Large-Scale Network Traffic Monitoring with DBStream, a System for Rolling Big Data Analysis

Size: px
Start display at page:

Download "Large-Scale Network Traffic Monitoring with DBStream, a System for Rolling Big Data Analysis"

Transcription

1 Large-Scale Nework Traffic Monioring wih DBSream, a Sysem for Rolling Big Daa Analysis Arian Bär, Alessandro Finamore, Pedro Casas, Lukasz Golab, Marco Mellia FTW Vienna, Ausria - {baer, Poliecnico di Torino, Ialy - {finamore, Universiy of Waerloo, Canada - Absrac The complexiy of he Inerne has rapidly increased, making i more imporan and challenging o design scalable nework monioring ools. Nework monioring ypically requires rolling daa analysis, i.e., coninuously and incremenally updaing (rolling-over) various repors and saisics over highvolume daa sreams. In his paper, we describe DBSream, which is an SQL-based sysem ha explicily suppors incremenal queries for rolling daa analysis. We also presen a performance comparison of DBSream wih a parallel daa processing engine (Spark), showing ha, in some scenarios, a single DBSream node can ouperform a cluser of en Spark nodes on rolling nework monioring workloads. Alhough our performance evaluaion is based on nework monioring daa, our resuls can be generalized o oher big daa problems wih high volume and velociy. Keywords-Big Daa Analysis; Daa Sream Processing; Nework Daa Analysis; Sysem Performance. I. INTRODUCTION The complexiy of large-scale, Inerne-like neworks is consanly increasing. Wih more services being offered, he massive adopion of Conen Delivery Neworks (CDNs) and Cloud services for raffic hosing and delivery, and he coninuous growh of bandwidh-hungry video-sreaming services, nework and server infrasrucures are becoming exremely difficul o monior. In paricular, he challenge faced by Nework Traffic Monioring and Analysis (NTMA) is o process big, heerogeneous and high-speed daa. Nework monioring daa are heerogeneous by naure, conaining muliple ypes of measuremens coming from differen kinds of logging sysems. In addiion, nework monioring daa come in he form of high-speed sreams, which need o be coninuously analyzed. The noion of a daa sream used in his paper is ha of a coninuous flow of measuremens coming in he form of shor ime slices or baches, e.g., all he TCP flows capured in a backbone link in he las minue. These baches can conain a very large number of samples, given he high capaciy of nework links and he dynamics of Inerne raffic. NTMA and oher monioring applicaions ypically perform wha we refer o as rolling daa analysis: resuls are periodically and incremenally updaed (rolled-over) as new daa arrive. In his paper, we describe DBSream, which is a sysem The research leading o hese resuls has received funding from he European Union under he FP7 Gran Agreemen n (Inegraed Projec mplane.) buil upon he PosgreSQL daabase ha explicily suppors incremenal queries for rolling daa analysis. DBSream, recenly inroduced in [2], ingess daa sreams coming in he form of shor ime-scale aggregaed baches (i.e., minue) from a wide variey of sources (e.g., passive nework raffic daa, acive measuremens, rouer logs and alers, ec.) and performs complex coninuous analysis, aggregaion and filering jobs. DBSream can sore ens of erabyes of heerogeneous daa, and allows boh real-ime queries on recen daa as well as deep analysis of hisorical daa. The echnical conribuions of his paper are as follows. Firs, we presen he Coninuous Execuion Language (CEL), which is a declaraive SQL-based inerface for specifying rolling daa analysis in DBSream. CEL allows DBSream users o rapidly implemen advanced daa analyics which run in parallel and coninuously over ime using jus a few lines of code, acceleraing he developmen of new applicaions. Second, we compare he performance of DBSream wih he popular Spark parallel processing engine using real nework raffic daa from an operaional nework. We show ha rolling queries can be easily implemened in CEL, and a single DBSream node can, in some scenarios, execue hem faser han a cluser of en Spark nodes. The remainder of he paper is organized as follows. Sec. II discusses he relaed work; Sec. III presens he rolling daa analysis capabiliies of DBSream; Sec. IV compares DB- Sream wih Spark; and Sec. V concludes he paper. II. RELATED WORK There has been a grea deal of effor o improve he performance and scalabiliy of radiional daabase managemen sysems by re-implemening he daa processing engine, relaxing daa consisency consrains and/or applying novel daa processing paradigms. Sill, a major limiaion is he inabiliy o cope wih coninuous/rolling analyics. Some relaional daabase sysems suppor maerialized views, bu incremenal view mainenance over ime is resriced o simple ypes of queries such as filers and joins, which is no sufficien for monioring applicaions. Furhermore, NoSQL sysems such as Hadoop [6] have been considered in he conex of nework monioring [], bu hey are suiable for off-line raher han rolling analyics. However, here has been some recen work

