Development of Mathematical Model for MarketOriented Cloud Computing


 Darcy Powers
 2 years ago
 Views:
Transcription
1 Intenational Jounal of Compute Applications ( ) Volume 9 No.11, Novembe 2010 Development of Mathematical Model fo MaketOiented Cloud Computing K.Mukhejee Depatment of Compute Science & Engineeing. Bila Institute of Technology Mesa, Ranchi, India G.Sahoo Depatment of Infomation Technology Bila Institute of Technology Mesa, Ranchi, India ABSTRACT Cloud computing is an emeging appoach to a shaed infastuctue of computing esouces in which lage pools of systems o clouds ae linked togethe via the intenet to povide IT sevices, such as secuely managing billions of online tansactions and othe highly dataintensive application. Cloud computing is being diven by seveal factos, including the damatic gowth of connected devices, such as mobile phones, smat cads and othe devices. It can be used to beak many computing tasks into many smalle pieces and managed in paallel on a massive scale. Thus, Cloud computing offes a simplified, centalized platfom fo use when needed, loweing costs and enegy use. But cloud computing is in its nascent stage of pogession. Thee ae no mathematical models egading the pefomance analysis of cloud computing. In this pape, we intend to intoduce Mathematical Model fo cloud computing. Keywods Web Sevice; Ant System; Scalability 1. INTRODUCTION Cloud computing povides a platfom fo the execution of massive tasks on cloud instead of the execution of tasks on uses Pesonal Computes, Seves etc. It is vey beneficial fo small oganizations that cannot affod huge investment on thei IT secto but at the same time expect maximum benefit fom this suppoting industy in ode to suvive in today s complex competitive business wold. Cloud computing can help such oganizations by poviding massive computing powe, unlimited stoage capacity, less maintenance cost, availability of useful websevices etc. As pe Buyya et. all [7], A Cloud is a type of paallel as well as distibuted system consisting of a collection of inteconnected and vitualized computes that ae dynamically povisioned and pesented as one o moe unified computing esouces based on sevicelevel ageements established though negotiation between the sevice povide and consume. The definition clealy implies that thee is a Sevice Level Ageement (SLA) between the povide and the consume fo getting sevices fom cloud on pay pe use basis. Actually Cloud computing offes the thee layes of abstaction such as Infastuctue as a Sevice (IaaS), Platfom as a Sevice(PaaS), Softwae as a Sevice (SaaS). SaaS povides diffeent types of applications as a Sevice fo the end use. It includes diffeent useful websevices. PaaS povides a standad platfom fo bette execution of application with pope exploitation of physical esouces using Middlewae Sevices. PaaS includes Database sevices, Middlewae Sevices etc, In this endeavo we intend to thow light on Softwae as a sevice (SaaS). IaaS povides the infastuctue of cloud consisting of physical esouces like CPU, Stoage, and Netwok etc. Again middlewae of PaaS consists of Coe Middlewae and use level middlewae. Coe Middlewae povides a set of sevices [7] including Admission Contolle, Sevice Request Examine Monitoing, Accounting Billing etc; wheeas the use level middlewae povides access point of sevices as deliveed by the Coe Middlewae. The Sevice Request Examine and Admission Contolle accepts the equest afte ensuing that thee is an availability of esouces fo caying out the equest. Fo this, the Sevice Request Examine and Admission Contolle inteacts continuously with Vitual Machine Monito mechanism egading esouce availability; while the Picing Mechanism decides the chages fo equest sevice. The final cost of the equest sevice is fixed by the Accounting Mechanism, based on the actual usage of esouces fo the equested sevice. The ole of Vitual Machine Monito Mechanism is to keep tack of the availability of Vitual Machines (VM). The