Quality-Of-Service Class Specific Traffic Matrices in IP/MPLS Networks

Size: px
Start display at page:

Download "Quality-Of-Service Class Specific Traffic Matrices in IP/MPLS Networks"

Transcription

1 ualiy-of-service Class Specific Traffic Marices in IP/MPLS Neworks Sefan Schnier Deusche Telekom, T-Sysems D-4 Darmsad +4 Franz Harleb Deusche Telekom, T-Sysems D-4 Darmsad +4 Marin Horneffer Deusche Telekom, T-Com Hammer Sr. - D-4 Münser +4 0 ABSTRACT In his paper we consider he problem of deermining raffic marices for end-o-end demands in an IP/MPLS nework ha suppors muliple qualiy of service (os) classes. More precisely, we wan o deermine he se of raffic marices T i for each os class i separaely. T i conains average bandwidh levels for os class i for every pair of rouers wihin he nework. We propose a new mehod for obaining os class specific raffic marices ha combines esimaion and measuremen mehods: We ake advanage of he fac ha he oal raffic marix can be measured precisely in MPLS neworks using eiher he LDP or RSVP-TE proocol. These measuremens can hen be used in a mahemaical model o improve esimaion mehods known as nework omography ha esimae os class specific raffic marices from os class specific link uilizaions. In addiion o he mahemaical model, we presen resuls of he proposed mehod from is applicaion in Deusche Telekom s global IP/MPLS backbone nework and we show ha he esimaion accuracy (mean relaive error) is improved by a facor of. compared o resuls from nework omograviy. We invesigae he srucure of he esimaed raffic marices for he differen os classes and moivae in his paper why os class specific raffic marices will be essenial for efficien nework planning and nework engineering in he fuure. Caegories and Subjec Descripors C.4 [Performance of Sysems]: Measuremen echniques, Modeling echniques. C.. [Compuer-Communicaion Neworks]: Nework Operaions. General Terms: Algorihms, Measuremen. Keywords: Traffic Marices, MPLS, LDP, os.. INTRODUCTION Traffic marices or origin-desinaion (o-d) marices conain end o end raffic demands beween each pair of nodes in a given IP nework. In his paper we are ineresed in minues average raffic demands beween rouers. For an ISP, here are muliple reasons why he availabiliy of good qualiy raffic Permission o make digial or hard copies of all or par of his work for personal or classroom use is graned wihou fee provided ha copies are no made or disribued for profi or commercial advanage and ha copies bear his noice and he full ciaion on he firs page. To copy oherwise, or republish, o pos on servers or o redisribue o liss, requires prior specific permission and/or a fee. IMC0, Ocober 4-, 00, San Diego, California, USA. Copyrigh 00 ACM ---0-/0/000...$.00. marices is essenial: Imporan asks for an ISP ha require raffic marices include nework planning and raffic engineering. They are needed o perform simulaions of failure scenarios and nework exension scenarios as well as for (IGP) rouing opimizaion. For some asks especially in nework planning raffic marices are needed on he level of PoPs bu hey can generally be obained by aggregaing rouer level raffic marices. Already he ask of raffic marix generaion is difficul for he oal raffic wihin a nework and may require esimaion mehods or complex measuremen infrasrucures. Bu as rue muli-service neworks become realiy and raffic classes wih differen service requiremens exis, he need for raffic marices per class-of-service (CoS) is geing sronger: In order o plan and operae a muli-service IP/MPLS nework economically, an ISP needs o incorporae os class specific raffic marices ino is planning and engineering asks. Possible scenarios include: Differen service level agreemens for he os classes require a differen bandwidh dimensioning. For each os class he acual raffic mus never exceed he bandwidh guaraneed o ha class by he scheduler. Only he bes effor class can be allowed o use up o he physical link bandwidh wihou violaing given service level agreemens. The use of links wih low delay (e.g. direc links from Europe o Asia no via America) only for raffic from cerain os classes. Failure simulaions in order o validae cerain os conceps (e.g. o make sure ha non-bes-effor raffic remains below a cerain link uilizaion hreshold) While applicaions wih high os requiremens such as IPTV or VoIP are growing o a significan conribuion o he overall raffic demand, a service provider needs o incorporae os class specific raffic daa ino is planning and engineering processes. In his paper we consider only unicas raffic marices. Mulicas raffic will generally be divided ino differen os classes, oo, and has o be measured separaely. For disribuion plaforms wih fixed raffic sources and sinks (e.g. IPTV wihin he backbone nework) his may be possible from he knowledge of he source raffic and he mulicas ree. For (unicas) raffic marices, a variey of mehods have already been invesigaed, boh for raffic marix esimaion and for raffic marix measuremen. One mehod ha is widely deployed for raffic marix measuremen based on IP flow level measuremens is Cisco s Neflow []. Bu he implemenaion is raher complex and issues ha arise in pracice include:

2 Neflow availabiliy and performance depend on he line card ypes in use in a service provider s nework here is usually a large number of differen hardware ypes in use. Thus only a parial measuremen migh be possible. IP flow measuremens generally use packe sampling, so here is a radeoff o make beween he measuremen accuracy (low sampling raes) and performance (high sampling raes). Some rouer ypes are no even able o provide anyhing han very high sampling raes. Large effor for aggregaion of he flows ha are expored by he rouers. Well-esablished esimaions mehods for raffic marices are graviy esimaion [0], where a raffic demand from s o is se proporional o he oal ougoing raffic of s and he oal incoming raffic of or nework omography, where end-o-end demands are esimaed from link uilizaions see []. The combinaion of hose wo esimaion mehods is commonly referred o as nework omograviy see [] for a survey and comparison of differen esimaion mehods. I is obvious ha hose esimaion mehods can also be used o esimae os class specific raffic marices if he necessary inpu daa (mosly link uilizaions) are available per os class. In neworks ha use muli-proocol label swiching - MPLS [] o forward packes here are addiional mehods o measure he oal raffic marix. If RSVP-TE [] is used, a full mesh of unnels can be deployed and couners for hose unnels exis o measure he raffic marix. If LDP [4] is used o disribue he label informaion in a nework, LDP saisics of he rouers can be used o compue raffic marices on a rouer level see [], []. The LDP mehod resuls in a very high measuremen accuracy (for example, here is no sampling involved) while he measuremen complexiy is very low: The measuremen is based on aggregaed forwarding equivalence classes (FEC) ha are inroduced in MPLS/LDP and is no based on he IP flow level. However, he LDP mehod can only be used for he nework s oal raffic marix and no per os class. For Deusche Telekom s IP/MPLS backbone nework he LDP mehod is currenly used o compue oal raffic marices o suppor IGP meric opimizaion (see [] for a heoreical survey or [] for a discussion on he pracical implemenaion) and nework planning. Dependen on he exising measuremen infrasrucure, he nework opology or he deployed proocols, i may be easier o obain raffic marices for he oal raffic wihin he nework han os class specific raffic marices. This is why we propose a model for os class specific raffic marices ha combines esimaion mehods wih oal raffic marix measuremens. This paper is organized as follows: Secion describes he mahemaical model used o esimae os class specific raffic marices and secion gives numerical resuls from he applicaion of his model o a par of Deusche Telekom s global IP/MPLS backbone nework.. OS TRAFFIC MATRIX MODEL. Noaions for Tomograviy Model If our nework has n nodes and m links we denoe by x he vecor of link uilizaion and he vecor represenaion of he raffic marix, i.e. i j (i,j=,,n) conains he raffic from node i o node j. From he nework opology and he IGP merics in use, we can consruc he nework s rouing marix A. The enry a k, i j [0,] (i,j=,,n; k=,,m) of A conains he par of he demand i j on link k. The rouing marix A resuls from a shores-pah calculaion wih respec o he given IGP merics if he nework makes no use of equal cos pah spliing (ECMP) a k, i j {0,} holds. The relaion beween raffic marix, link uilizaions and rouing marix is hen described by he following equaion: (R) A = x. The omography esimaion of he raffic marix can be consruced by solving he following sysem of equaions for a given rouing marix A, a given vecor of measured link uilizaions x and a given iniial esimae E for he raffic marix: A x min (TG) s.. E min The soluion of (TG) is given by ~ = E + A ( x A E ) where ~ T A = V S U denoes he Moore-Penrose-Inverse (also pseudo inverse) of he rouing marix A and is consruced wih a singular value decomposiion (SVD) of A: A = U S V We use he SVD rouines conained in LAPACK [4] for his purpose ieraive mehods for large scale SVD compuaions are also available see []. If a graviy esimaion is used as an iniial esimae E, (TG) is referred o as omograviy mehod. The soluion of (TG) can be inerpreed as a (orhogonal) projecion of he iniial soluion E ono he subspace of all raffic marices ha saisfy he link uilizaion resricions (R), i.e. we calculae ha admissible raffic marix ha is closes o E. For omograviy esimaion mehods a graviy esimae is chosen for E one possibiliy o improve he graviy esimae is he use of a generalized graviy model as given in [] which akes ino accoun he knowledge of where peering raffic eners he nework. The assumpion ha here is no ransi peering raffic in he nework hen resrics he number of possible raffic demand combinaions and hereby improves he esimae. One mehod o improve he numerical sabiliy of he soluion of he sysem (TG) is o remove parallel links from he nework opology. Service providers ofen use parallel links in heir neworks o increase capaciy before hey move o a echnology wih higher capaciy (e.g. wo or hree.gbi/s links before insalling on 0Gbi/s link). Those parallel links have he same IGP meric so ha hey are uilized equally when using ECMP. In pracice, he raffic is no shared enirely equal depending on which hashing algorihm he rouers use for load sharing ha means ha here are differences in he corresponding componens of he uilizaion vecor x. On he oher hand he rouing marix A inroduces heoreical load sharing properies T 4