2 on enabling real-ime and/or incremenal analyics in NoSQL sysems, such as Incoop [4], Muppe [0], SCALLA [2] and Spark Sreaming [8]. Addiionally, Daa Sream Managemen Sysems (DSMSs) such as Borealis [], Gigascope [6] and Sreambase [5] suppor coninuous processing, bu hey usually canno suppor analyics over hisorical daa. Recenly, Daa Sream Warehouses (DSWs) have been inroduced, which exend radiional daabase sysems wih (nearly) coninuous daa inges and processing. DaaCell [3] and DaaDepo [8] are wo examples, as well as he DBSream sysem presened in his paper. The novely of DBSream is ha i enables users and applicaions o declaraively specify, using arbirary SQL, exacly how o updae a view when a new bach of daa arrives a he sysem. These specificaions may even refer o previously generaed resuls ha are sored in he same view, which, o he bes of our kledge, is no declaraively suppored by any oher sysem. Finally we noe ha here has been recen work in he neworking communiy on exending SQL wih addiional funcionaliies required for nework monioring; examples include complex window expressions [5] and sequenial paerns [9]. However, none of hese proposals include he declaraive rolling analyics ha DBSream suppors. III. ROLLING ANALYTICS IN DBSTREAM DBSream is a rolling analysis sysem implemened as a Daa Sream Warehouse (DSW). Is main purpose is o process and combine daa from muliple sources as hey are produced, creae aggregaions, and sore query resuls for furher processing by exernal analysis modules or visualizaion. The sysem arges, bu is no limied o, coninuous nework monioring. For insance, smar grid, inelligen ransporaion sysems, or any oher use case ha requires coninuous processing of large amouns of daa over ime can ake advanage of DBSream. In his paper, we focus on he following wo imporan feaures of DBSream: I suppors incremenal queries defined using a declaraive inerface based on SQL. Incremenal queries are hose which updae heir resuls by combining newly arrived daa wih previously generaed resuls raher han re-compuing hem from scrach (see Sec. III-A for more deails). This enables efficien processing of wo ineresing groups of queries. Firs, aggregaed variables can be kep for he elemens of he moniored se, e.g., he number of byes uploaded and downloaded by each clien over a sliding window of ime. Second, a se of iems can be moniored over ime by looking a he las sae plus he new daa, e.g., monioring he se of all server IP addresses ha are accessed wihin a sliding window of ime such as in he las wo weeks. In conras o many oher sysems, DBSream does no change he query processing engine. Insead, queries over daa sreams are evaluaed as repeaed invocaions of a process ha consumes a bach of newly arrived daa and combines hem wih he previous resul o come up wih he new resul. Therefore, DBSream is able o reuse he full funcionaliy of he underlying DBMS, including is query processing engine and query opimizer. A. Coninuous Execuion Language (CEL) In his secion we describe he user and applicaion inerface o DBSream, based on SQL, o define rolling analyics. We give a high-level overview of CEL using examples from he neworking domain. Le us assume we have a sream of daa coming from a rouer. I sends one row per minue and per TCP flow wih informaion abou he uploaded and downloaded byes ypical for NeFlow [4] complian rouers. The schema of inpu daa is hus kn. We are ineresed in how many byes are uploaded and downloaded per hour on ha link. In CEL, his can be expressed as he following job: <job inpus="a (window 60min)" oupu="b" schema="serial_ime in4, oal_download in8, oal_upload in8"> <query> selec _STARTTS, sum(download), sum(upload) from A group by _STARTTS </query></job> The inpus aribue defines he inpu window and he oupu aribue defines he desinaion for he resul. Here, a 60-minue window over A is specified, meaning ha for each new hour of daa in A, he query specified in he query elemen will run and is resuls will be appended o able B. DBSream suppors all SQL queries ha are suppored by he underlying DBMS, which is PosgreSQL a he momen. In his example, he query sums up he uploaded and downloaded byes for each hour. The query includes a from A saemen, which does no acually read all of A, only he window of A ha was specified in he inpus saemen (i.e., he mos recen 60 minues). The schema of he oupu sream B is defined using he schema saemen, for which he firs field mus be a imesamp called serial ime. In he above example, he imesamp field is he sar ime of a window, denoed by _STARTTS. Fig. illusraes he suppored window definiions. For each job, one window is defined o be he window and is marked wih he keyword. Afer a job insance is done, he sae of he job is shifed by he size of he window. As soon as here is a full new inpu window, he nex insance of he job is execued. The oher imporan keyword is delay, which shifs a window ino he pas by a given amoun of ime. Par A) of Fig. shows he simples window definiion, similar o he previous example. Only a single window exiss, which is also he window. Therefore, he defined query is execued for every minue of he inpu sream. In Par B), we have wo windows. Every hree minues (he lengh Alhough more complex definiions can be used, here a flow can be idenified by he 5-uple: source IP, desinaion IP, source por desinaion por and IP proocol.

3 window A) Single window Query window 3min window 3min B) Two window query window 3min C) Sliding window query window delay D) Incremenal query window window Fig.. Muliple inpu window definiions possible in DBSream s Coninuous Execuion Language (CEL). of he window), he query for his job reads daa from each of he wo inpu windows. Par C) shows how o independenly define he window lengh and he frequency of query execuion. The window is one minue long, meaning ha he query is execued every minue. However, he query can access he las hree minues of he same sream A hrough he oher window, enabling many ineresing kinds of queries, such as a rolling average, sum or any oher aggregaion. Par D) explains he delay keyword. Here, he same inpu sream is referenced wice, bu for he second window, a delay of one minue is specified. As a resul, he query can read daa from boh he curren minue (window ) and he previous minue (window delay ) of sream A. This makes complex incremenal queries possible, such as a rolling/moving se or median, by being able o reference he previous sae of he daa and compare i wih he curren sae. The main difference beween DBSream s CEL and sream processing languages is he handling and definiion of windows and sliding windows in paricular. For example, in SreamBase [5], windows are specified as [SIZE x ADVANCE y TIME], where x defines he lengh of he window and y he query execuion frequency. In CEL, he keyword corresponds o ADVANCE, bu is specified only once regardless of he number of inpus o make i clear how ofen o re-compue he query. Alhough Fig. shows several possible window ypes, i sill covers only a small fracion of possible window definiions. Since daa in DBSream are always sored on non-volaile sorage, windows can reference pas hisory. I is possible o reference daa from one week or even one monh ago, e.g., o compare he curren sae of he nework wih he pas. B. Examples of Rolling Analyics We give wo more complex incremenal job examples, deailing how rolling analyics can be implemened in CEL. We sar wih a rolling window average shown below, in which every minue, we calculae he average uploaded and downloaded byes over he las hree minues. <job inpus="a (window ) as A, A (window 3min) as A2" oupu="b" schema="serial_ime in4, avg_download floa8, avg_upload floa8"> <query> selec _STARTTS, avg(download), avg(upload) from A2 </query></job> The firs window A is he window ha denoes he query execuion frequency. The second window, A2, is used o run he acual average calculaion. Fig. 2(a) illusraes how he windows over sream A correspond o resuls appended o B; he oupu of he above job is a sequence of new resuls generaed every minue, all of which are sored in B and idenified by heir _STARTTS (window sar ime) imesamps. There is a simple performance opimizaion ha can easily be expressed in CEL: we can pre-aggregae each minue of he daa in A using one query, and hen wrie a second query o add up he hree mos recenly pre-aggregaed windows and compue he hree-minue aggregaes. In he nex example, we compue he disinc se of IP addresses acive in he las hour, updaed every minue. A naive approach is o always scan he las hour of daa from scrach whenever he resul is o be updaed. A more efficien approach is o keep an inermediae sae of disinc IP addresses of he las hour in memory. Then, we can compue he disinc se of IP addresses for he curren minue as he union of he se of IP addresses from he curren minue and hose from he las 59 minues. However, since sae is kep in memory, i mus be re-buil in case of a sysem crash. In CEL, we can implemen he laer via a job ha uses is own pas oupu as inpu. This approach is no only more efficien, bu also, as we show in Sec. IV-C, i is more faul-oleran since he sae of he compuaion is acually sored in he oupu able. The corresponding CEL job definiion is shown below. The inpu is a sream C, which conains, among oher hings, he IP addresses of acive erminals. We wan o ransform sream C ino a new sream D conaining, for each minue, he disinc se of acive IP addresses in he las hour. To achieve his, we firs add a new imesamp las o D recording he ime of he las aciviy of a IP address. Now, from he curren minue of C, we produce a new uple for each disinc IP address and we se he las aciviy o he sar of he curren window using he _STARTTS keyword. From he previous minue of D we selec hose IP addresses which where acive less han one hour ago. We hen combine hose wo resuls using he SQL UNION ALL operaor and selec for each

