Capacity Planning and Performance Benchmark Reference Guide v. 1.8
|
|
- Myles Goodman
- 8 years ago
- Views:
Transcription
1 Environmenal Sysems Research Insiue, Inc., 380 New York S., Redlands, CA USA TEL FAX Capaciy Planning and Performance Benchmark Reference Guide v. 1.8 Prepared by: ESRI Professional Services Enerprise Implemenaion Services Team Redlands, California July 12, 2009
2 1 OBJECTIVE BENCHMARK USAGE BENCHMARK INFORMATION TYPE BENCHMARK RESULTS CAPACITY CALCULATOR BENCHMARK AND MODELED DATA RELATED INFORMATION ESTIMATING CAPACITY USING BENCHMARK DATA FUNDAMENTAL LAWS, FORMULAS AND DEFINITIONS Sysem Capaciy CPU Service ime Queue ime modeling ADJUSTING FOR THROUGHPUT AND CONCURRENT USERS RELATIVE DIFFERENCE ADJUSTING FOR WORKFLOW RELATIVE DIFFERENCE ADJUSTING FOR TRANSACTION "SIZE" RELATIVE DIFFERENCE ADJUSTING FOR CPU SPEED RELATIVE DIFFERENCE EXAMPLE 1: ESTIMATING REQUIRED NUMBER OF CPU CORES EXAMPLE 2: USING FASTER CPU EXAMPLE 3: USING FASTER CPU TO MAXIMIZE THROUGHPUT EXAMPLE 4: USING MORE CPU CORES EXAMPLE 5: ESTIMATING THE VALUE OF TUNING EXAMPLE 6: CHANGING FREQUENCY OF USER OPERATIONS EXAMPLE 7: NETWORK SIZING - CHANGING THE SIZE OF THE MAP DISPLAY EXAMPLE 8: NETWORK SIZING INCREASING THROUGHPUT WALK-THROUGH OF BENCHMARK RESULT SECTIONS APPLICATION ARCHITECTURE HARDWARE AND SOFTWARE CONFIGURATION BENCHMARK RESULTS Performance and Scalabiliy Key Resource Uilizaion CAPACITY PLANNING MODEL INPUT CPU Service Time Nework Bis/Transacion HELPFUL RESOURCES
3 1 Objecive The objecive of his documen is o provide guidance for undersanding and applying he performance benchmarks published in he Implemenaion Gallery sie. This documen describes each secion of a ypical published benchmark and provides addiional informaion regarding mehodology and definiions. 2 Performance Benchmark Usage The ESRI enerprise esing eam conducs and publishes performance benchmarks of represenaive es resuls in he Implemenaion Gallery. Undersanding hese performance benchmarks can help wih, Sysem sizing Capaciy planning model calibraion and inpu Selecion of opimal echnology archiecures Selecion of opimal applicaion archiecure Wihou relevan performance benchmarks, i may be challenging or impossible o conduc effecive capaciy planning. In many cases, esimaing capaciy wih no es daa or using oudaed capaciy models can lead o increased poenial error when planning a soluion. Performance benchmarks provide only a shor descripion and summary of key resuls. To apply hese repors for capaciy planning of differen sysems, refer o he informaion conained in his documen or reques suppor from ESRI Professional Services. 3 Benchmark Informaion Type Found in he Implemenaion Gallery, a performance benchmark repor ypically conains key es resul informaion and focuses on a single es. In many cases, ESRI conducs several variaions of a similar es by changing only one configuraion parameer a a ime. For example, benchmarking an ArcGIS Server map service may involve varying daa source ype (e.g., file geodaabase, shapefile, or ArcSDE) or image compression ype (e.g., JPEG, PNG 8, PNG 24). Since he esed applicaion and mehodology remain he same, variaion resuls are grouped as supplemenal informaion wihin he performance benchmark repor. 3.1 Benchmark Resuls See Walk-hrough of Benchmark Resuls secion ha describes he informaion presened wihin a ypical benchmark repor in more deail. 3
4 The key par of a performance benchmark repor is Capaciy Planning secion, which provides imporan informaion for sizing esimaes, including: CPU service ime Nework Mbis/ransacion Machine SpecRae (for exrapolaion of resuls for differen hardware) Maximum hroughpu Response ime for a given user load This informaion should be used as an inpu for capaciy planning ools. The benchmark package includes a simple capaciy calculaor, described in he nex secion. 3.2 Capaciy Calculaor Along wih he performance benchmark repor, a simple capaciy calculaor is provided o help calculae he required number of CPUs and nework bandwidh under specified user load, service ime, SpecRae ec. The calculaor is an Excel spreadshee as shown in he following figure. Parameer RT Users Think ST ArcSOC Zoom b Mbis/r b Value Uni 2.80 sec users 6.00 sec 0.60 sec 1.81 Mbis/r SpecRae b PerCPU SpecRae PerCPU %CPU 95 % TH #CPU Mbps 20,864 r/hr 3.63 CPU cores Mbps inpu values calculaed values The calculaions are based on he capaciy planning formulas as described in Fundamenal Laws, Formulas and Definiion secion. This ool can be used for capaciy planning he following: Esimaing number of CPU cores required o suppor a defined user load Esimaing required bandwidh o suppor a defined user load Esimae hroughpu based on a defined user load 4
5 Validaing es resuls hrough comparison wih Capaciy Calculaor values 3.3 Benchmark and Modeled Daa As discussed in he previous secion, performance benchmark resuls can be used o esimae maximum capaciy of a sysem using he Capaciy Calculaor. However, o beer analyze he performance and scalabiliy profile of he benchmark, raw benchmark, modeled daa and comparison chars are provided. This informaion allows an analysis of he enire user load range, no jus a cerain poin of sysem capaciy, as is he case using he Capaciy Calculaor. Beyond his, raw benchmark daa can be used for modeling purposes by alering specific parameers. This allows an invesigaion of he impac of changes such as, 1. Wha happens if a faser CPU is used? 2. Wha happens if more CPU cores are added? 3. Wha happens if ransacion rae varies (hink ime)? The figure below shows he benchmark and modeled daa workshee. Figure 1: Benchmark and Modeled Daa Workshee 5
6 3.4 Relaed Informaion In addiion o hose iems discussed abovem, he following relaed informaion may be included, if feasible, in a performance benchmark package: Daa and relaed ArcMap documen Tes scrip and load es projec (ypically Visual Sudio Tes Team ediion projec) 6