3 and equaion (R) canno be fulfilled exacly. We herefore combine parallel links o one link wih he sum of he parallel links capaciies and heir average uilizaion.. Inegraed Model wih os class raffic In his secion we exend (TG) o a sysem ha can be used o esimae raffic marices per os class. Unlike in he previous secion we now assume ha he full raffic marix is given. In Deusche Telekom s IP/MPLS backbone nework we use he LDP mehod o obain - oher mehods as discussed in he inroducion (RSVP-TE unnel couners or Neflow measuremens) could also be used. If we inend o apply (TG) for differen os classes, os class specific link uilizaions are a prerequisie. Bu here may no be os class specific link uilizaions available for all links in he nework: For example in case of Cisco rouers os-class specific link uilizaions require he use of he Modular os CLI (MC) whose availabiliy depends on he IOS sofware release and he hardware (line card ypes) in use. For a os-class raffic marix esimaion mehod ha can be implemened in pracice i is reasonable o assume os-class link uilizaions are available for a subse of all links and End-o-End loads are available for he sum of all os classes (l) If here are q os classes in he nework and we denoe by and x ˆ l he raffic marix and he link uilizaion vecor for he os class l (l =,,q), and by  he rouing marix reduced o subse of links whose os-class specific link uilizaions are available, he following se of equaion holds analog o (R): Aˆ () () x 0 ˆ () O M = ( q) 0 Aˆ M xˆ 44 L q I I 4 A x (R) Using he model (R) we can now apply he same esimaion mehod as in secion II.A analog o (TG) o solve (R): The esimaion accuracy of (TG) will depend highly on he size of he subse of links ha have os specific link uilizaions available. Also i should be noed ha (TG) can resul in raffic marix esimaions wih negaive enries since (TG) has no condiion for being nonnegaive. We apply a simple ieraion scheme o assure posiive soluions whose convergence is discussed in he following secion. Compared o an applicaion of he omograviy mehod (TG) for each os class separaely (TG) inroduces furher resricions: he sum of he os class specific raffic demands mus equal he oal raffic for he respecive raffic relaion. These addiional resricions should improve he esimaion qualiy of he mehod. Secion invesigaes he improvemen of he esimaion qualiy for a concree example wih realisic daa. Model (R) applies he same rouing for all os classes bu i can be easily exended o os specific rouing schemes if we replace  by os specific rouing marices ˆ l A (l =,,q).. NUMERICAL RESULTS The os-omography model (TG) is applied o an example nework wih nodes and 0 edges (Figure ). We assume four os classes: voice, low loss, low delay, and bes effor. In he following secion, we discuss wo problems: esimaion convergence and esimaion accuracy. The hird example shows esimaion resuls based on os link uilizaion measuremens from our backbone nework. The numerical resuls focus on he differences in he demand srucures of he os class specific raffic marices. Therefore raffic marices for one given (- minue) ime inerval are compared. Anoher difference ha is no covered wihin his paper is he difference in he ime dependen variance of he raffic volume: Differen os classes will have differen daily peak hours. As an example Figure shows his difference in he daily link uilizaion char for one backbone link. The ime dependen behavior of raffic marices is paricularly imporan for he choice of he raffic marix ha is acually used for raffic engineering or nework planning: For non-bes-effor raffic classes he raffic profile migh follow business hours and a raffic marix from a differen ime inerval has o be used for nework planning or raffic engineering purposes. (TG) A s.. x E min min The iniial esimae for he os class specific raffic marix E is consruced from he oal raffic demand beween he nodes ha is divided proporionally o os class specific link uilizaions, more precisely: where ( l) ( ) E i j = i j q r= X E l X r = ( ( q) E, K, E ) m ( r), l =,, q and X r = xˆ k. k = Figure : Daily profiles for differen os classes for one backbone link (normalized diagrams)

4 . Esimaion accuracy For analyzing he esimaion accuracy we assume given raffic marix values for all os classes. This given vecor of raffic marices is denoed by. We use a random number generaor o se he values in he range beween 0 and In a simulaion, he raffic of he four os classes is roued according o he IGP merics of our backbone nework. From he roued raffic we can hen calculae he link uilizaions x ˆ l k for each link k and os class l. The raffic marix of he oal raffic is calculaed from he sum over he given os raffic marices. Finally, is esimaed from and x as a soluion of (TG) and we calculae he relaive error E as measure for he esimaion accuracy. = qn E i i i i Figure : Example nework opology.. Esimaion convergence Dependen on he iniial esimae vecor E, problem (TG) may resul in negaive values for individual raffic marix enries. In his case we apply an ieraive procedure where he negaive elemens per origin-desinaion (o-d) relaion are se o 0 and he negaive volume is added o he values of he posiive enries. Afer his modificaion, he sum over he os classes per o-d relaion is unchanged and all enries of he raffic marix are non negaive. This resul is used as a new sar vecor E for he nex ieraion wih equaion (TG). The diagram in Figure shows ha he ieraion scheme converges: The percenage of he negaive raffic marix volume is reduced from % o 0.0 % afer only 0 ieraions. Negaive raffic volume [%] Ieraion Figure : Esimaion convergence If we ake ino consideraion all elemens of he raffic marices, he mean relaive error is dominaed by he small elemens. As he conribuion of hese elemens o he overall raffic volume is very small, we focus on he mean relaive error E α of hose elemens ha are larger han he (-α) quanile of he raffic marix elemen disribuion funcion. Figure 4 we compare E α from he (TG) mehod (normal lines) wih resuls from a omography model wih a sar vecor from he graviy model (TG model: dashed lines, same color). The omography model (TG) is applied o each os class separaely. The resuls show ha he mean relaive error is reduced by a facor of. compared o resuls from he (TG) esimaion. Neverheless, a mean relaive error in he range beween 0 % and 00 % indicae ha furher improvemens are needed. Mean relaive error [%] TG: os TG: os TG: os TG: os TG: os TG: os4 TG: os TG: os α Figure 4: Esimaion accuracy.