4 delay delay delay Sream D Sream D Sream D Sream D Sream C Sream C Sream C Sream C (a) Rolling average over he las 3 minues, updaed every minue (b) Complex daa processing flow for an incremenal query. Fig. 2. Daa flow of wo example incremenal jobs; he windows of he curren ask are marked in black. disinc IP address, he curren ime, he maximum value of he las aciviy imesamp, and he IP address iself. By using his feedback loop, we can efficienly compue he se of IP addresses acive in he las hour per minue, wihou keeping any addiional sae informaion. The windows used in his compuaion are visualized in Fig. 2(b). <job inpus="c (window ), D (window delay )" oupu="d" schema="serial_ime in4, las in4, ip ine"> <query> selec _STARTTS, max(las), ip from ( selec _STARTTS as las, ip from C group by,2 union all selec las, ip from D where las <= _STARTTS-60 group by,2) group by,3 </query></job> IV. PERFORMANCE ANALYSIS We compare DBSream wih respec o he sae-ofhe-ar Big Daa framework Spark. Spark is an open-source MapReduce soluion proposed by he UC Berkley Amplab. I explois Resilien Disribued Daases (RDDs), i.e., a disribued memory daa absracion which allows in-memory operaions on large clusers in a faul-oleran manner [7]. This approach has been demonsraed o be paricurlarly efficien [3] enabling boh ieraive and ineracive applicaions in Scala, Java or Pyhon. Spark does no sricly require he presence of Hadoop cluser o run. In fac, despie he sysem is commonly used in combinaion wih Hadoop and HDFS, i also offers a simple, sandalone resource manager o coordinae he aciviies of differen hoss and suppors direc access o he Linux file sysem. A recen evoluion of Spark is Spark Sreaming [8]. Differenly from Spark, which is a pure bach processing soluion, Spark Sreaming enables real ime analysis hrough processing of shor baches. Of paricular ineres are he sysem primiives for defining sliding windows and developing incremenal queries similarly o wha was discussed in Sec. III-A. However, Spark Sreaming arges mainly real ime analysis scenarios and offers limied suppor for processing hisorical daa, which is also required by NTMA. Recen discussions on he Spark Sreaming mailing lis sugges ha some workarounds may be possible 2. However, we were unable o implemen hese and herefore we leave he evaluaion of Spark Sreaming for rolling analyics as fuure work. A. Sysem Seup and Daases We insalled DBSream and Spark on a se of machines having he same hardware (6 core XEON E5 2640, 32 GB of RAM and a 5 HD of 3TB each). One machine has been dedicaed o DBSream, recombining 4 of he available HDs in a RAID0 and insalling PosgreSQL v9.2.4 as a underlying Daabase Managemen Sysem (DBMS). The remaining 0 machines compose a producion Hadoop ha runs CDH 4.6 wih Map Reduce v Job Tracker enabled. On he cluser we also insalled Spark v..0 where we could only enable he sandalone resource manager 3. All machines are locaed wihin he same rack conneced hrough a Gb/s swich. The rack also conains a 40TB NAS used o collec hisorical daa. In paricular, we use four 5 daylong daases, each colleced a a differen nework Vanage Poin (VP) in a real ISP nework beween February 3 and February 7, 204. Each VP is insrumened wih Tsa [7] o produce per-flow ex log files from monioring he raffic of more han 20,000 households. For he purpose of his work we focus only on TCP raffic for which Tsa repors more han 00 nework indexes and generaes a new log file each hour. Overall, each VP generaed a daase of abou 60 GB of raw 2 hp://apache-spark-user-lis n3.nabble.com/ window-analysis-wih-spark-and-spark-sreaming-d8806.hml#a985 3 Apparenly, he implemenaion of Yarn provided in CDH 4.6 has some incompaibiliies wih Spark. These seem be solved in CDH 5 providing Yarn by defaul and a parcel for Spark v..0. Unforunaely, esing such a configuraion requires an upgrade of he node operaing sysems, which was no possible o do in our producion environmen.