7 4 Esimaing Capaciy Using Benchmark Daa Wihou relevan performance benchmarks, i migh be challenging or impossible o conduc an effecive capaciy analysis. In many cases, "ball-parking" or "raionalizing" leads o increased poenial error ha can jeopardize he success of he projec. This secion describes how o effecively uilize he performance benchmarks published in he Implemenaion Gallery sie and how o model a differen sysem. 4.1 Fundamenal Laws, Formulas and Definiions 7
8 Alhough i is possible o effecively use he Capaciy Calculaor wihou a horough undersanding of he capaciy planning formulas, being familiar wih hese principles will help esimaing he poenial error, validaing he analysis or cusomizing he ool o specific needs. Capaciy planning expressions are as follows: Equaion 1: CPU Service Time # CPU 3600 % CPU ST = TH 100 Equaion 2: Relaive CPU Service Time SpecRaePerCPU b ST = STb SpecRaePerCPU Equaion 3: Response Time as a funcion of CPU Service Time and Queue Time Equaion 4: CPU Sizing # ST TH 100 b CPU = 3600 % CPU SpecRaePerCPU SpecRaePerCPU Equaion 5: Throughpu as a funcion of concurren users and operaion frequency (Response Time + Think Time) TH = RT ST + Q Equaion 6: Throughpu as a funcion of CPU service ime and number of cores Equaion 7: Nework Sizing Mbps Users 3600 = RT + Think = 3600 % CPU # CPU TH = STb 100 TH Mbispr 3600 Where, ST - Service ime RT - Response ime Q - Queue ime b SpecRaePerCPU SpecRaePerCPU b b 8
9 #CPU - Number of CPU cores %CPU - Percenage of CPU uilizaion (Typically a arge hreshold is se beween 85% and 95%.) TH - Maximum hroughpu Think - Assumed hink ime (sec.) beween ransacions Users - User load b (subscriped) Benchmarked inpus (subscriped) Targe oupus SpecRae SpecRae CINT 2006 benchmarked sysem (See hp:// Undersanding Sysem Capaciy Typically, a graphical relaionship may be esablished beween user load or CPU uilizaion and response ime and hroughpu. To undersand his relaionship and deermine capaciy of a sysem, he following graph may be composed: horizonal axis: user load or CPU Uilizaion verical axis: Response Time and Throughpu 9
10 As we can use, when he sysem has higher uilizaion (e.g. a 40-50%), he response ime sar increasing due o increasing queue ime. I should be noed once he maximum hroughpu is reached, adding more load migh acually degrade hroughpu. The capaciy of he sysem may be defined as one of he following: Maximum hroughpu. Desired response ime, e.g. as per Level of Service agreemen Desired sysem uilizaion. A firs error Undersanding CPU Service ime CPU service ime is a measure of he ime required for he CPU o process a reques or ransacion, and is an imporan inpu for capaciy planning. As a general rule, his service ime should remain fairly consan, even as increased load is applied. Performance benchmarks ypically include he CPU service ime for each ier. In order o calculae service ime for a paricular ier, is CPU uilizaion mus be known. Someimes a benchmark is conduced wih each ier on a separae physical machine, allowing uilizaion o be easily found. However, many imes a benchmark is conduced wih all iers on one machine (known as a workgroup configuraion). In his case, each ier s uilizaion may be calculaed by summing he CPU uilizaion of each associaed process. The following are ypical processes encounered: 10
11 Web: w3wp.exe, lsass.exe ArcGIS Server: arcsom.exe, arcsoc.exe ArcGIS Image Server: ESRIImageServiceProvider.exe Daabase: oracle.exe, sqlserver.exe Deailed process informaion is lised in each performance benchmark s appendix Undersanding Queue Time Queue ime is a measure of he ime a reques or ransacion spen waiing o be processed, and is an imporan inpu for capaciy planning. As a general rule, queue ime will increase as load and CPU uilizaion increase, alhough i is someimes considered negligible a lower uilizaion levels ( less han 50%). There are several queuing heory models available, such as M/M/n. In many cases hese generic models provide adequae values for capaciy planning, especially when he sysem is primarily CPU bound. The advanage of performance benchmark daa is ha a cusom model may be developed for each benchmark using regression analysis (a rend line). Typically, an exponenial rend/regression provides he bes fi, as deermined by he coefficien of deerminaion (R 2 ) Helpful Resources Fundamenal Laws hp:// Lile's Law hp://en.wikipedia.org/wiki/lile_law#use_in_performance_esing_of_compuer_sysems 4.2 Throughpu and Concurren Users Variance Undersanding hroughpu, average concurren users, and peak concurren users on an exising environmen is imporan when beginning o plan for capaciy. In many cases average concurren users may be a very small fracion of he peak concurren users; however i could jus as easily be much higher. In many cases i is difficul o esimae hese values, bu a good place o sar is by, Monioring he exising sysem usage Analyzing proposed business processes and exrapolae average concurren users from peak number of users. For example, via web server logs an analysis could be conduced each hour o undersand usage rends. However, if only a oal monhly usage repor is available, an average usage could be calculaed by dividing by of he monh s operaional hours, (e.g. 8 hours a day, 5 days a week). To calculae hroughpu knowing he concurren number of users and frequency of requess or ransacions, use he hroughpu equaion from he previous secion. For example, if users perform an operaion approximaely every 10 seconds (e.g. 1 sec operaion and 9 seconds hink ime), his would equae o hroughpu 1*3600/10= 360 operaions/hour. 11