5 One applicaion of os class specific raffic marices is os dependen rouing - especially if he srucure of he os raffic marices differs from class o class. The esimaion resul is useful only, if he srucures are idenified accuraely by he esimaion mehod. The following accuracy es analyzes his problem. We assume wo differen os classes. Class raffic originaes a nodes o, class raffic originaes a nodes o. Each source sends raffic unis o each oher node of our nework. Nodes o and nodes o respecively are no in a close opological region. The nodes are sored by name alphabeically and numbered from o. The esimaion resul for he os class specific raffic marices is shown in Figure and Figure. The marix elemens are colored as follows: green: less han 000 raffic unis, yellow: beween 000 and 0000 raffic unis, red: more han 0000 raffic unis. Therefore, a perfec mach is achieved, if he firs rows of he os marix are red and he ohers are green and vice versa for os. The srucures of he os raffic marices are accuraely idenified in he esimaion. The mean relaive error E is 4. % for os class and. % for class. If we calculae he E α, only he posiive elemens of he raffic marices are considered. In ha case, he mean relaive error E α is. % for class and. % for class ### Figure : Esimaion of os raffic Figure : Esimaion of os raffic.. Esimaion resuls wih real nework daa In he following secion he os class specific raffic marix esimaion (TG) is applied o daa from our backbone nework. The oal raffic marix is calculaed by means of he LDP mehod. In he considered nework opology only of he backbone rouers could measure he link uilizaion saisics per os class. Therefore, we expec more accurae resuls when all he rouers are upgraded o an OS version ha allows measuremens per os class. Firs we show he relaion of he amoun of raffic beween he four os classes ( Figure ). Obviously, he oal raffic volume is dominaed by he bes effor raffic (os4) and he srucure of he os4 marix agrees wih he srucure of he oal raffic marix (Figure ). The srucures of he os, os and os raffic marices are differen o he srucure of he oal raffic marix. This is mainly caused by specific applicaion archiecures (e.g. posiion of voice gaeway rouers), or locaions of specific cusomers wih relaively high os demands. From he os raffic marix in Figure (voice raffic), we can see ha a lo of raffic is desined o node and node, he locaion of voice gaeways. Main sources are he nodes o node 4. Traffic.E+0.E+0.E+0.E+00.E-0 os os os os4 os classes Figure : Disribuion over os classes.

6 Figure : Overall raffic marix Figure : os raffic marix. 4. CONCLUSION We have presened a new model and numerical resuls from a real world nework for os class specific raffic marices. We demonsraed ha his mehod can significanly improve he esimaion qualiy. The numerical resuls show he differences in he demand srucure for differen os classes. Thereby we moivae why he availabiliy of good qualiy os class specific raffic marices is imporan for efficien nework planning and raffic engineering. The absolue size of he esimaion errors is sill quie high for he inended use of he os class specific raffic marices. On he oher hand a good esimaion of he demand srucure of he raffic marices could already be achieved and he need for os class specific raffic marices will only grow wih he raffic growh in non-bes-effor raffic classes. Figure shows ha here is sill some ime o improve he esimaion qualiy. Fuure improvemens of our mehod include he usage of saeof-he-ar mehods from numerical linear algebra (e.g. sparse marix SVD). This would allow us o invesigae larger nework opologies. Furhermore, we aim o incorporae a larger number of os class specific link uilizaions no only from links wihin he backbone nework bu also from ingress links ino he nework - o improve he esimaion qualiy.. REFERENCES [] Neflow Aggregaion, Cisco IOS release.0()t. [] E. Rosen, A.Viswanahan, R. Callon: Muliproocol Label Swiching Archiecure, IETF RFC 0, Jan 00 [] D. Awduche, L. Berger, D. Gan, T. Li, V. Srinivasan, G.Swallow: RSVP-TE: Exensions o RSVP for LSP Tunnels, IETF RFC 0, Dec 00 [4] L. Anderson, P. Doolan, N. Feldman, A. Fredee, B. Thomas: LDP Specificaion, IETF RFC 0, Jan 00 [] S. Schnier, M. Horneffer, Traffic Marices for MPLS Neworks wih LDP Saisics, Proc Neworks004, VDE- Verlag, Vienna, 004. [] S. Schnier, T. Morsein and M. Horneffer, Combining LDP Measuremens and Esimaion Mehods for Traffic Marices in IP/MPLS Neworks, Proc. Neworks00, VDE-Verlag, New Delhi, 00. [] S. Schnier, G. Haßlinger, Heurisic Soluions o he LSP- Design for MPLS Traffic Engineering, Proc. Neworks00, VDE-Verlag, Munich, 00. [] M Horneffer, IGP uning in an MPLS nework, NANOG, Las Vegas, 00. [] B. Forz, M. Thorup, Inerne Traffic Engineering by Opimizing OSPF Weighs, Proc. IEEE INFOCOM 000, 000. [0] J. Kowalski, B. Warfield, Modeling raffic demand beween nodes in a elecommunicaions nework, Proc. ATNAC,. [] J. Cao, D. Drew, S. Wiel, B. Yu, Time-Varying Nework Tomography: Rouer Link Daa, Bell Labs Tech. Memo, 000. [] Y. Zhang, M. Roughan, N. Duffield, A. Greenberg, Fas Accurae Compuaions of Large-Scale IP Traffic Marices from Link Loads, Proc. SIGMETRICS 0, San Diego, 00. [] A. Gunnar, M. Johansson, T. Telkamp, Traffic Marix Esimaion on a Large IP Backbone A Comparison on Real Daa, Proc. IMC 04, Taormina, 004. [4] E. Anderson, Z. Bai, C. Bischof, S. Blackford, J. Demmel, J. Dongarra, J. Croz, A. Greenbaum, S. Hammarling, A. McKenny, D. Sorense, LAPACK User s Guide, SIAM,. [] M. Berry, Large Scale Singular Value Compuaions, Inernaional Journal of Supercompuer Applicaions, :, pp. -4,

Quality-Of-Service Class Specific Traffic Matrices in IP/MPLS Networks

Quality-Of-Service Class Specific Traffic Matrices in IP/MPLS Networks ualiy-of-service Class Specific Traffic Marices in IP/MPLS Neworks Sefan Schnier Deusche Telekom, T-Sysems D-4 Darmsad +4 sefan.schnier@-sysems.com Franz Harleb Deusche Telekom, T-Sysems D-4 Darmsad +4

More information

Multiprocessor Systems-on-Chips

Multiprocessor Systems-on-Chips Par of: Muliprocessor Sysems-on-Chips Edied by: Ahmed Amine Jerraya and Wayne Wolf Morgan Kaufmann Publishers, 2005 2 Modeling Shared Resources Conex swiching implies overhead. On a processing elemen,

More information

PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE

PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE Profi Tes Modelling in Life Assurance Using Spreadshees PROFIT TEST MODELLING IN LIFE ASSURANCE USING SPREADSHEETS PART ONE Erik Alm Peer Millingon 2004 Profi Tes Modelling in Life Assurance Using Spreadshees

More information

Journal Of Business & Economics Research September 2005 Volume 3, Number 9

Journal Of Business & Economics Research September 2005 Volume 3, Number 9 Opion Pricing And Mone Carlo Simulaions George M. Jabbour, (Email: jabbour@gwu.edu), George Washingon Universiy Yi-Kang Liu, (yikang@gwu.edu), George Washingon Universiy ABSTRACT The advanage of Mone Carlo