Dispatche Mechanism executes the tasks on allocated Vitual Machines, whee as the Sevice Request Monito keeps tack of the execution of the sevice equest. The Vitual Machines execute the job on physical machines, which consist of multiple computing seves, data stoage etc. It is obseved that pefomance monitoing of any application in PaaS is always complex, diffeent and challenging. In the absence of pecise knowledge about the availability of esouces at any point of time and due to the dynamic esouce usage scenaio, pedictionbased analysis is not possible. Thus, pefomance analysis in PaaS must be chaacteized by dynamic data collection (as pefomance poblems must be identified duing untime), data eduction (as the amount of monitoing data is lage), lowcost data captuing (as ovehead due to instumentation and pofiling may contibute to the application pefomance), and adaptability to heteogeneous envionment. Just like the day to day utility sevices such as wate, gas, electicity etc ae essential fo the smooth unning of ou daily lives; the tend of cloud computing [16] is also to povide diffeent IT esouces on demand on a pay pe use basis. The IT esouces include diffeent computational web sevices of diffeent natue like tax calculation web sevice, weathe infomation web sevice, shipping status web sevice etc. The aim of Cloud computing is to be global and to povide such computational web sevices to the masses, anging fom the end use that hosts its pesonal documents on the intenet to entepises outsoucing thei entie IT infastuctue to extenal data centes. In fact, Cloud has given a new appoach to make IT a eal utility which is global and complete fo the end uses. The agenda of computational web sevice of SaaS is to offe simple 19
2 Intenational Jounal of Compute Applications ( ) Volume 9 No.11, Novembe 2010 web methods that computational clients can call to pefom application specific computation on thei own data. Web Sevices ae pogammable and eusable. They ae available anywhee via the intenet. Pogams built on the basis of this model will un acoss multiple websites extacting infomation fom each of them and combining and deliveing it in a customized fom to any device anywhee in the wold. The potential of web sevices is unlimited. Fo example, a softwae company may povide a web sevice to calculate income tax. Any company that wants to calculate income tax can subscibe to this web sevice. The company offeing the sevice can dynamically update it to accommodate new taxation ates. The subscibes need not to do anything to get the new updates. In futue a collection of such web sevices may eplace packaged softwae. As web sevices beak down the distinction between the intenet, standalone applications and computing devices of evey kind, they enable business to collaboate and offe an unpecedented ange of integated and customized solutions that enable thei customes to act on infomation at any time, any place and on any device. Oganization of this pape is as follows: Related wok is discussed in Section II. Poposed Mathematical Model of Cloud Computing is discussed in Section III. Poposed Algoithm is discussed in Section IV. Section V and Section VI give details of the expeimental setup and esults. Section 7 concludes with a diection of the futue wok.. 2. RELATED WORK The potentiality of computing utility was fist pointed by Leonad Kleinock[1], one of the pionee eseache, who seeded the idea of intenet. He had pedicted long back, egading the wide use of compute utilities as ou day to day utilities like Electical Utilities, Telephone Utilities etc. Today, his vision came into existence in the fom of Cloud computing. Cloud computing povides diffeent utilities in the fom of websevices, which ae pay pe use basis. Most of the existing wok in web sevice pefomance focuses on the latest tend of technologies and standads. Andeozzi et al. [2] pesent a model fo igoous epesentation of sevice chaacteistics. Gouscos et al. [3] pesents a simple appoach to model cetain web sevice management attibutes. Thomas et al.