12 Some mission criical applicaions may have high performance and availabiliy requiremens and should be designed for peak concurren users, however his may increase hardware and license coss. For soluions ha have shor peak usage duraions or if emporary performance degradaion is accepable, hese coss may be reduced by designing for average concurren usage. If he hroughpu canno be exrapolaed from he exising usage, i can be esimaed using he Throughpu equaion from he previous secion. The key inpu values are concurren users and frequency of operaions (hink ime) as shown (highlighed in green) in he following figures for benchmark modeled daa and he capaciy calculaor. Think Time Spec_Per_CPU Mbis_per_Tr SOCCPUCoun Benchmark (B) Targe (T) or Modeled Parameer RT Users Think Value Uni 4.00 Sec 1.00 users 6.00 Sec ST ArcSOC Zoom b 0.60 Sec Mbis/r b 1.81 Mbis/r SpecRae b PerCPU SpecRae PerCPU %CPU 95 % TH 360 r/hr CPU #CPU 0.06 cores Mbps 0.18 Mbps 4.3 Workflow Variance Prior o applying he published ESRI benchmark resuls, i is imporan o undersand differences beween he curren workflow and he performance benchmark so a proper model can be derived. Deermining all deails of he workflow during he early planning phase may be difficul. However, for capaciy planning purposes he focus should primarily be on: Transacions performed frequenly wih a relaively long response ime Bach operaions ransacions wih a long response ime 12
13 For example, a workflow may be composed of: Transacion 1: o Zoom agains dynamic service. o Think 6 seconds. Transacion 2: o Zoom agains dynamic service. o Think 6 seconds. Transacion 3 o Query agains small daase. o Think 6 seconds. Transacion 4 o Zoom 3 agains dynamic service. o Think 6 seconds. Transacion 5 o Prin agains he dynamic service. For capaciy planning purposes, ransacions performed infrequenly and have comparaively shor response imes may be excluded. In he above example, he query ransacion agains a small daase migh be jus a fracion of a zoom operaion agains he dynamic service. Therefore, he focus should be on he zoom and prin ransacions only. Once a workflow is defined for capaciy planning, review he published benchmarks and selec he relevan Performance Benchmark repor (similar applicaion and workflow) from he Implemenaion Gallery. Even excluding insignifican ransacions, i is possible he workflow will be more complex han one paricular performance benchmark, and several may be required. Gahering he CPU service imes for he various ransacions from he respecive benchmarks, he complex workflow may be pieced ogeher. This is discussed in he CPU Service Time secion. 4.4 Transacion "Size" Variance As menioned in he Workflow Variance secion, ransacions define he core unis of work in a workflow. Each ransacion will have a differen size, which can impac how much nework bandwidh is required o suiably ranspor Transacion "size" ypically is relaed o he following; each can have a significan impac: Daa sources ArcMap documens Number of feaures and layers reurned (e.g., zoom, search) Image compression Map sizes Caching To exrapolae from published benchmark resuls, review and accoun for poenial performance facor differences as described in Performance and Scalabiliy secion. 13
14 Once he final size has been undersood, i may be adjused wihin he modeled capaciy resul or as shown below in he sample Capaciy Calculaor. RT Users Think 4.00 sec 1.00 users 6.00 sec ST ArcSOC Zoom b 0.60 sec Mbis/r b 1.81 Mbis/r SpecRae b PerCPU SpecRae PerCPU %CPU 95 % TH 360 r/hr CPU #CPU 0.06 cores Mbps 0.18 Mbps 4.5 Adjusing for CPU Speed Relaive Difference This difference can be measured as a raio of SpecRae CINT 2006 base per core as per hp:// published benchmarks. I should be noed ha under a ligh uder load,he CPU speed, no he number of cores, is he primary conribuor o sysem performance (measured as a response ime). For example, a ypical ESRI benchmark CPU is PowerEdge 1950 (Inel Xeon processor 5160, 3.00 GHz), SpecRae/Core = 53.7/4 = PowerEdge 1950 III (Inel Xeon E5450, 3.00 GHz), SpecRae/Core = 109/8 = The following able is an excerp from a published SpecRae CINT 2006 benchmarked sysem. See hp:// Hardware Vendor Sysem Resul Baseline # Cores Dell Inc. PowerEdge 1950 (Inel Xeon 5140, 2.33 GHz) Dell Inc. PowerEdge 1950 (Inel Xeon 5150, 2.66 GHz) Dell Inc. PowerEdge 1950 (Inel Xeon 5160, 3.00 GHz) Dell Inc. PowerEdge 1950 (Inel Xeon E5335, 2.00 GHz) Dell Inc. PowerEdge 1950 III (Inel Xeon E5410, 2.33 GHz) Dell Inc. PowerEdge 1950 III (Inel Xeon E5420, 2.50 GHz) Dell Inc. PowerEdge 1950 III (Inel Xeon E5430, 2.66 GHz) Dell Inc. PowerEdge 1950 III (Inel Xeon E5450, 3.00 GHz)
15 If all oher facors are consan, for a CPU-bound soluion, he CPU service ime measured from one sysem can be exrapolaed using Relaive CPU Service Time equaion in he previous secion. In our case, he raio is / = I can be concluded ha even hough differen server ypes are used, he relaive CPU difference is marginal. Users are ofen ineresed in relaive performance (response ime difference) beween wo sysems. In case of low CPU uilizaion(less han 50%) queue ime can be considered negligible. However, for a CPU- bound sysem response ime (RT) can be esimaed from a slower o a faser sysem using he following formula: SpecRaePerCPU b RT RTb SpecRaePerCPU 4.6 Example 1: Esimaing required number of CPU cores A hosing company needs o design a sysem o suppor 50 concurren users requesing maps. How many CPU cores are required o suppor his load? Soluion: Sep 1: Deermine he SpecRae of he proposed server using hp:// as described in Adjusing for CPU Speed Relaive Difference secion. IF here is no informaion abou he ype of CPU, assume he laes CPU is used. The spec raes were deermined as Spec_Per_CPU=30. Sep 2: Adjus for hroughpu. Since here is no informaion available abou frequency of ransacions, hroughpu is esimaed a 6 seconds hink ime and 1 second response ime. Sep 3: Selec he relevan Performance Benchmark repor (similar applicaion) from he Implemenaion Gallery. For inpu, use informaion in he Capaciy Planning Model Inpu, including spec rae and CPU service ime. Sep 4: Adjus for ransacion size. IF here is no informaion abou ransacion ype or size, assume he benchmarked CPU service ime. Sep 5: Esimae capaciy of he proposed sysem using Capaciy Calculaor RT 1.00 Sec Users Users Think 6.00 Sec ST ArcSOC Zoom b 0.60 Sec Mbis/r b 1.81 Mbis/r SpecRae b PerCPU