More information

Distributing Human Resources among Software Development Projects 1

Distributing Human Resources among Software Development Projects 1 Disribuing Human Resources among Sofware Developmen Proecs Macario Polo, María Dolores Maeos, Mario Piaini and rancisco Ruiz Summary This paper presens a mehod for esimaing he disribuion of human resources

More information

The Application of Multi Shifts and Break Windows in Employees Scheduling

The Application of Multi Shifts and Break Windows in Employees Scheduling The Applicaion of Muli Shifs and Brea Windows in Employees Scheduling Evy Herowai Indusrial Engineering Deparmen, Universiy of Surabaya, Indonesia Absrac. One mehod for increasing company s performance

More information

Automatic measurement and detection of GSM interferences

Automatic measurement and detection of GSM interferences Auomaic measuremen and deecion of GSM inerferences Poor speech qualiy and dropped calls in GSM neworks may be caused by inerferences as a resul of high raffic load. The radio nework analyzers from Rohde

More information

Trends in TCP/IP Retransmissions and Resets

Trends in TCP/IP Retransmissions and Resets Trends in TCP/IP Reransmissions and Reses Absrac Concordia Chen, Mrunal Mangrulkar, Naomi Ramos, and Mahaswea Sarkar {cychen, mkulkarn, msarkar,naramos}@cs.ucsd.edu As he Inerne grows larger, measuring

More information

Single-machine Scheduling with Periodic Maintenance and both Preemptive and. Non-preemptive jobs in Remanufacturing System 1

Single-machine Scheduling with Periodic Maintenance and both Preemptive and. Non-preemptive jobs in Remanufacturing System 1 Absrac number: 05-0407 Single-machine Scheduling wih Periodic Mainenance and boh Preempive and Non-preempive jobs in Remanufacuring Sysem Liu Biyu hen Weida (School of Economics and Managemen Souheas Universiy

More information

Morningstar Investor Return

Morningstar Investor Return Morningsar Invesor Reurn Morningsar Mehodology Paper Augus 31, 2010 2010 Morningsar, Inc. All righs reserved. The informaion in his documen is he propery of Morningsar, Inc. Reproducion or ranscripion

More information

Distributed Echo Cancellation in Multimedia Conferencing System

Distributed Echo Cancellation in Multimedia Conferencing System Disribued Echo Cancellaion in Mulimedia Conferencing Sysem Balan Sinniah 1, Sureswaran Ramadass 2 1 KDU College Sdn.Bhd, A Paramoun Corporaion Company, 32, Jalan Anson, 10400 Penang, Malaysia. sbalan@kdupg.edu.my

More information

Individual Health Insurance April 30, 2008 Pages 167-170

Individual Health Insurance April 30, 2008 Pages 167-170 Individual Healh Insurance April 30, 2008 Pages 167-170 We have received feedback ha his secion of he e is confusing because some of he defined noaion is inconsisen wih comparable life insurance reserve

More information

Making a Faster Cryptanalytic Time-Memory Trade-Off

Making a Faster Cryptanalytic Time-Memory Trade-Off Making a Faser Crypanalyic Time-Memory Trade-Off Philippe Oechslin Laboraoire de Securié e de Crypographie (LASEC) Ecole Polyechnique Fédérale de Lausanne Faculé I&C, 1015 Lausanne, Swizerland philippe.oechslin@epfl.ch

More information

Duration and Convexity ( ) 20 = Bond B has a maturity of 5 years and also has a required rate of return of 10%. Its price is $613.

Duration and Convexity ( ) 20 = Bond B has a maturity of 5 years and also has a required rate of return of 10%. Its price is $613. Graduae School of Business Adminisraion Universiy of Virginia UVA-F-38 Duraion and Convexiy he price of a bond is a funcion of he promised paymens and he marke required rae of reurn. Since he promised

More information

USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES

USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES Mehme Nuri GÖMLEKSİZ Absrac Using educaion echnology in classes helps eachers realize a beer and more effecive learning. In his sudy 150 English eachers were

More information

Task is a schedulable entity, i.e., a thread

Task is a schedulable entity, i.e., a thread Real-Time Scheduling Sysem Model Task is a schedulable eniy, i.e., a hread Time consrains of periodic ask T: - s: saring poin - e: processing ime of T - d: deadline of T - p: period of T Periodic ask T

More information

How To Calculate Price Elasiciy Per Capia Per Capi

How To Calculate Price Elasiciy Per Capia Per Capi Price elasiciy of demand for crude oil: esimaes for 23 counries John C.B. Cooper Absrac This paper uses a muliple regression model derived from an adapaion of Nerlove s parial adjusmen model o esimae boh

More information

PRACTICES AND ISSUES IN OPERATIONAL RISK MODELING UNDER BASEL II

PRACTICES AND ISSUES IN OPERATIONAL RISK MODELING UNDER BASEL II Lihuanian Mahemaical Journal, Vol. 51, No. 2, April, 2011, pp. 180 193 PRACTICES AND ISSUES IN OPERATIONAL RISK MODELING UNDER BASEL II Paul Embrechs and Marius Hofer 1 RiskLab, Deparmen of Mahemaics,

More information

A Note on Using the Svensson procedure to estimate the risk free rate in corporate valuation

A Note on Using the Svensson procedure to estimate the risk free rate in corporate valuation A Noe on Using he Svensson procedure o esimae he risk free rae in corporae valuaion By Sven Arnold, Alexander Lahmann and Bernhard Schwezler Ocober 2011 1. The risk free ineres rae in corporae valuaion

More information

Heuristics for dimensioning large-scale MPLS networks

Heuristics for dimensioning large-scale MPLS networks Heurisics for dimensioning large-scale MPLS newors Carlos Borges 1, Amaro de Sousa 1, Rui Valadas 1 Depar. of Elecronics and Telecommunicaions Universiy of Aveiro, Insiue of Telecommunicaions pole of Aveiro

More information

TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS

TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS TEMPORAL PATTERN IDENTIFICATION OF TIME SERIES DATA USING PATTERN WAVELETS AND GENETIC ALGORITHMS RICHARD J. POVINELLI AND XIN FENG Deparmen of Elecrical and Compuer Engineering Marquee Universiy, P.O.

More information

Premium Income of Indian Life Insurance Industry

Premium Income of Indian Life Insurance Industry Premium Income of Indian Life Insurance Indusry A Toal Facor Produciviy Approach Ram Praap Sinha* Subsequen o he passage of he Insurance Regulaory and Developmen Auhoriy (IRDA) Ac, 1999, he life insurance

More information

Advanced Traffic Routing as Part of the USA Intelligent Telecommunications Network

Advanced Traffic Routing as Part of the USA Intelligent Telecommunications Network Advanced Traffic Rouing as Par of he USA Inelligen Telecommunicaions Nework Edmund P. GOULD Bell Communicaions Research 435 Souh Sree Morrisown, New Jersey 07960 The USA Telecommunicaions Nework is evolving

More information

Analogue and Digital Signal Processing. First Term Third Year CS Engineering By Dr Mukhtiar Ali Unar

Analogue and Digital Signal Processing. First Term Third Year CS Engineering By Dr Mukhtiar Ali Unar Analogue and Digial Signal Processing Firs Term Third Year CS Engineering By Dr Mukhiar Ali Unar Recommended Books Haykin S. and Van Veen B.; Signals and Sysems, John Wiley& Sons Inc. ISBN: 0-7-380-7 Ifeachor

More information

Principal components of stock market dynamics. Methodology and applications in brief (to be updated ) Andrei Bouzaev, bouzaev@ya.