5 daa (i.e., abou 5 imes he memory available on each node) for a oal of abou 640 GB (i.e., wice he memory available on he whole cluser). B. Benchmark Definiion We use a se of 7 jobs, represening daily operaions performed on a producion Hadoop cluser we are considering. J: for every 0 minues, i) map each desinaion IP address o is organizaion name (orgname for shor) hrough he Maxmind Orgname daabase (www.maxmind.com/en/geoip2-isp), and ii) for each Orgname found, compue aggregaed raffic saisics (min/max/average Round-Trip Time (RTT), number of disinc server IP addresses, oal number of uploaded/downloaded byes). J2: for every hour, i) compue he orgname-ip mapping as in J, ii) filer all orgname s relaed o he Akamai CDN, and iii) compue some aggregaed saisics (min/max/average RTT). J3: for every hour, i) compue he orgname-ip mapping as in J, and ii) selec he op 0 orgname having he highes number of disinc IP addresses. J4: for every hour, i) ransform he desinaion IP address ino a /24 subne, and ii) selec he op 0 /24 subnes having he highes number of flows. J5: for every minue, for each source IP address, compue he oal number of uploaded/downloaded byes and flows. J6: for every minue, i) find he se of disinc desinaion IP addresses, and ii) use i o updae he se of IP addresses ha were acive over he pas 60 minues. J7: for every minue, i) compue he oal uploaded/downloaded byes for each source IP address, and ii) compue he average over he pas 60 minues. Overall, hese jobs define performance indexes relaed o CDN (J o J4), saisics relaed o he moniored households (J5), and wo incremenal queries (J6 and J7). C. Benchmark implemenaion Each analysis engine has differen peculiariies, properies and uning opions. Differen implemenaions are herefore possible for he defined benchmark. We define a possible implemenaion ha we consider reasonable, discussing possible modificaions ha can affec performance. DBSream benchmark: All queries are expressed in he Coninuous Execuion Language (CEL). The fac ha he oupu of a job is sored on disk and can be used as inpu o anoher job is exploied o achieve beer performance. Fig. 3 shows he resuling job dependencies, where he nodes represen he jobs and an arrow from e.g. job J o J2 means ha he oupu of J is used as inpu o J2. The number nex o an arrow indicaes he size of he inpu window in minues. For insance, J4 and J5 are implemened in a single sep using a inpu window of 60 minues of impored daa. Conversely, J6 is implemened using an inermediae sep J6 prepare which pre-aggregaes he se of acive IP addresses per minue in windows of 0 minues of impored daa. Now, J6 can uilize he oupu of J6 prepare and combine i wih is own pas as oupu, as indicaed by he reflexive arrow saring Impor J4 J5 J6 prepare 60 J J J7 60 J3 0 0 J prepare Fig. 3. Job iner-dependencies for he DBSream implemenaion. Nodes represen jobs and arrows precedence consrains. from and going back ino J6, o compue he final resul. Please noe ha each minue of J6 conains he acive IP addresses of he las 60 minues along wih a imesamp indicaing when hose IPs was las acive. In each one minue sep of J6 his imesamp is checked and IPs which were las acive longer han 60 minues ago are removed. Spark benchmark: Each job is implemened as a separae Spark applicaion using Scala. Each applicaion receives a lis of files locaed on HDFS as inpu and processes hem sequenially. The firs 5 jobs have a sraighforward implemenaion, since he do no presen srong daa dependencies and daa are already spli per hour. The wo incremenal queries, J6 and J7, insead are more complex o implemen. In fac, we need o implemen he logic o sore and updae he daa in windows. We consider a simple approach, creaing an RDD collecing per-minue daa bins on which we hen loop o compose 60 minue windows. Our implemenaion processes daa in a sream of hourly baches, where he resuls are available afer each he processing for each bach has finished. D. Resuls Fig. 4 shows he resuls of running Spark on our cluser of 0 machines. The labels VP and 4VP correspond o he number of vanage poins collecing daa, i.e., 4VP corresponds o four imes as much daa as VP. For he jobs J o J5, Spark offers excellen performance and he whole cluser is perfecly able o parallelize processing, leading o very good resuls. However, jobs J6 and J7 do no scale well. J6 in paricular canno be parallelized very well, since daa have o be synchronized and merged in one single locaion afer each minue. We also ried differen implemenaions of J6 using more complex sraegies and higher number of map/reduce asks aiming o uilize furher cluser resources, which urned ou o be even less performing. Also for J7, he compuaion has o be synchronized for every minue, bu here he amoun of daa is smaller since he oupu for every minue is only a single number. This migh be he reason why J7 J6

6 Execuion Time [minues] Spark, 0 node, VP Spark, 0 node, 2 VP Spark, 0 node, 4 VP Spark, node, VP Impor J J2 J3 J4 J5 J6 J7 Execuion Time [minues] Fig Fig. 5. Performance numbers for differen seups using Spark. Spark, 0 nodes, J J7 Spark, 0 nodes, Impor + J J7 DBSream, node, Impor + J J7 VP 2 VPs 4 VPs Scalabiliy comparison of DBSream and Spark. does show a beer performance han J6. Whereas we can no exclude he possibiliy of more performance implemenaion in Spark for J6 and J7, hese resuls show ha obaining good performance wih Spark in such scenarios is no a all sraighforward. Typical opimizaion used for such a problem such as skip liss or complex ree srucures are hard o parallelize and would no be a fair comparison o a declaraive language like CEL. In Fig. 5, we compare he performance of Spark and DBSream. In DBSream, he oal execuion ime is measured from he sar of he impor of he firs hour of daa unil all jobs finished processing he las hour of daa. For Spark, all jobs were sared a he same ime in parallel. We repor he oal execuion ime of he job finishing las, which was J6 in his experimen. Since for Spark, daa impor and daa processing is separaed, we also repor he solve job processing ime wihou daa impor. For DBSream, he execuion ime increases nearly linearly wih he number of VPs and indicaing a linear scalabiliy, a leas up o he used amoun of VPs.In conras, for Spark he main boleneck is he execuion ime of J6. The oal execuion ime does no increase much wih more VPs, since muliple insances of J6 run in parallel. Therefore, Spark is able o uilize is parallel naure beer, he more jobs are running, whereas DBSream shows beer performance for incremenal jobs. Noably, for he VP case, Spark akes 2.6 imes longer o finish imporing and processing he daa. V. CONCLUSION In his paper, we presened he DBSream sysem for rolling big daa analysis. We focused on he way in which DBSream allows a declaraive specificaion of incremenal queries, including hose which access heir previous resuls in order o compue new resuls. When esed wih real nework monioring daases and workloads, a single DBSream node performed as well as a cluser of en Spark nodes due o he performance advanages of incremenal processing. There are several ineresing direcions for fuure work. One is o develop DBSream on op of a parallel daabase engine such as Greenplum so ha i can scale-ou as well as or beer han Spark on cluser implemenaions. Anoher opion is o use Spark (in paricular, is laes version ha can direcly execue SQL queries) as DBSream s processing engine, and compare he wo archiecures. Finally, since nework monioring (and oher monioring applicaions) ofen involves complex machine learning ha canno be easily expressed in SQL, we will invesigae how o implemen rolling machine learning operaors in DBSream. REFERENCES [] D. Abadi, D. Carney, U. Ceinemel, M. Cherniack, C. Convey, S. Lee, M. Sonebraker, M. Tabul, S. Zdonik, Aurora: a new model and archiecure for daa sream managemen, THe VLDB Journal 2(2):20-39 (2003). [2] A. Bär, P. Casas, L. Golab, A. Finamore, DBSream: an Online Aggregaion, Filering and Processing Sysem for Nework Traffic Monioring, in IWCMC 204-5h TRAC Workshop, 204. [3] Berkeley AMPLab, Big Daa Benchmark, hps://amplab.cs.berkeley. edu/benchmark/, 204. [4] P. Bhaoia, A. Wieder, R. Rodrigues, U. Acar, R. Pasquin, Incoop: MapReduce for Incremenal Compuaions, in SOCC 20, -4. [5] K. Borders, J. Springer, M. Burnside, Chimera: A Declaraive Language for Sreaming Nework Traffic Analysis, in USENIX Securiy Symp., 202. [6] C. Cranor, T. Johnson, O. Spascheck, V. Shkapenyuk, Gigascope: a sream daabase for nework applicaions, in SIGMOD 2003, [7] A. Finamore, M. Mellia, M. Meo, M. Munafo, P. D. Torino, D. Rossi, Experiences of inerne raffic monioring wih sa. IEEE Nework 25(3): 8-4 (20) [8] L. Golab, T. Johnson, J. S. Seidel, V. Shkapenyuk, Sream Warehousing wih DaaDepo, in SIGMOD 2009, [9] L. Golab, T. Johnson, S. Sen, J. Yaes, A sequence-oriened sream warehouse paradigm for nework monioring applicaions, in PAM 202, [0] W. Lam, L. Liu, S. Prasad, A. Rajaraman, Z. Vacheri, A. Doan, Muppe: MapReduce-syle processing of fas daa, PVLDB 5(2):84-825, 202. [] Y. Lee, Y. Lee, Toward Scalable Inerne Traffic Measuremen and Analysis wih Hadoop, in SIGCOMM Compu. Commun. Rev. (CCR) 43():5-3, 202. [2] B. Li, E. Mazur, Y. Diao, A. McGregor, P. Shenoy, SCALLA: A plaform for scalable one-pass analyics using MapReduce, ACM Transacions on Daabase Sysems 37(4):-43, 202. [3] E. Liarou, S. Idreos, S. Manegold, M. Kersen, MoneDB/DaaCell: online analyics in a sreaming column-sore, PVLDB 5(2):90-93, 202. [4] RFC Cisco Sysems NeFlow Services Expor Version 9, [5] SreamBase. Sreambase: Real-ime, low laency daa processing wih a sream processing engine. hp://www.sreambase.com, 204. [6] T. Whie, Hadoop: he definiive guide, O Reilly, 202. [7] M. Zaharia, M. Chowdhury, M. Franklin, S. Shenker, I. Soica, Spark: Cluser Compuing wih Working Ses, in HoCloud workshop, 200. [8] M. Zaharia, T. Das, H. Li, S. Shenker, I. Soica, Discreized Sreams: An Efficien and Faul-Toleran Model for Sream Processing on Large Clusers, in HoCloud workshop, 202.