16 SpecRae PerCPU %CPU 95 % TH 25,714 r/hr CPU #CPU 4.48 cores Mbps Mbps Soluion: Approximaely 5 CPU cores are required o suppor he given load. The curren servers are sold ypically wih 4 or 8 cores. Therefore, o accoun for fuure growh i is recommended o use 8-core server. 4.7 Example 2: Using faser CPU A hosing company charges $ 0.01 per map reques wih he performance level of service less han 1 second. The company has more poenial cusomers, however he curren sysem based on 4 core 3 GHz CPU is reaching he capaciy and users repor a degraded performance, wih a map response ime frequenly above 2 seconds. An IT manager considers upgrading he exising server o a newer one. He esimaes he oal cos of upgrading hardware is $30,000 How should he esimae he reurn on invesmen (ROI)? Soluion: Sep 1: Deermine he SpecRae of he exising and proposed (modeled) environmen using hp:// as described in Adjusing for CPU Speed Relaive Difference secion. The spec raes were deermined as Spec_Per_CPU = for he exising sysem and Spec_Per_CPU = 30 for he proposed sysem. Sep 2: Selec relevan Performance Benchmark repor and daa from Implemenaion Gallery. Ensure he benchmark and design applicaions are similar. Sep 3: Model he exising and proposed sysem using benchmark daa. Deermine he maximum hroughpu corresponding o conraced level of service (response ime=1 sec). Noe, we are no evaluaing he number of concurren users, bu he hroughpu. For his analysis, we can assume any hink ime and number of concurren users. For he purpose of analysis we will use he benchmark hink ime of 6 seconds. Also, he benchmark and he exising sysem are he same. If no boh sysem would need o be modeled. We inpu Spec_Per_CPU=30 in his workshee and analyze he resul. ThinkingTime Spec_Per_CPU Mbis_per_Tr SOCCPUCoun Benchmark (B) Targe (T) or Modeled
17 We deermine he exising hroughpu corresponding o he required response ime by drawing a horizonal line (blue) from 1 sec Response Time and inersecing wih he Throughpu curve (blue). We can esimae he exising hroughpu a 19,000 map/hour. We repea he same seps (pink line) and esimae he new hroughpu a 38,000 map/hour a an average response ime less han 1 second for he argeed server Noe, for more precision, we can use Equaion 6: Throughpu as a funcion of CPU service ime and number of cores or benchmark daa o calculae he hroughpu. Since he sysem has o deliver 1 sec response ime, we should firs deermine he corresponding CPU uilizaion from benchmark daa as shown below (we can assume 60% ). 17
18 Sep 4: Conduc Cos/Benefi Analysis The new sysem can suppor an addiional 19,000 map/hour; he esimaed profi is $190/hour. Assuming he sysem will be consisenly uilized a 38,000 maps/hour for 10 hours a day, 5 day a week (10*5*4=200 hr/monh), his invesmen would yield 10*5*4*190=$38,000 poenial profi per monh. 4.8 Example 3: Using faser CPU o maximize hroughpu The same company from he previous Example considers renegoiaing he exising level of service agreemen. The managemen wans o evaluae wha would be an addiional monhly profi if he company did no have o guaranee he performance level of service (response ime under 1 sec)? Soluion: Follow Sep 1-2 from he previous example. Sep 3: Model he exising and proposed sysem. Deermine he maximum hroughpu. 18
19 New max hroughpu max = 47,000 Exising max hroughpu = 20,000 We can summarize he hroughpu and response ime for he exising and upgraded sysem in he following able: Max Throughpu Max User load (r/hr) Before 20, Afer 47, % Improvemen 130% 94% I can be esimaed ha by using faser CPU (30/13.425=2.2 imes faser) we improve boh performance and scalabiliy: Throughpu (scalabiliy) increased by 100% ; he curve exended User load (scalabiliy) increased by 94% Sep 4: Conduc Cos/Benefi Analysis Wih he new sysem we can suppor addiional 47,000-21,000=26,000 map/hour, wih he esimaed profi of $260/hour. Assuming he sysem will be used 10 hours a day, 5 day a week (10*5*4=200 hr/monh), his invesmen would yield 10*5*4*260=$52,000 profi per monh. 4.9 Example 4: Using more CPU cores 19
20 The same hosing company from he previous Example considers adding (doubling) CPU cores o he exising sysem. How will his improve performance and scalabiliy of he sysem? Howis reurn on invesmen esiamed? Soluion: Follow Sep 1-2 from previous Example. Sep 3: Model he exising and proposed sysem. Deermine he maximum hroughpu. In his case, we increase he CPU Coun o 8 in his workshee and analyze he daa. ThinkingTime Spec_Per_CPU Mbis_per_Tr SOCCPUCoun Benchmark (B) Targe (T) or Modeled Throughpu doubled Response ime no changed (up o 25 users) The following able summarizes he key saisics. Response Time (sec) Max Throughpu (a 23 users) (r/hr) Max User load 20
21 Before , Afer , % Improvemen 0% 100% 94% I can be esimaed ha adding more (doubling) CPU cores from 4 o 8: Response ime (performance) remains he same for he moderae user load (less han 25 users); he curve shifed o he righ. Throughpu (scalabiliy) increased by 100%; he curve exended. User load (scalabiliy) increased by 94% Sep 4: Conduc Cos/Benefi Analysis Wih he new sysem we can suppor addiional 43,000-21,000=22,000 map/hour, wih he esimaed profi of $220/hour. Assuming he sysem will be used 10 hours a day, 5 day a week (10*5*4=200 hr/monh), his invesmen would yield 10*5*4*220=$44,000 poenial profi per monh Example 5: Esimaing he value of uning The same hosing company from he previous Example hired a vendor o une heir exising sysem a he cos of $30,000. As a resul i was repor performance improved 50% (response ime 0.5 imes). Wha is he reurn on invesmen for he uning engagemen? Soluion: Follow Sep 1-2 from previous Example. Sep 3: Model he before and afer sysem. Deermine he maximum hroughpu. Based on he response ime reducion informaion, we can assume CPU service ime was reduced o 0.3 seconds. From he benchmark, we deermined he original sysem CPU service ime was equal o 0.6 seconds. We can hen calculae he corresponding capaciy of he sysem (4 core, 95% CPU uilizaion) using Equaion 6: Throughpu as a funcion of CPU service ime and number of cores: 3600 % CPU # CPU T T TH = STb 100 SpecRaePerCPU SpecRaePerCPU TH before uning =(3600*95*4)/(0.6*100)* /13.425= 22,800 r/hr TH afer uning =(3600*95*4)/(0.3*100)* /13.425= 45,600 r/hr T b As expeced, given all oher facors as consan, he reducion of CPU service ime by 50% yields double he hroughpu. Sep 4: Conduc Cos/Benefi Analysis Wih he new sysem we can suppor addiional 45,600-22,800=22,800 map/hour, wih he esimaed profi of $228/hour. Assuming he sysem will be used 10 hours a day, 5 day a week (10*5*4=200 hr/monh), his invesmen would yield 10*5*4*228=$45,600 poenial profi in one monh Example 6: Changing frequency of user operaions 21