Principal components of stock market dynamics. Methodology and applications in brief (to be updated ) Andrei Bouzaev, bouzaev@ya. Principal componens of sock marke dynamics Mehodology and applicaions in brief o be updaed Andrei Bouzaev, bouzaev@ya.ru Why principal componens are needed Objecives undersand he evidence of more han one

More information

Risk Modelling of Collateralised Lending

Risk Modelling of Collateralised Lending Risk Modelling of Collaeralised Lending Dae: 4-11-2008 Number: 8/18 Inroducion This noe explains how i is possible o handle collaeralised lending wihin Risk Conroller. The approach draws on he faciliies

More information

A Resource Management Strategy to Support VoIP across Ad hoc IEEE 802.11 Networks

A Resource Management Strategy to Support VoIP across Ad hoc IEEE 802.11 Networks A Resource Managemen Sraegy o Suppor VoIP across Ad hoc IEEE 8.11 Neworks Janusz Romanik Radiocommunicaions Deparmen Miliary Communicaions Insiue Zegrze, Poland j.romanik@wil.waw.pl Pior Gajewski, Jacek

More information

Hedging with Forwards and Futures

Hedging with Forwards and Futures Hedging wih orwards and uures Hedging in mos cases is sraighforward. You plan o buy 10,000 barrels of oil in six monhs and you wish o eliminae he price risk. If you ake he buy-side of a forward/fuures

More information

Chapter 8: Regression with Lagged Explanatory Variables

Chapter 8: Regression with Lagged Explanatory Variables Chaper 8: Regression wih Lagged Explanaory Variables Time series daa: Y for =1,..,T End goal: Regression model relaing a dependen variable o explanaory variables. Wih ime series new issues arise: 1. One

More information

Analysis of Pricing and Efficiency Control Strategy between Internet Retailer and Conventional Retailer

Analysis of Pricing and Efficiency Control Strategy between Internet Retailer and Conventional Retailer Recen Advances in Business Managemen and Markeing Analysis of Pricing and Efficiency Conrol Sraegy beween Inerne Reailer and Convenional Reailer HYUG RAE CHO 1, SUG MOO BAE and JOG HU PARK 3 Deparmen of

More information

Constant Data Length Retrieval for Video Servers with Variable Bit Rate Streams

Constant Data Length Retrieval for Video Servers with Variable Bit Rate Streams IEEE Inernaional Conference on Mulimedia Compuing & Sysems, June 17-3, 1996, in Hiroshima, Japan, p. 151-155 Consan Lengh Rerieval for Video Servers wih Variable Bi Rae Sreams Erns Biersack, Frédéric Thiesse,

More information

Network Effects, Pricing Strategies, and Optimal Upgrade Time in Software Provision.

Network Effects, Pricing Strategies, and Optimal Upgrade Time in Software Provision. Nework Effecs, Pricing Sraegies, and Opimal Upgrade Time in Sofware Provision. Yi-Nung Yang* Deparmen of Economics Uah Sae Universiy Logan, UT 84322-353 April 3, 995 (curren version Feb, 996) JEL codes:

More information

SELF-EVALUATION FOR VIDEO TRACKING SYSTEMS

SELF-EVALUATION FOR VIDEO TRACKING SYSTEMS SELF-EVALUATION FOR VIDEO TRACKING SYSTEMS Hao Wu and Qinfen Zheng Cenre for Auomaion Research Dep. of Elecrical and Compuer Engineering Universiy of Maryland, College Park, MD-20742 {wh2003, qinfen}@cfar.umd.edu

More information

Smooth Priorities for Multi-Product Inventory Control

Smooth Priorities for Multi-Product Inventory Control Smooh rioriies for Muli-roduc Invenory Conrol Francisco José.A.V. Mendonça*. Carlos F. Bispo** *Insiuo Superior Técnico - Universidade Técnica de Lisboa (email:favm@mega.is.ul.p) ** Insiuo de Sisemas e

More information

DYNAMIC MODELS FOR VALUATION OF WRONGFUL DEATH PAYMENTS

DYNAMIC MODELS FOR VALUATION OF WRONGFUL DEATH PAYMENTS DYNAMIC MODELS FOR VALUATION OF WRONGFUL DEATH PAYMENTS Hong Mao, Shanghai Second Polyechnic Universiy Krzyszof M. Osaszewski, Illinois Sae Universiy Youyu Zhang, Fudan Universiy ABSTRACT Liigaion, exper

More information

Efficient One-time Signature Schemes for Stream Authentication *

Efficient One-time Signature Schemes for Stream Authentication * JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, 611-64 (006) Efficien One-ime Signaure Schemes for Sream Auhenicaion * YONGSU PARK AND YOOKUN CHO + College of Informaion and Communicaions Hanyang Universiy

More information

Distributed Online Localization in Sensor Networks Using a Moving Target

Distributed Online Localization in Sensor Networks Using a Moving Target Disribued Online Localizaion in Sensor Neworks Using a Moving Targe Aram Galsyan 1, Bhaskar Krishnamachari 2, Krisina Lerman 1, and Sundeep Paem 2 1 Informaion Sciences Insiue 2 Deparmen of Elecrical Engineering-Sysems

More information

LEVENTE SZÁSZ An MRP-based integer programming model for capacity planning...3

LEVENTE SZÁSZ An MRP-based integer programming model for capacity planning...3 LEVENTE SZÁSZ An MRP-based ineger programming model for capaciy planning...3 MELINDA ANTAL Reurn o schooling in Hungary before and afer he ransiion years...23 LEHEL GYÖRFY ANNAMÁRIA BENYOVSZKI ÁGNES NAGY

More information

Supplementary Appendix for Depression Babies: Do Macroeconomic Experiences Affect Risk-Taking?

Supplementary Appendix for Depression Babies: Do Macroeconomic Experiences Affect Risk-Taking? Supplemenary Appendix for Depression Babies: Do Macroeconomic Experiences Affec Risk-Taking? Ulrike Malmendier UC Berkeley and NBER Sefan Nagel Sanford Universiy and NBER Sepember 2009 A. Deails on SCF

More information

The Greek financial crisis: growing imbalances and sovereign spreads. Heather D. Gibson, Stephan G. Hall and George S. Tavlas

The Greek financial crisis: growing imbalances and sovereign spreads. Heather D. Gibson, Stephan G. Hall and George S. Tavlas The Greek financial crisis: growing imbalances and sovereign spreads Heaher D. Gibson, Sephan G. Hall and George S. Tavlas The enry The enry of Greece ino he Eurozone in 2001 produced a dividend in he

More information

MEDDELANDEN FRÅN SVENSKA HANDELSHÖGSKOLAN SWEDISH SCHOOL OF ECONOMICS AND BUSINESS ADMINISTRATION WORKING PAPERS

MEDDELANDEN FRÅN SVENSKA HANDELSHÖGSKOLAN SWEDISH SCHOOL OF ECONOMICS AND BUSINESS ADMINISTRATION WORKING PAPERS MEDDELANDEN FRÅN SVENSKA HANDELSHÖGSKOLAN SWEDISH SCHOOL OF ECONOMICS AND BUSINESS ADMINISTRATION WORKING PAPERS 3 Jukka Liikanen, Paul Soneman & Oo Toivanen INTERGENERATIONAL EFFECTS IN THE DIFFUSION

More information

How To Predict A Person'S Behavior

How To Predict A Person'S Behavior Informaion Theoreic Approaches for Predicive Models: Resuls and Analysis Monica Dinculescu Supervised by Doina Precup Absrac Learning he inernal represenaion of parially observable environmens has proven

More information

Nikkei Stock Average Volatility Index Real-time Version Index Guidebook

Nikkei Stock Average Volatility Index Real-time Version Index Guidebook Nikkei Sock Average Volailiy Index Real-ime Version Index Guidebook Nikkei Inc. Wih he modificaion of he mehodology of he Nikkei Sock Average Volailiy Index as Nikkei Inc. (Nikkei) sars calculaing and