[4] epesent distibuted web sevice by modeling the flow of messages and methods in a web sevice tansaction. Tue et al.[5] discuss design stategies to impove the pefomance of web sevice. Levy et al.[6] pesent an achitectue and pototype implementation of pefomance management system fo cluste based web sevice. Cadellini et al. [15] conside diffeent categoies of web applications, and evaluate how static, dynamic and secue web sevice equests affect pefomance and quality of sevice at distibuted web sites. Buyya et. al.[7] have poposed the use of maket oiented esouce management in ode to egulate the supply and demand of cloud esouces at maket equilibium. Again Buiya et. They have poposed the achitectue fo maket oiented allocation of esouces within clouds. Xinhui et. al. [9] have poposed total cost of owneship of cloud computing using a suite of metics and fomulas. But none of above mentioned woks have given any systematic mathematical model egading the analysis of cloud computing. 3. PROPOSED MATHEMATICAL MODEL OF MARKED ORIENTED CLOUD COMPUTING In this pape, we pesent a mathematical model of Maketoiented cloud. The achitectue of maket oiented cloud computing[7] implies that thee ae thee impotant agents viz. Vitual Machine(VM) Monito, Dispatche and Sevice Request Monito, which collectively guide the sevice equest examine and contolle, whethe to accept a job o not and if accepted, how to execute the job effectively and efficiently. Fo these agents to wok effectively and efficiently, we popose Bee and Ant colony system. It is obseved that honey bees [17] wok in a decentalized, self oganized manne. The honey bee colonies pactice division of labo between the foges, who wok in the field collecting the necta and the foodstoes who wok in the hive to stoe necta. The foge bees seach the necta at andom and afte getting specific necta it etuns back to the hive and stats dancing in ode to motivate the followe foges to access that vey necta. The duation of this dance is closely coelated with the seach time expeienced by the dancing bees. The VM Monito is consideed as a hive that consists of bee agents. Fom the bee hive, the agents stat to exploe fo the pupose of collecting the infomation egading esouce availability. Afte etuning to the hive, they stat to pefom dances on the dancing floo in ode to communicate about the food souce i.e. the availability of Vitual Machines. The oientation as well as the duation of dance of bees infom the othe bees egading the exact place as well as its ichness[12][13][14]. Thee ae two types of dance of bees viz. temble dance(t.d) and waggle dance(w.d). The W.D of bees implies that thee is no availability of Vitual Machine fo caying out the new job, whee as the T.D dance of bees implies the availability of V.M fo caying out new jobs. The bees existing in the Sevice Request Monito hive obseve the temble dance of the bees on the dancing floo by VM Monito bees and hence stat to find the necta (i.e. available VMs), in ode to eschedle the pending jobs on the available VMs and hence enhance the pefomance of the Cloud. The waggle dance helps the Sevice Request Contolle and Examine to take the decision of not accepting any new job, wheeas temble dance helps the Sevice Request Contolle and Examine to entetain new jobs and to cay out jobs on V.M by the Dispatche. Now in ode to enhance the pefomance of dispatche, we popose that dispatche should use ant colony system in ode to assign jobs to the available V.Ms. Ant System was poposed by Doigo et al.[18]at the yea In Ant System, ants built tou based on pobabilistic action choice ule, called andom popotional ule. Ant Colony woks to find the shotest paths between food souces and nest[18].due to thei esticted visibility, ants inteact among themselves, by dopping a chemical known as pheomone, which guides them on thei way fom the nest to the food souce and vice vesa. In this pape neithe ant system no bee colony has been discussed in details. In this pape, we have poposed the Mathematical Model of cloud and tavesing cost of job assignment to diffeent VMs by the Dispatche. Thus ole of VM Monito and Sevice Request Monito ae beyond the scope of this pape. Hee we have focused only on the ole of Dispatche in ode to allocate jobs to 20