22 The same hosing company from he previous Example observed ha he sysem was running a capaciy (95% average CPU uilizaion) and users repored performance degradaion. The IT manager analyzed he previous user workflow and anicipaed ha, users zoomed o an area of ineres hen analyzed he daa for 6 seconds before repeaing he operaion. He now esimaes he new and improved analyical ools allow users o reduce he analysis ime from 6 o 3 seconds. As a resul, users reques a new map every 3 seconds. The same hosing company is negoiaing new level of service agreemen based on he guaraneed maximum number of concurren users. Wha is he maximum number of users he company should agree o? Soluion: Follow Sep 1-2 from he previous example. Sep 3: Model he exising and proposed sysem. Deermine he maximum hroughpu. In his case, reduce Think Time from 6 seconds o 3 seconds. ThinkingTime Spec_Per_CPU Mbis_per_Tr SOCCPUCoun Benchmark (B) Targe (T) or Modeled
23 Max Throughpu no changed Response ime no changed (up o 12 users) Max user load reduced from 51 o 29 The following able summarizes he key saisics. Response Time (sec) Max Throughpu Max User load (a 23 users) (r/hr) Before Afer % Improvemen 0% 0% <43%> I can be esimaed ha by increasing he frequency of requesing maps (shorening hink ime): Response ime (performance) remains he same for he moderae user load (less han 12 users). Maximum hroughpu (scalabiliy) no changed. User load decreased 43%, from 51 o29 users. Sep 4: Conduc Cos/Benefi Analysis Wih he new user workflow, he sysem can suppor a maximum of 29 users. 23
24 4.12 Example 7: Nework sizing - changing he size of he map display The same hosing company from he previous Example hoss an applicaion wih he map display size se a 800 x 600. Users have been requesing a larger map display size o improve usabiliy, e.g x Currenly, he company pays $1000 a monh per 1 Mbps of bandwidh. How much exra should he company charge per reques? Soluion: Follow Sep 1-2 from he previous example. Sep 3: Model he exising and proposed sysem. We analyzed he published benchmarks for a similar applicaion. In addiion o hroughpu, we found ha a map display size of 1200 x 1000 requires 1.81 Mbis/r while an 800 x 600 size requires 0.6 Mbis/r. We can use he Capaciy Calculaor o conduc he analysis. The following able liss he curren requiremens: RT Users Think sec users sec ST ArcSOC Zoom b 0.60 sec Mbis/r b 0.6 Mbis/r SpecRae b PerCPU SpecRae PerCPU %CPU % TH #CPU Mbps 21,000 r/hr CPU cores 3.50 Mbps To calculae he new requiremens, we inpu Mbis/r=1.81. RT Users Think sec users sec ST ArcSOC Zoom b 0.60 sec Mbis/r b 1.81 Mbis/r SpecRae b PerCPU SpecRae PerCPU %CPU % TH #CPU 21,000 r/hr CPU cores 24
25 Mbps Mbps Sep 4: Conduc he Cos/Benefi Analysis Assuming he sysem will be used 10 hours a day, 5 day a week, we esimae he number of requess per monh is 10*5*4 * 21,000 = 4,200,000 reques/monh. The addiional nework cos per monh is ( )*$1000= $7,060. The company should charge exra 7060/4,200,000=$ per reques Example 8: Nework sizing increasing hroughpu The same hosing company from he previous Example upgraded is server hardware and now suppors double he number of requess from 21,000 r/hr o 42,000 r/hr. The company currenly pays $1000 a monh per 1 Mbps. Wha is he addiional monhly nework fee? Soluion: Follow Sep 1-2 from he previous example. Sep 3: Model he exising and proposed sysem. We analyzed he published benchmarks for a similar applicaion. In addiion o hroughpu, we found ha a map display size of 1200 x 1000 requires 1.81 Mbis/r. We can use Capaciy Calculaor o conduc analysis. The following able liss he curren and new nework requiremens: RT Users Think sec users sec ST ArcSOC Zoom b 0.60 sec Mbis/r b 1.81 Mbis/r SpecRae b PerCPU SpecRae PerCPU %CPU % TH #CPU Mbps 21,000 r/hr CPU cores 10.5 Mbps RT sec Users users Think sec ST ArcSOC Zoom b 0.60 sec Mbis/r b 1.81 Mbis/r SpecRae b PerCPU SpecRae PerCPU 25
26 %CPU % TH #CPU Mbps 42,000 r/hr CPU cores Mbps Sep 4: Conduc Cos/Benefi Analysis The company will have o pay ( )*$1000=$10,560 per monh. 5 Walk-hrough of Benchmark Resul Secions The following secions describe he informaion presened wihin a ypical benchmark repor. 5.1 Applicaion Archiecure This secion provides a brief overview of he esed applicaion. For pracical informaion on his opic, see ESRI Applicaion Archiecures. 5.2 Hardware and Sofware Configuraion This secion liss he esed hardware and sofware configuraion. For more informaion, see ESRI Applicaion Archiecures. The following is an example of a es configuraion. Figure 2: Tes Configuraion Example Diagram Key Web SOM SOC RDBMS File 5.3 Benchmark Resuls This is a key secion of he performance benchmark documen. I will repor performance and scalabiliy informaion. 26
27 5.3.1 Performance and Scalabiliy This secion conains key es resuls and summary informaion derived from he raw es values. The figure below shows an example of a key es resuls repor. Figure 3: Key Tes Resuls Sysem capaciy marker Defined by maximum hroughpu Lised under his char you will find a summary of he sysem capaciy. Capaciy of he sysem can be deermined as user load corresponding o one of he following crieria: a firs error maximum hroughpu desired response ime In ESRI benchmarks, capaciy is referred o as user load corresponding o maximum hroughpu. Below is an example of how capaciy informaion is repored: Maximum Throughpu (Transacions/Hour) A User Load Average Response Time (Seconds) 21,