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

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

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

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

Performance Center Overview. Performance Center Overview 1

Performance Center Overview. Performance Center Overview 1 Performance Cener Overview Performance Cener Overview 1 ODJFS Performance Cener ce Cener New Performance Cener Model Performance Cener Projec Meeings Performance Cener Execuive Meeings Performance Cener

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

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

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

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

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

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

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

Information Systems for Business Integration: ERP Systems

Information Systems for Business Integration: ERP Systems Informaion Sysems for Business Inegraion: ERP Sysems (December 3, 2012) BUS3500 - Abdou Illia, Fall 2012 1 LEARNING GOALS Explain he difference beween horizonal and verical business inegraion. Describe

More information

Impact of scripless trading on business practices of Sub-brokers.

Impact of scripless trading on business practices of Sub-brokers. Impac of scripless rading on business pracices of Sub-brokers. For furher deails, please conac: Mr. T. Koshy Vice Presiden Naional Securiies Deposiory Ld. Tradeworld, 5 h Floor, Kamala Mills Compound,

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

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

PolicyCore. Putting Innovation and Customer Service at the Core of Your Policy Administration and Underwriting

PolicyCore. Putting Innovation and Customer Service at the Core of Your Policy Administration and Underwriting PolicyCore Puing Innovaion and Cusomer Service a he Core of Your Policy Adminisraion and Underwriing As new echnologies emerge and cusomer expecaions escalae, P&C insurers are seeing opporuniies o grow

More information

Process Optimization Time for a Service in 4G Network by SNMP Monitoring and IAAS Cloud Computing

Process Optimization Time for a Service in 4G Network by SNMP Monitoring and IAAS Cloud Computing Process Opimizaion Time for a Service in 4G Nework by SNMP Monioring and IAAS Cloud Compuing Yassine El Mahoi Laboraory of Compuer Science, Operaions Research and Applied Saisics. Téouan, Morocco Souad

More information

Market Liquidity and the Impacts of the Computerized Trading System: Evidence from the Stock Exchange of Thailand

Market Liquidity and the Impacts of the Computerized Trading System: Evidence from the Stock Exchange of Thailand 36 Invesmen Managemen and Financial Innovaions, 4/4 Marke Liquidiy and he Impacs of he Compuerized Trading Sysem: Evidence from he Sock Exchange of Thailand Sorasar Sukcharoensin 1, Pariyada Srisopisawa,

More information

Secure Election Infrastructures Based on IPv6 Clouds

Secure Election Infrastructures Based on IPv6 Clouds Secure Elecion Infrasrucures Based on IPv6 Clouds Firs IPv6-only OpenSack Cloud used o deliver producion services is de-ployed by Nephos6, Cikomm and SnT-Universiy of Luxembourg. Laif Ladid, Presiden,

More information

Chabot College Physics Lab RC Circuits Scott Hildreth

Chabot College Physics Lab RC Circuits Scott Hildreth Chabo College Physics Lab Circuis Sco Hildreh Goals: Coninue o advance your undersanding of circuis, measuring resisances, currens, and volages across muliple componens. Exend your skills in making breadboard

More information

The Grantor Retained Annuity Trust (GRAT)

The Grantor Retained Annuity Trust (GRAT) WEALTH ADVISORY Esae Planning Sraegies for closely-held, family businesses The Granor Reained Annuiy Trus (GRAT) An efficien wealh ransfer sraegy, paricularly in a low ineres rae environmen Family business

More information

OPERATION MANUAL. Indoor unit for air to water heat pump system and options EKHBRD011ABV1 EKHBRD014ABV1 EKHBRD016ABV1

OPERATION MANUAL. Indoor unit for air to water heat pump system and options EKHBRD011ABV1 EKHBRD014ABV1 EKHBRD016ABV1 OPERAION MANUAL Indoor uni for air o waer hea pump sysem and opions EKHBRD011ABV1 EKHBRD014ABV1 EKHBRD016ABV1 EKHBRD011ABY1 EKHBRD014ABY1 EKHBRD016ABY1 EKHBRD011ACV1 EKHBRD014ACV1 EKHBRD016ACV1 EKHBRD011ACY1