3 Intenational Jounal of Compute Applications ( ) Volume 9 No.11, Novembe 2010 available VMs based on ant colony system. Fo this, we have poposed a Mathematical Model. In ode to pesent ou new mathematical model, let us conside undiected gaph G(V,A,C), whee V= { 0,1,2,..,n} is a set of nodes epesenting Vitual Machines. Hee we assume that node 0 epesents the dispatche, wheeas node 1 and onwads epesenting available Vitual Machines. Let A = { (i,j): i,j ε V, i j} is a set of acs joining the nodes, having a tavel cost(i.e. distances). Let C = { c ij: i,j ε V, i j } is the cost of tavese between the nodes. Let Ω be the total numbe of ants each with capacity ξ c, assign jobs to M c (Vitual Machine) based on the stochastic demands. Hee Vitual Machine demands ae stochastic vaiables Ψ i, whee i = { 0,1,, n}, which ae independently distibuted with known distibutions. Hee the actual demand of each Vitual Machine is not known unless and until the ant is not aiving at the Vitual Machine. Hee we assume that the demand Ψ i does not exceed the ξ c and follows a discete distibution and pobability mass function p id. p id = Pob(Ψ i = d), Whee d is demand of Vitual Machine and d ε W, set of Whole Numbe. Accoding to the Vitual Machine s actual demand the ant decides whethe to poceed to the next Vitual Machine o to go back to the job execute fo estocking of new job. Sometimes the choice of estocking is bette one, when the ants ae not caying any jobs to assign o do not have sufficient load to seve poceeding Vitual Machine s demand. This action is called peventive estocking. The goal of peventive estocking is backtacking of the ants when they do not have sufficient jobs to assign poceeding Vitual Machine. Thus ants make back and foth tip to the job execute and Vitual Machine fo assigning the jobs to the available Vitual Machine. Let Z i,j,k = Binay flow vaiable Obviously Z i,j,k = 1, if path(i,j) is tavesed by k th agent. = 0, othewise J i,k = Quantity of job is supplied at i th Vitual Machine by k th agent. Ω = Total Numbe of ants initially available at the Job Execute. δ = Remaining jobs associated with the ants. A c = Capacity of ants fo caying out jobs. ф jk p ( δ) = Expected Cost of the move fowad action. ф jk ( δ) = Expected cost of peventive estocking. If ф jk ( δ) is the job assignment to the j th Vitual Machine by the k th ant then ou poposed objective function is: ф jk ( δ) = Minimum ( ф jk p ( δ), ф jk ( δ)) Whee the paametes ae given below: ф jk p (δ)=c j,j+1+ ф j+1,k(δd)p i+1,d + 1 d:d<= δ + [2C j+1,0+ф j+1,k(δ+a cd)p j+1,d] d:d>δ Again ф jk ( δ) be given as: ф jk ( δ) = C j, 0 + C 0, j+1 + ф j+1,k (A cd)p j+1,d 2 D d=1 Now we discuss two cases as follows: Case I : When δ is geate than next Vitual Machines demand. Fo (j+1) th Vitual Machine, we have only ф j+1,k(δd)p i+1,d d: d <= δ Case II : When δ is less than next Vitual Machines demand. Fo (j+1) th Vitual Machine, we have [2C j+1,0+ф j+1,k(δ+a cd)p j+1,d] Again 2C j+1,0 is the cost of taveling to the job execute and back to oute C j,0 is the distance cost fom j th custome back to Job Execute. C 0,j+1 is the distance cost fom job execute i.e. 0 to j+1 th Vitual Machine. 4. PROPOSED ALGORITHM Based upon the above discussion, the poposed algoithms ae given below: Pocedue Use_Request_Submission (jobs) Boolean flag flag SRBAC(jobs) if (flag = 0) then else wite( Request can not be caied out) wite( Request is accepted ) function Boolean SRBAC (jobs) Boolean flag. flag VMMonito(Request fo checking of If (flag =0) Resouce Avablity) 21