28 For more informaion on how o apply his informaion o your soluion, see Esimaing Capaciy Using Benchmark Daa. For deails on how o analyze load es resuls, see MSDN Library, Analyzing Load Tes Runs DS2009: ArcGIS Server Performance and Scalabiliy Tesing Mehodologies Resource Uilizaion This secion includes key resource uilizaion ha can be used o validae es resuls and idenify soluion bolenecks. For example, in he case as shown below, he green line represens CPU uilizaion and demonsraes he sysem is CPU bound. I can be concluded ha by adding more CPU resources, he capaciy of his soluion could be expanded. Figure 4: Key Resource Uilizaion Couner Insance Caegory Compuer Color User Load _Toal Load Tes: Scenario ArcGIS Server % Processor Time _Toal Processor ArcGIS Server % Idle Time 0 C: Physical Disk ArcGIS Server Available MB - Memory ArcGIS Server Byes Received/Sec. Broadcom BCM5708C NeXreme II GigE Nework Inerface ArcGIS Server Range Min. Max. Avg ,000 5,909 6,378 6, ,000 8,989 84,503 26,905 28
29 For deails on how o analyze load es resuls, see MSDN Library, Analyzing Load Tes Runs DS2009: ArcGIS Server Performance and Scalabiliy Tesing Mehodologies 5.4 Capaciy Planning This secion provides he inpu for he capaciy planning model, ypically found as an Excel spreadshee wihin he benchmark package CPU SpecRae CPU SpecRae SpecRae is a sandardized meric allowing various sysems o be compared. See hp://spec.org for specific informaion and resuls. The following is an example of a repored Spec resuls: SpecRae/CPU = Toal CPU Cores = CPU Service Time CPU service ime is a measure of how many seconds, on average, were needed for he CPU processor o process one reques or ransacion. I is used as an inpu for sizing models. The following is an example of a service ime repored resul: Transacion Size Web ArcGIS SOC/SOM Daabase N/A: RDBMS no uilized The following is an example of nework bandwidh (?) repored resuls: Average map size: 244,275 byes 29
30 6 Helpful Resources DS2009: ArcGIS Server Performance and Scalabiliy Tesing Mehodologies hp://resources.esri.com/arcgisserver/apis/javascrip/arcgis/index.cfm?fa=mediagallerydeails&mediaid=6d73b2db a5154ba hp://proceedings.esri.com/library/userconf/devsummi09/papers/ performanceesing2009_devsummi_pp_v1.pdf Paerns & pracices: Performance Tesing Guidance for Web Applicaions hp://perfesingguide.codeplex.com/release/projecreleases.aspx?releaseid=6690#downloadid=17955 SpecRae CINT 2006 hp:// MSDN, Chaper 2 Performance Modeling hp://msdn.microsof.com/en-us/library/ms aspx Performance Tesing Guidance for Web Applicaions hp://msdn.microsof.com/en-us/library/bb aspx MSDN, Chaper 16 Tesing.NET Applicaion Performance hp://msdn.microsof.com/en-us/library/ms aspx Geing Sared wih Team Sysem Tesing Tools hp://msdn.microsof.com/en-us/library/ms aspx Fiddler hp:// Visual Sudio 2008 Professional Ediion (90-day rial) hp:// MSDN, Chaper 17 Load-Tesing Web Applicaions hp://msdn.microsof.com/en-us/library/bb aspx Creaing a Web Tes hp://msdn.microsof.com/en-us/library/ms aspx 30
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 informationDuration 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 informationHedging 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 informationChapter 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 informationPROFIT 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 informationPerformance 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 informationTask 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 informationChapter 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 informationMorningstar 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 informationDistributing 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 informationGUIDE GOVERNING SMI RISK CONTROL INDICES
GUIDE GOVERNING SMI RISK CONTROL IND ICES SIX Swiss Exchange Ld 04/2012 i C O N T E N T S 1. Index srucure... 1 1.1 Concep... 1 1.2 General principles... 1 1.3 Index Commission... 1 1.4 Review of index
More informationTrends 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 informationIndividual 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 informationINTRODUCTION 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 informationJournal 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 informationSystem 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 informationChapter 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 informationInformation 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 informationWhy 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 informationMaking Use of Gate Charge Information in MOSFET and IGBT Data Sheets
Making Use of ae Charge Informaion in MOSFET and IBT Daa Shees Ralph McArhur Senior Applicaions Engineer Advanced Power Technology 405 S.W. Columbia Sree Bend, Oregon 97702 Power MOSFETs and IBTs have
More informationThe 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 informationINTEREST 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 informationForecasting, 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 informationAppendix 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 informationPolicyCore. 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 informationUSE 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 informationMarket 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 informationTable of contents Chapter 1 Interest rates and factors Chapter 2 Level annuities Chapter 3 Varying annuities
Table of conens Chaper 1 Ineres raes and facors 1 1.1 Ineres 2 1.2 Simple ineres 4 1.3 Compound ineres 6 1.4 Accumulaed value 10 1.5 Presen value 11 1.6 Rae of discoun 13 1.7 Consan force of ineres 17
More informationCable & Wireless Jamaica s Price Cap Plan
Office of Uiliies Regulaion Cable & Wireless Jamaica s Price Cap Plan Deerminaion Noice 2001 Augus 1 Absrac Cable and Wireless Jamaica (C&WJ) has radiionally been regulaed under a rae of reurn regime.