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

Inductance and Transient Circuits

Inductance and Transient Circuits Chaper H Inducance and Transien Circuis Blinn College - Physics 2426 - Terry Honan As a consequence of Faraday's law a changing curren hrough one coil induces an EMF in anoher coil; his is known as muual

More information

Automated Allocation of ESA Ground Station Network Services

Automated Allocation of ESA Ground Station Network Services Auomaed Allocaion of ESA Ground Saion Nework Services Sylvain Damiani (), Holger Dreihahn (), Jörg Noll (), Marc Niézee (), and Gian Paolo Calzolari () () VEGA, Aerospace Division Rober Bosch Sraße 7,

More information

Price elasticity of demand for crude oil: estimates for 23 countries

Price elasticity of demand for crude oil: estimates for 23 countries 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

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

MODEL AND ALGORITHMS FOR THE REAL TIME MANAGEMENT OF RESIDENTIAL ELECTRICITY DEMAND. A. Barbato, G. Carpentieri

MODEL AND ALGORITHMS FOR THE REAL TIME MANAGEMENT OF RESIDENTIAL ELECTRICITY DEMAND. A. Barbato, G. Carpentieri MODEL AND ALGORITHMS FOR THE REAL TIME MANAGEMENT OF RESIDENTIAL ELECTRICITY DEMAND A. Barbao, G. Carpenieri Poliecnico di Milano, Diparimeno di Eleronica e Informazione, Email: barbao@ele.polimi.i, giuseppe.carpenieri@mail.polimi.i

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

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

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

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

System Performance Improvement By Server Virtualization

System Performance Improvement By Server Virtualization Sysem Performance Improvemen By Server Virualizaion Hioshi Ueno, Tomohide Hasegawa, and Keiichi Yoshihama Absrac Wih he advance of semiconducor echnology, microprocessors become highly inegraed and herefore

More information

t Thick,intelligent,or thin access points? t WLAN switch or no WLAN switch? t WLAN appliance with 3rd party APs?

t Thick,intelligent,or thin access points? t WLAN switch or no WLAN switch? t WLAN appliance with 3rd party APs? IRONPOINT-FES IronPoin-FES Wireless Soluion IronPoin Benefis Mos Flexible WiFi Archiecure Leading Sandards-based Securiy Enerprise-class Mobiliy Advanced AP Funcionaliy Cenralized Managemen (wired & wireless)

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

Chapter 4: Exponential and Logarithmic Functions

Chapter 4: Exponential and Logarithmic Functions Chaper 4: Eponenial and Logarihmic Funcions Secion 4.1 Eponenial Funcions... 15 Secion 4. Graphs of Eponenial Funcions... 3 Secion 4.3 Logarihmic Funcions... 4 Secion 4.4 Logarihmic Properies... 53 Secion

More information

INTEREST RATE FUTURES AND THEIR OPTIONS: SOME PRICING APPROACHES

INTEREST RATE FUTURES AND THEIR OPTIONS: SOME PRICING APPROACHES INTEREST RATE FUTURES AND THEIR OPTIONS: SOME PRICING APPROACHES OPENGAMMA QUANTITATIVE RESEARCH Absrac. Exchange-raded ineres rae fuures and heir opions are described. The fuure opions include hose paying

More information

Improvement of a TCP Incast Avoidance Method for Data Center Networks

Improvement of a TCP Incast Avoidance Method for Data Center Networks Improvemen of a Incas Avoidance Mehod for Daa Cener Neworks Kazuoshi Kajia, Shigeyuki Osada, Yukinobu Fukushima and Tokumi Yokohira The Graduae School of Naural Science and Technology, Okayama Universiy

More information

Why Did the Demand for Cash Decrease Recently in Korea?

Why Did the Demand for Cash Decrease Recently in Korea? Why Did he Demand for Cash Decrease Recenly in Korea? Byoung Hark Yoo Bank of Korea 26. 5 Absrac We explores why cash demand have decreased recenly in Korea. The raio of cash o consumpion fell o 4.7% in

More information

Can Individual Investors Use Technical Trading Rules to Beat the Asian Markets?

Can Individual Investors Use Technical Trading Rules to Beat the Asian Markets? Can Individual Invesors Use Technical Trading Rules o Bea he Asian Markes? INTRODUCTION In radiional ess of he weak-form of he Efficien Markes Hypohesis, price reurn differences are found o be insufficien

More information

RC Circuit and Time Constant

RC Circuit and Time Constant ircui and Time onsan 8M Objec: Apparaus: To invesigae he volages across he resisor and capacior in a resisor-capacior circui ( circui) as he capacior charges and discharges. We also wish o deermine he

More information

CAREER MAP HOME HEALTH AIDE

CAREER MAP HOME HEALTH AIDE CAREER MAP HOME HEALTH AIDE CAREER MAP HOME HEALTH AIDE Home healh aides are one of he fases growing jobs in New York Ciy. Wih more educaion, home healh aides can move ino many oher ypes of jobs in healh

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

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

Ecotopia: An Ecological Framework for Change Management in Distributed Systems

Ecotopia: An Ecological Framework for Change Management in Distributed Systems Ecoopia: An Ecological Framework for Change Managemen in Disribued Sysems Tudor Dumiraş 1, Daniela Roşu 2, Asi Dan 2, and Priya Narasimhan 1 1 ECE Deparmen, Carnegie Mellon Universiy, Pisburgh, PA 15213,

More information

C Fast-Dealing Property Trading Game C

C Fast-Dealing Property Trading Game C AGES 8+ C Fas-Dealing Propery Trading Game C Y Collecor s Ediion Original MONOPOLY Game Rules plus Special Rules for his Ediion. CONTENTS Game board, 6 Collecible okens, 28 Tile Deed cards, 16 Wha he Deuce?