More information

Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach * Ben S. Bernanke, Federal Reserve Board

Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach * Ben S. Bernanke, Federal Reserve Board Measuring he Effecs of Moneary Policy: A acor-augmened Vecor Auoregressive (AVAR) Approach * Ben S. Bernanke, ederal Reserve Board Jean Boivin, Columbia Universiy and NBER Pior Eliasz, Princeon Universiy

More information

Task-Execution Scheduling Schemes for Network Measurement and Monitoring

Task-Execution Scheduling Schemes for Network Measurement and Monitoring Task-Execuion Scheduling Schemes for Nework Measuremen and Monioring Zhen Qin, Robero Rojas-Cessa, and Nirwan Ansari Deparmen of Elecrical and Compuer Engineering New Jersey Insiue of Technology Universiy

More information

Stock Trading with Recurrent Reinforcement Learning (RRL) CS229 Application Project Gabriel Molina, SUID 5055783

Stock Trading with Recurrent Reinforcement Learning (RRL) CS229 Application Project Gabriel Molina, SUID 5055783 Sock raing wih Recurren Reinforcemen Learning (RRL) CS9 Applicaion Projec Gabriel Molina, SUID 555783 I. INRODUCION One relaively new approach o financial raing is o use machine learning algorihms o preic

More information

Chapter 5. Aggregate Planning

Chapter 5. Aggregate Planning Chaper 5 Aggregae Planning Supply Chain Planning Marix procuremen producion disribuion sales longerm Sraegic Nework Planning miderm shorerm Maerial Requiremens Planning Maser Planning Producion Planning

More information

Research on Inventory Sharing and Pricing Strategy of Multichannel Retailer with Channel Preference in Internet Environment

Research on Inventory Sharing and Pricing Strategy of Multichannel Retailer with Channel Preference in Internet Environment Vol. 7, No. 6 (04), pp. 365-374 hp://dx.doi.org/0.457/ijhi.04.7.6.3 Research on Invenory Sharing and Pricing Sraegy of Mulichannel Reailer wih Channel Preference in Inerne Environmen Hanzong Li College

More information

Chapter 1.6 Financial Management

Chapter 1.6 Financial Management Chaper 1.6 Financial Managemen Par I: Objecive ype quesions and answers 1. Simple pay back period is equal o: a) Raio of Firs cos/ne yearly savings b) Raio of Annual gross cash flow/capial cos n c) = (1

More information

ANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS

ANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS ANALYSIS AND COMPARISONS OF SOME SOLUTION CONCEPTS FOR STOCHASTIC PROGRAMMING PROBLEMS R. Caballero, E. Cerdá, M. M. Muñoz and L. Rey () Deparmen of Applied Economics (Mahemaics), Universiy of Málaga,

More information

Real-time Particle Filters

Real-time Particle Filters Real-ime Paricle Filers Cody Kwok Dieer Fox Marina Meilă Dep. of Compuer Science & Engineering, Dep. of Saisics Universiy of Washingon Seale, WA 9895 ckwok,fox @cs.washingon.edu, mmp@sa.washingon.edu Absrac

More information

Q-SAC: Toward QoS Optimized Service Automatic Composition *

Q-SAC: Toward QoS Optimized Service Automatic Composition * Q-SAC: Toward QoS Opimized Service Auomaic Composiion * Hanhua Chen, Hai Jin, Xiaoming Ning, Zhipeng Lü Cluser and Grid Compuing Lab Huazhong Universiy of Science and Technology, Wuhan, 4374, China Email:

More information

Measuring macroeconomic volatility Applications to export revenue data, 1970-2005

Measuring macroeconomic volatility Applications to export revenue data, 1970-2005 FONDATION POUR LES ETUDES ET RERS LE DEVELOPPEMENT INTERNATIONAL Measuring macroeconomic volailiy Applicaions o expor revenue daa, 1970-005 by Joël Cariolle Policy brief no. 47 March 01 The FERDI is a

More information

A Distributed Multiple-Target Identity Management Algorithm in Sensor Networks

A Distributed Multiple-Target Identity Management Algorithm in Sensor Networks A Disribued Muliple-Targe Ideniy Managemen Algorihm in Sensor Neworks Inseok Hwang, Kaushik Roy, Hamsa Balakrishnan, and Claire Tomlin Dep. of Aeronauics and Asronauics, Sanford Universiy, CA 94305 Elecrical

More information

Relationships between Stock Prices and Accounting Information: A Review of the Residual Income and Ohlson Models. Scott Pirie* and Malcolm Smith**

Relationships between Stock Prices and Accounting Information: A Review of the Residual Income and Ohlson Models. Scott Pirie* and Malcolm Smith** Relaionships beween Sock Prices and Accouning Informaion: A Review of he Residual Income and Ohlson Models Sco Pirie* and Malcolm Smih** * Inernaional Graduae School of Managemen, Universiy of Souh Ausralia

More information

DETERMINISTIC INVENTORY MODEL FOR ITEMS WITH TIME VARYING DEMAND, WEIBULL DISTRIBUTION DETERIORATION AND SHORTAGES KUN-SHAN WU

DETERMINISTIC INVENTORY MODEL FOR ITEMS WITH TIME VARYING DEMAND, WEIBULL DISTRIBUTION DETERIORATION AND SHORTAGES KUN-SHAN WU Yugoslav Journal of Operaions Research 2 (22), Number, 6-7 DEERMINISIC INVENORY MODEL FOR IEMS WIH IME VARYING DEMAND, WEIBULL DISRIBUION DEERIORAION AND SHORAGES KUN-SHAN WU Deparmen of Bussines Adminisraion

More information

Monotonic, Inrush Current Limited Start-Up for Linear Regulators

Monotonic, Inrush Current Limited Start-Up for Linear Regulators Applicaion epor SLA156 March 2004 Monoonic, Inrush urren Limied Sar-Up for Linear egulaors Jeff Falin PMP Porable Producs ABSA he oupu volage of a linear regulaor ends o rise quickly afer i is enabled.

More information

4. International Parity Conditions

4. International Parity Conditions 4. Inernaional ariy ondiions 4.1 urchasing ower ariy he urchasing ower ariy ( heory is one of he early heories of exchange rae deerminaion. his heory is based on he concep ha he demand for a counry's currency

More information

Capital Budgeting and Initial Cash Outlay (ICO) Uncertainty

Capital Budgeting and Initial Cash Outlay (ICO) Uncertainty Financial Decisions, Summer 006, Aricle Capial Budgeing and Iniial Cash Oulay (ICO) Uncerainy Michael C. Ehrhard and John M. Wachowicz, Jr. * * The Paul and Beverly Casagna Professor of Finance and Professor

More information

Time Series Analysis Using SAS R Part I The Augmented Dickey-Fuller (ADF) Test

Time Series Analysis Using SAS R Part I The Augmented Dickey-Fuller (ADF) Test ABSTRACT Time Series Analysis Using SAS R Par I The Augmened Dickey-Fuller (ADF) Tes By Ismail E. Mohamed The purpose of his series of aricles is o discuss SAS programming echniques specifically designed

More information

Appendix D Flexibility Factor/Margin of Choice Desktop Research

Appendix D Flexibility Factor/Margin of Choice Desktop Research Appendix D Flexibiliy Facor/Margin of Choice Deskop Research Cheshire Eas Council Cheshire Eas Employmen Land Review Conens D1 Flexibiliy Facor/Margin of Choice Deskop Research 2 Final Ocober 2012 \\GLOBAL.ARUP.COM\EUROPE\MANCHESTER\JOBS\200000\223489-00\4

More information

The Architecture of a Churn Prediction System Based on Stream Mining

The Architecture of a Churn Prediction System Based on Stream Mining The Archiecure of a Churn Predicion Sysem Based on Sream Mining Borja Balle a, Bernardino Casas a, Alex Caarineu a, Ricard Gavaldà a, David Manzano-Macho b a Universia Poliècnica de Caalunya - BarcelonaTech.