More informationMultiprocessor 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 informationOptimal Growth for P&C Insurance Companies
Opimal Growh for P&C Insurance Companies by Luyang Fu AbSTRACT I is generally well esablished ha new business produces higher loss and expense raios and lower reenion raios han renewal business. Ironically,
More information4. 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 informationStock 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 informationForecasting. Including an Introduction to Forecasting using the SAP R/3 System
Forecasing Including an Inroducion o Forecasing using he SAP R/3 Sysem by James D. Blocher Vincen A. Maber Ashok K. Soni Munirpallam A. Venkaaramanan Indiana Universiy Kelley School of Business February
More informationBid-ask Spread and Order Size in the Foreign Exchange Market: An Empirical Investigation
Bid-ask Spread and Order Size in he Foreign Exchange Marke: An Empirical Invesigaion Liang Ding* Deparmen of Economics, Macaleser College, 1600 Grand Avenue, S. Paul, MN55105, U.S.A. Shor Tile: Bid-ask
More informationLEASING 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 informationCAPt. Print e-procurement: Changing the Face of the Printing Industry CAP VENTURES. Market Forecast for Web-Based Print e-procurement
Prin e-procuremen: Changing he Face of he Prining Indusry Marke Forecas for Web-Based Prin e-procuremen Buyers Crieria for Web Procuremen Opporuniies and Advanages for Prin Service Providers Profiles of
More informationStatistical 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 informationPredicting 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 informationMaking 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 informationUsefulness of the Forward Curve in Forecasting Oil Prices
Usefulness of he Forward Curve in Forecasing Oil Prices Akira Yanagisawa Leader Energy Demand, Supply and Forecas Analysis Group The Energy Daa and Modelling Cener Summary When people analyse oil prices,
More informationt 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 informationChapter 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 informationReturn 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 informationTEMPORAL 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 information11/6/2013. Chapter 14: Dynamic AD-AS. Introduction. Introduction. Keeping track of time. The model s elements
Inroducion Chaper 14: Dynamic D-S dynamic model of aggregae and aggregae supply gives us more insigh ino how he economy works in he shor run. I is a simplified version of a DSGE model, used in cuing-edge
More informationInformation technology and economic growth in Canada and the U.S.
Canada U.S. Economic Growh Informaion echnology and economic growh in Canada and he U.S. Informaion and communicaion echnology was he larges conribuor o growh wihin capial services for boh Canada and he
More informationAutomatic 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 informationChapter 6 Interest Rates and Bond Valuation
Chaper 6 Ineres Raes and Bond Valuaion Definiion and Descripion of Bonds Long-erm deb-loosely, bonds wih a mauriy of one year or more Shor-erm deb-less han a year o mauriy, also called unfunded deb Bond-sricly
More informationBALANCE 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 informationImprovement 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 informationWATER MIST FIRE PROTECTION RELIABILITY ANALYSIS
WATER MIST FIRE PROTECTION RELIABILITY ANALYSIS Shuzhen Xu Research Risk and Reliabiliy Area FM Global Norwood, Massachuses 262, USA David Fuller Engineering Sandards FM Global Norwood, Massachuses 262,
More informationA 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 informationII.1. Debt reduction and fiscal multipliers. dbt da dpbal da dg. bal
Quarerly Repor on he Euro Area 3/202 II.. Deb reducion and fiscal mulipliers The deerioraion of public finances in he firs years of he crisis has led mos Member Saes o adop sizeable consolidaion packages.
More informationThe 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 informationRelationships 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 informationDouble 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 informationJournal of Business & Economics Research Volume 1, Number 10
Annualized Invenory/Sales Journal of Business & Economics Research Volume 1, Number 1 A Macroeconomic Analysis Of Invenory/Sales Raios William M. Bassin, Shippensburg Universiy Michael T. Marsh (E-mail:
More informationVector 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 informationACTUARIAL FUNCTIONS 1_05
ACTUARIAL FUNCTIONS _05 User Guide for MS Office 2007 or laer CONTENT Inroducion... 3 2 Insallaion procedure... 3 3 Demo Version and Acivaion... 5 4 Using formulas and synax... 7 5 Using he help... 6 Noaion...