4 Intenational Jounal of Compute Applications ( ) Volume 9 No.11, Novembe 2010 Retun(false) Else do submit Expected_Cost (jobs) Retun(tue) end Function Boolean VMMonito (Request_fo Checking_of Resouce_Avablity) Boolean flag Bee stoes in hive. Foge Bees fom Hive stats to seach fo necta i.e. Availability of VMs. Foge Bees etun back and stat dancing on the dancing floo. If (dance be Waggle Dance) then Retun( false.) else do Call Seve_Request_Monito() Retun(Tue) Pocedue Seve_Request_Monito() Reschedule the Pending jobs Pocedue Expected_cost (job) Cost Submit_jobs_to_Dispatche (jobs) Wite(Expected Cost fo Tavesing is equal to Cost) Function Submit_jobs_to_Dispache(jobs) [Initialization] Set δ A c Set j ξ l // ξ l is last VM accessed by l th Ant Set T1 0 Set T2 0 Set k 1 N Max_No_of_Vitual_Machine Repeat until k N Repeat until δ 0 Repeat until j 1 Compute ф jk ( δ) using 2 T1 T1+ ф jk ( δ) j j  1 Compute ф jk p (δ) using 1 T2 T2 + ф jk p (δ) δ δ1 k k+1 T T1 + T2 Retun (T). 5. EXPERIMENTAL SETUP A pivate cloud has been setup based on Ubuntu s Seve edition, that consists of two Seves Seve A and Seve B. Seve A acts as the cloud, cluste, waehouse and stoage contolle and Seve B acts as node contolle. We configued Machine A on a Coe2duoX6800 pocesso based machine with 2GB DDR 2 RAM and 80 GB Had disk. Machine B is unning on an AMD PhenomeII X4 965 pocesso with 4 GB DDR 3 RAM and 250 GB Had disk. The nodes communicate though a fast local aea netwok. This pape demonstates the esults of local accessing of multiple web sevices unning on the web seves. Expeiments have been caied out on above mentioned cloud envionment. In ode to execute the web sevices on Machine A, we have used the following commands: If [! e ~/.euca/mkey.piv]; then fi mkdi p m 700 ~/.euca touch ~/.euca/mykey.piv chmod 0600 ~/.euca/mykey.piv eucaaddkeypai mykey > ~/.euca > ~/.euca/mykey Thus ceating keypai, we now log into ou instance as oot. Afte that, we can monito state of instance using the command: watch n5 eucadescibeinstances Thus we stat to access diffeent web sevices. 6. RESULTS We have expeimented with two diffeent web sevices. Howeve, in ode to demonstate the effectiveness of ou system, we submitted multiple images of the same web sevices as sepaate jobs. The jobs have been initiated at diffeent times. The analysis of stochastic demand vesus objective function, based upon ou expeiments, is shown in figue 1. 22
5 Intenational Jounal of Compute Applications ( ) Volume 9 No.11, Novembe 2010 The figue1 clealy implies that as stochastic demand gadually inceases ou poposed objective function slowly inceases and povides moe optimized value when the stochastic demand is within a modeate to maximum ange. At maximum stochastic demand, we do not find the best pefomance exhibited by ou poposed objective function. This fact has been pointed by the colo code. The eddish pat of the colo code implies that the objective function has attained its optimized value and futhe incease of stochastic demand stats its etadation. This fact is tue fo all categoies of jobs including vey small amount of jobs, modeate jobs o fo huge jobs. This fact is shown by diffeent colo cuve in figue1.this suf deteioations of the objective function is due to moe back and foth movements of the ants in ode to assign the jobs to the available VMs, povided that the stochastic demands of VMs ae vey high. Again we obseve that initially when stochastic demand is vey less then also objective function attains its optimized value gadually due to othe paametes like escheduling of the pending jobs by Sevice Request Monito (which takes place befoe Dispatche that dispatches the new jobs to the available VMs.). In this expeiment we have consideed thee values of the stochastic demands, 510 value of stochastic demand implies low demand whee as values implies high demand and value 15 implies exteme stochastic demand. 