performace Moitorig Mobile Agets i Scalable Active Networks 481 chromosome to solve the problem i this case. I oe had, we ca achieve more optimal solutio for locatio problem, but i the other had, this might cause to icrease the complexity of fidig locatios of mobile agets. Refereces [1] D. L. Teehouse, J.M. Smith, W. D. Sicoskie, D. J. Wetherall, ad G.J. Mide. "A survey of Active Network research", IEEE Commuicatios Magazie, pages 8086, Jauary [2] S. Gree, L. Hurst, B. Nagle, P. Cuigham, F. Sommers ad R. Evas, "Software Agets: A review", Techical Report, Departmet of Computer Sciece, Triiy College, Deblei, Irlad, [3] D. Chess, C. G. Harriso ad A. Kershebaum, mobile agets: Are They a Good Idea? G. Viga (ed.), mobile agets ad Security, LNCS 1419, Spriger Verlag, [4] Atoio Liotta, Towards Flexible ad Scalable Distributed Moitorig with mobile agets, Doctor of Philosophy Dissertatio, Uiversity of Lodo, July [5] D. Gavalas, D. Greewood, M. Ghabari, ad M. O'Mahoy, "A ifrastructure for distributed ad dyamic etwork maagemet based o mobile aget techology." I Proceedigs of the IEEE Iteratioal Coferece o Commuicatios (ICC99), , [6] Daiel Rossier, Rudolf Scheurer, "A Ecosystemispired mobile aget Middleware for Active Network Maagemet", Swisscom Corporate Techology, Uiversity of Fribourg, Switzerlad, [7] Breugst, M. Magedaz, "mobile agets Eablig Techology for Active Itelliget Network Implemetatio", IEEE Network, Vol. 12, No. 3, May R. Kazi, P. Morreale, "mobile agets for Active Network Maagemet", Steves Istitute of Techology, IEEE, [8] A. Galis, D. Griffi, W. Eaves, G. Pavlou, S. Covaci, R. Broos "Mobile Itelliget Agets i Active Virtual Pipes" i "Itelligece i Services ad Networks" Spriger Verlag Berli Heildelberg, April [9] Rumeel Kazi, Patricia Moreale, "mobile agets for Active Network Maagemet", Steves Istitute of Techology, IEEE commuicatios surveys, [10] Celestie Brou et al., " Future Active IP Networks (FAIN) ", GMD compay, Iitial Specificatio of Case Study Systems, May [11] Floria Baumgarter, Torste Brau, ad Bharat Bhargava, Desig ad Implemetatio of a PythoBased Active Network Platform for Network Maagemet ad Cotrol, Desig ad Implemetatio of a PythoBased Active Network Platform for Network Maagemet ad Cotrol, pages , SprigerVerlag, [12] L. Kecl, J. L. Boudec, Adaptive Load Sharig for Network Processors, I Proceedigs of INFOCOM 02, IEEE, Jue [13] E. S. Correa, M. T. A. Steier, A. A. Freitas, C. Carieri, A geetic algorithm for solvig a capacitated pmedia problem, Numerical Algorithms 35, pages , Kluwer Academic Publishers, [14] C. Revelle, R. Swai, Cetral facilities locatio, Geographical Aalysis 2 (1970) [15] S. Shephered, A. Sumali, A Geetic Algorithm Based Approach to Optimal Toll Level ad Locatio Problems, Networks ad Spatial Ecoomics (4), pages , Kluwer Academic Publishers, [16] O. Alp, E. Erkut, Z. Drezer, A Efficiet Geetic Algorithm for the pmedia Problem, Aals of Operatios Research 122, pages 21 42, Kluwer Academic Publishers, 2003.
11 482 A.H. Hadad, M. Dehgha, ad H. Pedram [17] J. H. Jaramillo, J. Bhadury, R. Batta, O the use of geetic algorithms to solve locatio problems, Computers & Operatios Research 29, pages , Elsevier Sciece, [18] Grasshopper Developmet System, Light Editio v2.2.1, Programmer's Guide, IKV++ GmbH, [URL:http://www.grasshopper.de], Berli, 2001.
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