More information

The Kinetics of the Stock Markets

The Kinetics of the Stock Markets Asia Pacific Managemen Review (00) 7(1), 1-4 The Kineics of he Sock Markes Hsinan Hsu * and Bin-Juin Lin ** (received July 001; revision received Ocober 001;acceped November 001) This paper applies he

More information

A Scalable and Lightweight QoS Monitoring Technique Combining Passive and Active Approaches

A Scalable and Lightweight QoS Monitoring Technique Combining Passive and Active Approaches A Scalable and Lighweigh QoS Monioring Technique Combining Passive and Acive Approaches On he Mahemaical Formulaion of CoMPACT Monior Masai Aida, Naoo Miyoshi and Keisue Ishibashi NTT Informaion Sharing

More information

Load Prediction Using Hybrid Model for Computational Grid

Load Prediction Using Hybrid Model for Computational Grid Load Predicion Using Hybrid Model for Compuaional Grid Yongwei Wu, Yulai Yuan, Guangwen Yang 3, Weimin Zheng 4 Deparmen of Compuer Science and Technology, Tsinghua Universiy, Beijing 00084, China, 3, 4

More information

Model-Based Monitoring in Large-Scale Distributed Systems

Model-Based Monitoring in Large-Scale Distributed Systems Model-Based Monioring in Large-Scale Disribued Sysems Diploma Thesis Carsen Reimann Chemniz Universiy of Technology Faculy of Compuer Science Operaing Sysem Group Advisors: Prof. Dr. Winfried Kalfa Dr.

More information

BALANCE OF PAYMENTS. First quarter 2008. Balance of payments

BALANCE OF PAYMENTS. First quarter 2008. Balance of payments BALANCE OF PAYMENTS DATE: 2008-05-30 PUBLISHER: Balance of Paymens and Financial Markes (BFM) Lena Finn + 46 8 506 944 09, lena.finn@scb.se Camilla Bergeling +46 8 506 942 06, camilla.bergeling@scb.se

More information

INTRODUCTION TO FORECASTING

INTRODUCTION TO FORECASTING INTRODUCTION TO FORECASTING INTRODUCTION: Wha is a forecas? Why do managers need o forecas? A forecas is an esimae of uncerain fuure evens (lierally, o "cas forward" by exrapolaing from pas and curren

More information

The Complete VoIP Telecom Service Provider

The Complete VoIP Telecom Service Provider The Complee VoIP Telecom Service Provider 1 Overview Company Overview SIP Trunking Produc Overview Technical Specificaions Pricing Why SIP Trunking? Benefis over radiional elecom Ideal cusomer 2 Company

More information

Statistical Analysis with Little s Law. Supplementary Material: More on the Call Center Data. by Song-Hee Kim and Ward Whitt

Statistical Analysis with Little s Law. Supplementary Material: More on the Call Center Data. by Song-Hee Kim and Ward Whitt Saisical Analysis wih Lile s Law Supplemenary Maerial: More on he Call Cener Daa by Song-Hee Kim and Ward Whi Deparmen of Indusrial Engineering and Operaions Research Columbia Universiy, New York, NY 17-99

More information

Strategic Optimization of a Transportation Distribution Network

Strategic Optimization of a Transportation Distribution Network Sraegic Opimizaion of a Transporaion Disribuion Nework K. John Sophabmixay, Sco J. Mason, Manuel D. Rossei Deparmen of Indusrial Engineering Universiy of Arkansas 4207 Bell Engineering Cener Fayeeville,

More information

Spectrum-Aware Data Replication in Intermittently Connected Cognitive Radio Networks

Spectrum-Aware Data Replication in Intermittently Connected Cognitive Radio Networks Specrum-Aware Daa Replicaion in Inermienly Conneced Cogniive Radio Neworks Absrac The opening of under-uilized specrum creaes an opporuniy for unlicensed users o achieve subsanial performance improvemen

More information

Forecasting, Ordering and Stock- Holding for Erratic Demand

Forecasting, Ordering and Stock- Holding for Erratic Demand ISF 2002 23 rd o 26 h June 2002 Forecasing, Ordering and Sock- Holding for Erraic Demand Andrew Eaves Lancaser Universiy / Andalus Soluions Limied Inroducion Erraic and slow-moving demand Demand classificaion

More information

PATHWISE PROPERTIES AND PERFORMANCE BOUNDS FOR A PERISHABLE INVENTORY SYSTEM

PATHWISE PROPERTIES AND PERFORMANCE BOUNDS FOR A PERISHABLE INVENTORY SYSTEM PATHWISE PROPERTIES AND PERFORMANCE BOUNDS FOR A PERISHABLE INVENTORY SYSTEM WILLIAM L. COOPER Deparmen of Mechanical Engineering, Universiy of Minnesoa, 111 Church Sree S.E., Minneapolis, MN 55455 billcoop@me.umn.edu

More information

Capacity Planning and Performance Benchmark Reference Guide v. 1.8

Capacity Planning and Performance Benchmark Reference Guide v. 1.8 Environmenal Sysems Research Insiue, Inc., 380 New York S., Redlands, CA 92373-8100 USA TEL 909-793-2853 FAX 909-307-3014 Capaciy Planning and Performance Benchmark Reference Guide v. 1.8 Prepared by:

More information

Chapter 6: Business Valuation (Income Approach)

Chapter 6: Business Valuation (Income Approach) Chaper 6: Business Valuaion (Income Approach) Cash flow deerminaion is one of he mos criical elemens o a business valuaion. Everyhing may be secondary. If cash flow is high, hen he value is high; if he

More information

Vector Autoregressions (VARs): Operational Perspectives

Vector Autoregressions (VARs): Operational Perspectives Vecor Auoregressions (VARs): Operaional Perspecives Primary Source: Sock, James H., and Mark W. Wason, Vecor Auoregressions, Journal of Economic Perspecives, Vol. 15 No. 4 (Fall 2001), 101-115. Macroeconomericians

More information

TSG-RAN Working Group 1 (Radio Layer 1) meeting #3 Nynashamn, Sweden 22 nd 26 th March 1999

TSG-RAN Working Group 1 (Radio Layer 1) meeting #3 Nynashamn, Sweden 22 nd 26 th March 1999 TSG-RAN Working Group 1 (Radio Layer 1) meeing #3 Nynashamn, Sweden 22 nd 26 h March 1999 RAN TSGW1#3(99)196 Agenda Iem: 9.1 Source: Tile: Documen for: Moorola Macro-diversiy for he PRACH Discussion/Decision

More information

STABILITY OF LOAD BALANCING ALGORITHMS IN DYNAMIC ADVERSARIAL SYSTEMS

STABILITY OF LOAD BALANCING ALGORITHMS IN DYNAMIC ADVERSARIAL SYSTEMS STABILITY OF LOAD BALANCING ALGORITHMS IN DYNAMIC ADVERSARIAL SYSTEMS ELLIOT ANSHELEVICH, DAVID KEMPE, AND JON KLEINBERG Absrac. In he dynamic load balancing problem, we seek o keep he job load roughly

More information

Energy and Performance Management of Green Data Centers: A Profit Maximization Approach

Energy and Performance Management of Green Data Centers: A Profit Maximization Approach Energy and Performance Managemen of Green Daa Ceners: A Profi Maximizaion Approach Mahdi Ghamkhari, Suden Member, IEEE, and Hamed Mohsenian-Rad, Member, IEEE Absrac While a large body of work has recenly

More information

The option pricing framework

The option pricing framework Chaper 2 The opion pricing framework The opion markes based on swap raes or he LIBOR have become he larges fixed income markes, and caps (floors) and swapions are he mos imporan derivaives wihin hese markes.