8. CONCLUSIONS AND FUTURE DIRECTION OF WORK In this endeavo we have emphasized on the ole of Dispatche in ode to allocate jobs to available VMs. We have seen that as stochastic demand of VMs is within a specific ange, the pefomance of the Dispatche is the best and the objective function is optimized. But futhe incease in stochastic demand leads the ants to move back and foth moe than eve in ode to assign the jobs to Dispatche and hence a negative impact is obseved in the pefomance of the objective function. Futhe the oles of Vitual Machine Monito and Sevice Request Monito have not discussed in this pape, we intend to do so in ou fothcoming endeavo. 9. REFERENCES [1] L.Keleinock. A vision fo the Intenet. ST Jounal of Reseach, Nov.2005, pages 45 [2] S.Adeozzi, P. Ciancaini, D. Montesi, R. Moetti, Towads a model fo quality of web and gid sevice In Poc 13th IEEE intenational Wokshops on Enabling Technologies: Infastuctue fo Colloboative Entepises(WET ICE 04), 2004, page ,. [3] D. Gouscos, M. Kalikakis, and P. Geogiadis. An appoach to modeling Web sevice QoS and povision pice, in Poceding 3 d Intenational Confeence on Web Infomation Systems Engineeing Wokshops, 2003,pages [4] J.P Thomas, M.Thomas and G.Ghinea Modeling of Web sevice flow, in Poceeding IEEE Intenational Confeence on ECommece (CEC 03), 2003, pages ,. [5] S.Tu, M.Flanagin Y. Wu, M. Abdelguefi, E. Nomand, V. Mahadevan, Design Stategies to impove pefomance of GISWeb sevices, in Poceding Intenational Confeence on Infomation Technology : Coding and Computing(ITCC04), 2004, pages ,. [6] R.Levy, J Nagaajao, G. Pacifici, M. Speitze, A. Tantawi, and A. Youssef, Pefomance management fo cluste based Web sevices, in Poceding IFIP/IEEE 8 th Intenational Symposium on Integated Netwok Management, 2003, pages ,. [7] Rajkuma Buyya, Chee Shin Yeo and Sikuma Venugopal, MaketOiented Cloud Computing: Vision, Hype, and Reality fo Deliveing IT Sevices as Computing Utilities, The 10 th IEEE Intenational Confeence on High Pefomance Computing and Communications, IEEE Compute Society, 2008, pages [8] Foste I., C. Kesselman, J. Nick, and S. Tuecke, Gid Sevices fo Distibuted System Integation, IEEE Compute, June 2002, pages [9] Xinhui Li, Ying Li, Tiancheng Liu, Jie Qui, Fengchun Wang Intenational Confeence on Cloud Computing IEEE Compute, 2009, pages , [10] Fulinge K., M. Gendt, A Lightweight Dynamic Application Monito fo SMP Clustes, HLRB and KONWIHR joint Reviewing Wokshop 2004, Mach [11] Fumento N., A. Maye, S. McGough, S. Newhouse, T. field, and J. Dalington, ICENI: Optimization of Component Applications within a Gid envionment, Paallel Computing, 2002, pages ,. [12] H.FWedde and M.Faooq, A compehensive eview of natue inspied outing algoithm fo fixed telecommunication netwoks, Jounal of System Achitectue, 2006, vol. 52, no. 89, pages ,. [13] C. Blum and D. Mekle, Eds., Swam Intelligence: Intoduction and Applications, se. Natual Computing Seies. Spinge, 2008, pages
6 Intenational Jounal of Compute Applications ( ) Volume 9 No.11, Novembe 2010 [14] A. Abaham, C. Gosan, and V. Ramos, Stigmegic Optimization Studies in Computational Intelligence. Secaucus, NJ, USA: SpingeVelag New Yok., 2006, pages [15] V. Cadellini, E. Casalicchio, and M. Colajanni, A pefomance study of distibuted achitectues fo the quality of Web sevices, in Poceding 34 th Annual Hawaii Intenational Confeence on System Sciences,2001 [16] L.Vaqueo, L.RodeoMaino,J.Cacees, M.Linde, A beak in the clouds: towads a cloud definition, SIGC OMM Compute Communication Review,2009, pages [17] T.D. Seely, The Wisdom of the Hive, Havad Univesity Pess, 1995, pages Maco Doigo and Thomas Stutzle Ant Colony Optimization, PenticeHall of India, India, 2001 [18] Maco Doigo and Thomas Stutzle Ant Colony Optimization, PenticeHall of India, India,
HEALTHCARE INTEGRATION BASED ON CLOUD COMPUTING
U.P.B. Sci. Bull., Seies C, Vol. 77, Iss. 2, 2015 ISSN 22863540 HEALTHCARE INTEGRATION BASED ON CLOUD COMPUTING Roxana MARCU 1, Dan POPESCU 2, Iulian DANILĂ 3 A high numbe of infomation systems ae available
More informationAn Approach to Optimized Resource Allocation for Cloud Simulation Platform