More information

MACROECONOMIC FORECASTS AT THE MOF A LOOK INTO THE REAR VIEW MIRROR

MACROECONOMIC FORECASTS AT THE MOF A LOOK INTO THE REAR VIEW MIRROR MACROECONOMIC FORECASTS AT THE MOF A LOOK INTO THE REAR VIEW MIRROR The firs experimenal publicaion, which summarised pas and expeced fuure developmen of basic economic indicaors, was published by he Minisry

More information

Stochastic Volatility Option Pricing ASAP

Stochastic Volatility Option Pricing ASAP Cambridge-Kaiserslauern, Financial Mahemaics Workshop 2009 Fraunhofer ITWM, Kaiserslauern 05.05.2009 Sochasic Volailiy Opion Pricing ASAP Dr. Ulrich Nögel, Parner u.noegel@devne.de Disincive Financial

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

Diane K. Michelson, SAS Institute Inc, Cary, NC Annie Dudley Zangi, SAS Institute Inc, Cary, NC

Diane K. Michelson, SAS Institute Inc, Cary, NC Annie Dudley Zangi, SAS Institute Inc, Cary, NC ABSTRACT Paper DK-02 SPC Daa Visualizaion of Seasonal and Financial Daa Using JMP Diane K. Michelson, SAS Insiue Inc, Cary, NC Annie Dudley Zangi, SAS Insiue Inc, Cary, NC JMP Sofware offers many ypes

More information

Sampling Time-Based Sliding Windows in Bounded Space

Sampling Time-Based Sliding Windows in Bounded Space Sampling Time-Based Sliding Windows in Bounded Space Rainer Gemulla Technische Universiä Dresden 01062 Dresden, Germany gemulla@inf.u-dresden.de Wolfgang Lehner Technische Universiä Dresden 01062 Dresden,

More information

17 Laplace transform. Solving linear ODE with piecewise continuous right hand sides

17 Laplace transform. Solving linear ODE with piecewise continuous right hand sides 7 Laplace ransform. Solving linear ODE wih piecewise coninuous righ hand sides In his lecure I will show how o apply he Laplace ransform o he ODE Ly = f wih piecewise coninuous f. Definiion. A funcion

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

The naive method discussed in Lecture 1 uses the most recent observations to forecast future values. That is, Y ˆ t + 1

The naive method discussed in Lecture 1 uses the most recent observations to forecast future values. That is, Y ˆ t + 1 Business Condiions & Forecasing Exponenial Smoohing LECTURE 2 MOVING AVERAGES AND EXPONENTIAL SMOOTHING OVERVIEW This lecure inroduces ime-series smoohing forecasing mehods. Various models are discussed,

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

Information Theoretic Evaluation of Change Prediction Models for Large-Scale Software

Information Theoretic Evaluation of Change Prediction Models for Large-Scale Software Informaion Theoreic Evaluaion of Change Predicion Models for Large-Scale Sofware Mina Askari School of Compuer Science Universiy of Waerloo Waerloo, Canada maskari@uwaerloo.ca Ric Hol School of Compuer

More information

µ r of the ferrite amounts to 1000...4000. It should be noted that the magnetic length of the + δ

µ r of the ferrite amounts to 1000...4000. It should be noted that the magnetic length of the + δ Page 9 Design of Inducors and High Frequency Transformers Inducors sore energy, ransformers ransfer energy. This is he prime difference. The magneic cores are significanly differen for inducors and high

More information

Predicting Stock Market Index Trading Signals Using Neural Networks

Predicting Stock Market Index Trading Signals Using Neural Networks Predicing Sock Marke Index Trading Using Neural Neworks C. D. Tilakarane, S. A. Morris, M. A. Mammadov, C. P. Hurs Cenre for Informaics and Applied Opimizaion School of Informaion Technology and Mahemaical

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

DDoS Attacks Detection Model and its Application

DDoS Attacks Detection Model and its Application DDoS Aacks Deecion Model and is Applicaion 1, MUHAI LI, 1 MING LI, XIUYING JIANG 1 School of Informaion Science & Technology Eas China Normal Universiy No. 500, Dong-Chuan Road, Shanghai 0041, PR. China

More information

SKF Documented Solutions

SKF Documented Solutions SKF Documened Soluions Real world savings and we can prove i! How much can SKF save you? Le s do he numbers. The SKF Documened Soluions Program SKF is probably no he firs of your supplier parners o alk

More information

Time Series Prediction of Web Domain Visits by IF-Inference System

Time Series Prediction of Web Domain Visits by IF-Inference System Time Series Predicion of Web Domain Visis by IF-Inference Sysem VLADIMÍR OLEJ, JANA FILIPOVÁ, PETR HÁJEK Insiue of Sysem Engineering and Informaics Faculy of Economics and Adminisraion Universiy of Pardubice,

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

Double Entry System of Accounting

Double Entry System of Accounting CHAPTER 2 Double Enry Sysem of Accouning Sysem of Accouning \ The following are he main sysem of accouning for recording he business ransacions: (a) Cash Sysem of Accouning. (b) Mercanile or Accrual Sysem

More information

Chapter 2 Problems. 3600s = 25m / s d = s t = 25m / s 0.5s = 12.5m. Δx = x(4) x(0) =12m 0m =12m

Chapter 2 Problems. 3600s = 25m / s d = s t = 25m / s 0.5s = 12.5m. Δx = x(4) x(0) =12m 0m =12m Chaper 2 Problems 2.1 During a hard sneeze, your eyes migh shu for 0.5s. If you are driving a car a 90km/h during such a sneeze, how far does he car move during ha ime s = 90km 1000m h 1km 1h 3600s = 25m

More information

Mathematics in Pharmacokinetics What and Why (A second attempt to make it clearer)

Mathematics in Pharmacokinetics What and Why (A second attempt to make it clearer) Mahemaics in Pharmacokineics Wha and Why (A second aemp o make i clearer) We have used equaions for concenraion () as a funcion of ime (). We will coninue o use hese equaions since he plasma concenraions

More information

Research Article Survey for Sensor-Cloud System from Business Process Outsourcing Perspective

Research Article Survey for Sensor-Cloud System from Business Process Outsourcing Perspective Inernaional Disribued Sensor Neworks Volume 2015, Aricle ID 917028, 5 pages hp://dx.doi.org/10.1155/2015/917028 Research Aricle Survey for Sensor-Cloud Sysem from Business Process Ousourcing Perspecive

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

The Journey. Roadmaps. 2 Architecture. 3 Innovation. Smart City

The Journey. Roadmaps. 2 Architecture. 3 Innovation. Smart City The Journe 1 Roadmaps 2 Archiecure 3 Innovaion Uili Mobili Living Enr Poins o a Grid Journe Sraeg COMM projec Evoluion no Revoluion IT Concerns Daa Mgm Inegraion Archiecure Analics Regulaor Analics Disribued