More information

Towards Intrusion Detection in Wireless Sensor Networks

Towards Intrusion Detection in Wireless Sensor Networks Towards Inrusion Deecion in Wireless Sensor Neworks Kroniris Ioannis, Tassos Dimiriou and Felix C. Freiling Ahens Informaion Technology, 19002 Peania, Ahens, Greece Email: {ikro,dim}@ai.edu.gr Deparmen

More information

Exploring Imputation Techniques for Missing Data in Transportation Management Systems

Exploring Imputation Techniques for Missing Data in Transportation Management Systems Exploring Impuaion Techniques for Missing Daa in Transporaion Managemen Sysems Brian L. Smih Assisan Professor Universiy of Virginia Deparmen of Civil Engineering P. O. Box 400742 Charloesville, VA 22904-4742

More information

Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising

Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising Real Time Bid Opimizaion wih Smooh Budge Delivery in Online Adverising Kuang-Chih Lee Ali Jalali Ali Dasdan Turn Inc. 835 Main Sree, Redwood Ciy, CA 94063 {klee,ajalali,adasdan}@urn.com ABSTRACT Today,

More information

Mobile and Ubiquitous Compu3ng. Mul3plexing for wireless. George Roussos. g.roussos@dcs.bbk.ac.uk

Mobile and Ubiquitous Compu3ng. Mul3plexing for wireless. George Roussos. g.roussos@dcs.bbk.ac.uk Mobile and Ubiquious Compu3ng Mul3plexing for wireless George Roussos g.roussos@dcs.bbk.ac.uk Overview Sharing he wireless (mul3plexing) in space by frequency in 3me by code PuEng i all ogeher: cellular

More information

PREMIUM INDEXING IN LIFELONG HEALTH INSURANCE

PREMIUM INDEXING IN LIFELONG HEALTH INSURANCE Far Eas Journal of Mahemaical Sciences (FJMS 203 Pushpa Publishing House, Allahabad, India Published Online: Sepember 203 Available online a hp://pphm.com/ournals/fms.hm Special Volume 203, Par IV, Pages

More information

LEASING VERSUSBUYING

LEASING VERSUSBUYING LEASNG VERSUSBUYNG Conribued by James D. Blum and LeRoy D. Brooks Assisan Professors of Business Adminisraion Deparmen of Business Adminisraion Universiy of Delaware Newark, Delaware The auhors discuss

More information

DEMAND FORECASTING MODELS

DEMAND FORECASTING MODELS DEMAND FORECASTING MODELS Conens E-2. ELECTRIC BILLED SALES AND CUSTOMER COUNTS Sysem-level Model Couny-level Model Easside King Couny-level Model E-6. ELECTRIC PEAK HOUR LOAD FORECASTING Sysem-level Forecas

More information

Hotel Room Demand Forecasting via Observed Reservation Information

Hotel Room Demand Forecasting via Observed Reservation Information Proceedings of he Asia Pacific Indusrial Engineering & Managemen Sysems Conference 0 V. Kachivichyanuul, H.T. Luong, and R. Piaaso Eds. Hoel Room Demand Forecasing via Observed Reservaion Informaion aragain

More information

Network Discovery: An Estimation Based Approach

Network Discovery: An Estimation Based Approach Nework Discovery: An Esimaion Based Approach Girish Chowdhary, Magnus Egersed, and Eric N. Johnson Absrac We consider he unaddressed problem of nework discovery, in which, an agen aemps o formulae an esimae

More information

Chapter 7. Response of First-Order RL and RC Circuits

Chapter 7. Response of First-Order RL and RC Circuits Chaper 7. esponse of Firs-Order L and C Circuis 7.1. The Naural esponse of an L Circui 7.2. The Naural esponse of an C Circui 7.3. The ep esponse of L and C Circuis 7.4. A General oluion for ep and Naural

More information

Evolutionary building of stock trading experts in real-time systems

Evolutionary building of stock trading experts in real-time systems Evoluionary building of sock rading expers in real-ime sysems Jerzy J. Korczak Universié Louis Paseur Srasbourg, France Email: jjk@dp-info.u-srasbg.fr Absrac: This paper addresses he problem of consrucing

More information

UNDERSTANDING THE DEATH BENEFIT SWITCH OPTION IN UNIVERSAL LIFE POLICIES. Nadine Gatzert

UNDERSTANDING THE DEATH BENEFIT SWITCH OPTION IN UNIVERSAL LIFE POLICIES. Nadine Gatzert UNDERSTANDING THE DEATH BENEFIT SWITCH OPTION IN UNIVERSAL LIFE POLICIES Nadine Gazer Conac (has changed since iniial submission): Chair for Insurance Managemen Universiy of Erlangen-Nuremberg Lange Gasse

More information

Software Exclusivity and the Scope of Indirect Network Effects in the U.S. Home Video Game Market

Software Exclusivity and the Scope of Indirect Network Effects in the U.S. Home Video Game Market Sofware Exclusiviy and he Scope of Indirec Nework Effecs in he U.S. Home Video Game Marke Kenneh S. Cors Roman School of Managemen, Universiy of Torono Mara Lederman Roman School of Managemen, Universiy

More information

Distributed and Secure Computation of Convex Programs over a Network of Connected Processors

Distributed and Secure Computation of Convex Programs over a Network of Connected Processors DCDIS CONFERENCE GUELPH, ONTARIO, CANADA, JULY 2005 1 Disribued and Secure Compuaion of Convex Programs over a Newor of Conneced Processors Michael J. Neely Universiy of Souhern California hp://www-rcf.usc.edu/

More information

Setting Accuracy Targets for. Short-Term Judgemental Sales Forecasting

Setting Accuracy Targets for. Short-Term Judgemental Sales Forecasting Seing Accuracy Targes for Shor-Term Judgemenal Sales Forecasing Derek W. Bunn London Business School Sussex Place, Regen s Park London NW1 4SA, UK Tel: +44 (0)171 262 5050 Fax: +44(0)171 724 7875 Email:

More information

Option Put-Call Parity Relations When the Underlying Security Pays Dividends

Option Put-Call Parity Relations When the Underlying Security Pays Dividends Inernaional Journal of Business and conomics, 26, Vol. 5, No. 3, 225-23 Opion Pu-all Pariy Relaions When he Underlying Securiy Pays Dividends Weiyu Guo Deparmen of Finance, Universiy of Nebraska Omaha,

More information

SPEC model selection algorithm for ARCH models: an options pricing evaluation framework

SPEC model selection algorithm for ARCH models: an options pricing evaluation framework Applied Financial Economics Leers, 2008, 4, 419 423 SEC model selecion algorihm for ARCH models: an opions pricing evaluaion framework Savros Degiannakis a, * and Evdokia Xekalaki a,b a Deparmen of Saisics,

More information

Optimal Investment and Consumption Decision of Family with Life Insurance

Optimal Investment and Consumption Decision of Family with Life Insurance Opimal Invesmen and Consumpion Decision of Family wih Life Insurance Minsuk Kwak 1 2 Yong Hyun Shin 3 U Jin Choi 4 6h World Congress of he Bachelier Finance Sociey Torono, Canada June 25, 2010 1 Speaker

More information

Working Paper No. 482. Net Intergenerational Transfers from an Increase in Social Security Benefits

Working Paper No. 482. Net Intergenerational Transfers from an Increase in Social Security Benefits Working Paper No. 482 Ne Inergeneraional Transfers from an Increase in Social Securiy Benefis By Li Gan Texas A&M and NBER Guan Gong Shanghai Universiy of Finance and Economics Michael Hurd RAND Corporaion

More information