More informationPrincipal 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 informationDEMAND 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 informationPlanning Demand and Supply in a Supply Chain. Forecasting and Aggregate Planning
Planning Demand and Supply in a Supply Chain Forecasing and Aggregae Planning 1 Learning Objecives Overview of forecasing Forecas errors Aggregae planning in he supply chain Managing demand Managing capaciy
More informationMarkit Excess Return Credit Indices Guide for price based indices
Marki Excess Reurn Credi Indices Guide for price based indices Sepember 2011 Marki Excess Reurn Credi Indices Guide for price based indices Conens Inroducion...3 Index Calculaion Mehodology...4 Semi-annual
More informationNikkei 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 informationClaimCore. Putting Customers at the Core of Your Claims Processes. Integrated Customer Database. R es y me. Ad j u d ic ati o n
ClaimCore for Benefis Overview Feaures & Benefis The Core Suie Technology Services/Educaion Conac Us ClaimCore Puing Cusomers a he Core of Your Claims Processes Handle growh wih a rules-based, cusomer-focused
More informationCHAPTER FIVE. Solutions for Section 5.1
CHAPTER FIVE 5. SOLUTIONS 87 Soluions for Secion 5.. (a) The velociy is 3 miles/hour for he firs hours, 4 miles/hour for he ne / hour, and miles/hour for he las 4 hours. The enire rip lass + / + 4 = 6.5
More informationCeramic Modules And Trends In Efficient Compuing
Cos Opimizaion Model for Business Applicaions in Virualized Grid Environmens Jörg Srebel Universiä Karlsruhe (TU), 76131 Karlsruhe, Germany srebel@iism.uni-karlsruhe.de Absrac. The adven of Grid compuing
More informationLEVENTE 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 informationLECTURE: SOCIAL SECURITY HILARY HOYNES UC DAVIS EC230 OUTLINE OF LECTURE:
LECTURE: SOCIAL SECURITY HILARY HOYNES UC DAVIS EC230 OUTLINE OF LECTURE: 1. Inroducion and definiions 2. Insiuional Deails in Social Securiy 3. Social Securiy and Redisribuion 4. Jusificaion for Governmen
More informationMACROECONOMIC 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 informationForestry profitability panel dataset
Foresry profiabiliy panel daase Wei Zhang, Mou Research Daa Documenaion Mou Economic and Public Policy Research Dae accessed/creaed: Augus 2010 Mou Ref ID: U9970 Suggesed Ciaion: Zhang, Wei. "Foresry Profiabiliy
More informationName: Algebra II Review for Quiz #13 Exponential and Logarithmic Functions including Modeling
Name: Algebra II Review for Quiz #13 Exponenial and Logarihmic Funcions including Modeling TOPICS: -Solving Exponenial Equaions (The Mehod of Common Bases) -Solving Exponenial Equaions (Using Logarihms)
More informationcooking 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 informationDOES 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 informationDYNAMIC 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 informationChapter 9 Bond Prices and Yield
Chaper 9 Bond Prices and Yield Deb Classes: Paymen ype A securiy obligaing issuer o pay ineress and principal o he holder on specified daes, Coupon rae or ineres rae, e.g. 4%, 5 3/4%, ec. Face, par value
More informationCLASSIFICATION OF REINSURANCE IN LIFE INSURANCE
CLASSIFICATION OF REINSURANCE IN LIFE INSURANCE Kaarína Sakálová 1. Classificaions of reinsurance There are many differen ways in which reinsurance may be classified or disinguished. We will discuss briefly
More informationEcotopia: 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 informationExpecaion Heerogeneiy in Japanese Sock Index
JCER DISCUSSION PAPER No.136 Belief changes and expecaion heerogeneiy in buy- and sell-side professionals in he Japanese sock marke Ryuichi Yamamoo and Hideaki Hiraa February 2012 公 益 社 団 法 人 日 本 経 済 研
More informationHow 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 informationModel-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 informationARCH 2013.1 Proceedings
Aricle from: ARCH 213.1 Proceedings Augus 1-4, 212 Ghislain Leveille, Emmanuel Hamel A renewal model for medical malpracice Ghislain Léveillé École d acuaria Universié Laval, Québec, Canada 47h ARC Conference
More informationPRACTICES 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 informationMolding. 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 informationSELF-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 informationMobile Broadband Rollout Business Case: Risk Analyses of the Forecast Uncertainties
ISF 2009, Hong Kong, 2-24 June 2009 Mobile Broadband Rollou Business Case: Risk Analyses of he Forecas Uncerainies Nils Krisian Elnegaard, Telenor R&I Agenda Moivaion Modelling long erm forecass for MBB
More informationImpact 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 informationContrarian insider trading and earnings management around seasoned equity offerings; SEOs
Journal of Finance and Accounancy Conrarian insider rading and earnings managemen around seasoned equiy offerings; SEOs ABSTRACT Lorea Baryeh Towson Universiy This sudy aemps o resolve he differences in
More informationPrivate Cloud Computing for Enterprises: Meet the Demands of High Utilization and Rapid Change
Privae Cloud Compuing for Enerprises: Mee he Demands of High Uilizaion and Rapid Change Wha You Will Learn Enerprise daa ceners are faced wih a criical challenge: The number of applicaions and amoun of
More informationSTRUCTURING EQUITY INVESTMENT IN PPP PROJECTS Deepak. K. Sharma 1 and Qingbin Cui 2
ABSTRACT STRUCTURING EQUITY INVESTMENT IN PPP PROJECTS Deepak. K. Sharma 1 and Qingbin Cui 2 Earlier sudies have esablished guidelines o opimize he capial srucure of a privaized projec. However, in he
More informationAnalogue 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 informationActivity-Based Scheduling of IT Changes
Aciviy-Based Scheduling of IT Changes David Trasour, Maher Rahmoun Claudio Barolini Trused Sysems Laboraory HP Laboraories Brisol HPL-2007-03 July 3, 2007* ITIL, change managemen, scheduling Change managemen
More informationThe 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 informationSignal Processing and Linear Systems I
Sanford Universiy Summer 214-215 Signal Processing and Linear Sysems I Lecure 5: Time Domain Analysis of Coninuous Time Sysems June 3, 215 EE12A:Signal Processing and Linear Sysems I; Summer 14-15, Gibbons
More informationMeasuring the Effects of Exchange Rate Changes on Investment. in Australian Manufacturing Industry
Measuring he Effecs of Exchange Rae Changes on Invesmen in Ausralian Manufacuring Indusry Robyn Swif Economics and Business Saisics Deparmen of Accouning, Finance and Economics Griffih Universiy Nahan
More informationApplication of Fast Response Dual-Colour Pyroelectric Detectors with Integrated Op Amp in a Low Power NDIR Gas Monitor
Applicaion of Fas Response DualColour Pyroelecric Deecors wih Inegraed Op Amp in a Low Power NDIR Gas Monior Infraec GmbH, Gosrizer Sr. 663, 027 Dresden. Inroducion Monioring he concenraion of carbon dioxide
More informationHotel 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 informationRisk 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 informationThe Interest Rate Risk of Mortgage Loan Portfolio of Banks
The Ineres Rae Risk of Morgage Loan Porfolio of Banks A Case Sudy of he Hong Kong Marke Jim Wong Hong Kong Moneary Auhoriy Paper presened a he Exper Forum on Advanced Techniques on Sress Tesing: Applicaions
More information