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

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

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

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

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

Information Theoretic Approaches for Predictive Models: Results and Analysis

Information Theoretic Approaches for Predictive Models: Results and Analysis 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

GoRA. For more information on genetics and on Rheumatoid Arthritis: Genetics of Rheumatoid Arthritis. Published work referred to in the results:

GoRA. For more information on genetics and on Rheumatoid Arthritis: Genetics of Rheumatoid Arthritis. Published work referred to in the results: For more informaion on geneics and on Rheumaoid Arhriis: Published work referred o in he resuls: The geneics revoluion and he assaul on rheumaoid arhriis. A review by Michael Seldin, Crisopher Amos, Ryk

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

The Roos of Lisp paul graham Draf, January 18, 2002. In 1960, John McCarhy published a remarkable paper in which he did for programming somehing like wha Euclid did for geomery. 1 He showed how, given

More information

Full-wave rectification, bulk capacitor calculations Chris Basso January 2009

Full-wave rectification, bulk capacitor calculations Chris Basso January 2009 ull-wave recificaion, bulk capacior calculaions Chris Basso January 9 This shor paper shows how o calculae he bulk capacior value based on ripple specificaions and evaluae he rms curren ha crosses i. oal

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

Efficient big data processing strategy based on Hadoop for electronic commerce logistics

Efficient big data processing strategy based on Hadoop for electronic commerce logistics Absrac Efficien big daa processing sraegy based on Hadoop for elecronic commerce logisics Jiaojin Ci Insiue of Economy and Managemen, Nanyang Normal Universiy,Nanyang, 473061, China Corresponding auhor

More information

CHARGE AND DISCHARGE OF A CAPACITOR

CHARGE AND DISCHARGE OF A CAPACITOR REFERENCES RC Circuis: Elecrical Insrumens: Mos Inroducory Physics exs (e.g. A. Halliday and Resnick, Physics ; M. Sernheim and J. Kane, General Physics.) This Laboraory Manual: Commonly Used Insrumens:

More information

A Joint Optimization of Operational Cost and Performance Interference in Cloud Data Centers

A Joint Optimization of Operational Cost and Performance Interference in Cloud Data Centers A Join Opimizaion of Operaional Cos and Performance Inerference in Cloud Daa Ceners Xibo Jin, Fa Zhang, Lin Wang, Songlin Hu, Biyu Zhou and Zhiyong Liu Insiue of Compuing Technology, Chinese Academy of

More information

Return Calculation of U.S. Treasury Constant Maturity Indices

Return Calculation of U.S. Treasury Constant Maturity Indices Reurn Calculaion of US Treasur Consan Mauri Indices Morningsar Mehodolog Paper Sepeber 30 008 008 Morningsar Inc All righs reserved The inforaion in his docuen is he proper of Morningsar Inc Reproducion

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

cooking trajectory boiling water B (t) microwave 0 2 4 6 8 101214161820 time t (mins)

cooking trajectory boiling water B (t) microwave 0 2 4 6 8 101214161820 time t (mins) Alligaor egg wih calculus We have a large alligaor egg jus ou of he fridge (1 ) which we need o hea o 9. Now here are wo accepable mehods for heaing alligaor eggs, one is o immerse hem in boiling waer

More information

Caring for trees and your service

Caring for trees and your service Caring for rees and your service Line clearing helps preven ouages FPL is commied o delivering safe, reliable elecric service o our cusomers. Trees, especially palm rees, can inerfere wih power lines and

More information

DOES TRADING VOLUME INFLUENCE GARCH EFFECTS? SOME EVIDENCE FROM THE GREEK MARKET WITH SPECIAL REFERENCE TO BANKING SECTOR

DOES TRADING VOLUME INFLUENCE GARCH EFFECTS? SOME EVIDENCE FROM THE GREEK MARKET WITH SPECIAL REFERENCE TO BANKING SECTOR Invesmen Managemen and Financial Innovaions, Volume 4, Issue 3, 7 33 DOES TRADING VOLUME INFLUENCE GARCH EFFECTS? SOME EVIDENCE FROM THE GREEK MARKET WITH SPECIAL REFERENCE TO BANKING SECTOR Ahanasios

More information

A One-Sector Neoclassical Growth Model with Endogenous Retirement. By Kiminori Matsuyama. Final Manuscript. Abstract

A One-Sector Neoclassical Growth Model with Endogenous Retirement. By Kiminori Matsuyama. Final Manuscript. Abstract A One-Secor Neoclassical Growh Model wih Endogenous Reiremen By Kiminori Masuyama Final Manuscrip Absrac This paper exends Diamond s OG model by allowing he agens o make he reiremen decision. Earning a

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

1 HALF-LIFE EQUATIONS

1 HALF-LIFE EQUATIONS R.L. Hanna Page HALF-LIFE EQUATIONS The basic equaion ; he saring poin ; : wrien for ime: x / where fracion of original maerial and / number of half-lives, and / log / o calculae he age (# ears): age (half-life)

More information

Follow links Class Use and other Permissions. For more information, send to:

Follow links Class Use and other Permissions. For more information, send  to: COPYRIGHT NOTICE: David A. Kendrick, P. Ruben Mercado, and Hans M. Amman: Compuaional Economics is published by Princeon Universiy Press and copyrighed, 2006, by Princeon Universiy Press. All righs reserved.

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

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

Molding. Injection. Design. GE Plastics. GE Engineering Thermoplastics DESIGN GUIDE

Molding. Injection. Design. GE Plastics. GE Engineering Thermoplastics DESIGN GUIDE apple GE Plasics GE Engineering Thermoplasics DESIGN GUIDE Wall Thickness Paring Lines Ejecion Appearance Pars Ribs/Gusses Bosses Holes Depressions Radii, Filles and Corners Molding Design Injecion s for

More information

The Transport Equation

The Transport Equation The Transpor Equaion Consider a fluid, flowing wih velociy, V, in a hin sraigh ube whose cross secion will be denoed by A. Suppose he fluid conains a conaminan whose concenraion a posiion a ime will be

More information

Software Project Management tools: A Comparative Analysis

Software Project Management tools: A Comparative Analysis Sofware Projec Managemen ools: A Comparaive Analysis Mrs. Sonali Nemade Pad.Dr.D.Y.Pail A.C.S. College, Pimpri (India) sonali_namade@yahoo.co.in Mrs. Madhuri.A. Darekar Pad.Dr.D.Y.Pail A.C.S. College,

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