An Appoach to Optimized Resouce Allocation fo Cloud Simulation Platfom Haitao Yuan 1, Jing Bi 2, Bo Hu Li 1,3, Xudong Chai 3 1 School of Automation Science and Electical Engineeing, Beihang Univesity,
More informationSoftware Engineering and Development
I T H E A 67 Softwae Engineeing and Development SOFTWARE DEVELOPMENT PROCESS DYNAMICS MODELING AS STATE MACHINE Leonid Lyubchyk, Vasyl Soloshchuk Abstact: Softwae development pocess modeling is gaining
More informationEvaluating the impact of Blade Server and Virtualization Software Technologies on the RIT Datacenter
Evaluating the impact of and Vitualization Softwae Technologies on the RIT Datacente Chistophe M Butle Vitual Infastuctue Administato Rocheste Institute of Technology s Datacente Contact: chis.butle@it.edu
More informationAn Epidemic Model of Mobile Phone Virus
An Epidemic Model of Mobile Phone Vius Hui Zheng, Dong Li, Zhuo Gao 3 Netwok Reseach Cente, Tsinghua Univesity, P. R. China zh@tsinghua.edu.cn School of Compute Science and Technology, Huazhong Univesity
More informationCloud Service Reliability: Modeling and Analysis
Cloud Sevice eliability: Modeling and Analysis YuanShun Dai * a c, Bo Yang b, Jack Dongaa a, Gewei Zhang c a Innovative Computing Laboatoy, Depatment of Electical Engineeing & Compute Science, Univesity
More informationQuestions & Answers Chapter 10 Software Reliability Prediction, Allocation and Demonstration Testing
M13914 Questions & Answes Chapte 10 Softwae Reliability Pediction, Allocation and Demonstation Testing 1. Homewok: How to deive the fomula of failue ate estimate. λ = χ α,+ t When the failue times follow
More information883 Brochure A5 GENE ss vernis.indd 12
ess x a eu / u e a. p o.eu c e / :/ http EURAXESS Reseaches in Motion is the gateway to attactive eseach caees in Euope and to a pool of woldclass eseach talent. By suppoting the mobility of eseaches,
More informationThe Role of Gravity in Orbital Motion
! The Role of Gavity in Obital Motion Pat of: Inquiy Science with Datmouth Developed by: Chistophe Caoll, Depatment of Physics & Astonomy, Datmouth College Adapted fom: How Gavity Affects Obits (Ohio State
More informationThe transport performance evaluation system building of logistics enterprises
Jounal of Industial Engineeing and Management JIEM, 213 6(4): 194114 Online ISSN: 213953 Pint ISSN: 2138423 http://dx.doi.og/1.3926/jiem.784 The tanspot pefomance evaluation system building of logistics
More information9:6.4 Sample Questions/Requests for Managing Underwriter Candidates
9:6.4 INITIAL PUBLIC OFFERINGS 9:6.4 Sample Questions/Requests fo Managing Undewite Candidates Recent IPO Expeience Please povide a list of all completed o withdawn IPOs in which you fim has paticipated
More informationChapter 3 Savings, Present Value and Ricardian Equivalence
Chapte 3 Savings, Pesent Value and Ricadian Equivalence Chapte Oveview In the pevious chapte we studied the decision of households to supply hous to the labo maket. This decision was a static decision,
More informationAN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION (TECHNICAL REPORTTR06, OCTOBER, 2005) Dervis KARABOGA
AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION (TECHNICAL REPORTTR06, OCTOBER, 2005) Devis KARABOGA kaaboga@eciyes.edu.t Eciyes Univesity, Engineeing Faculty Compute Engineeing Depatment
More informationEffect of Contention Window on the Performance of IEEE 802.11 WLANs
Effect of Contention Window on the Pefomance of IEEE 82.11 WLANs Yunli Chen and Dhama P. Agawal Cente fo Distibuted and Mobile Computing, Depatment of ECECS Univesity of Cincinnati, OH 452213 {ychen,
More informationConcept and Experiences on using a Wikibased System for Softwarerelated Seminar Papers
Concept and Expeiences on using a Wikibased System fo Softwaeelated Semina Papes Dominik Fanke and Stefan Kowalewski RWTH Aachen Univesity, 52074 Aachen, Gemany, {fanke, kowalewski}@embedded.wthaachen.de,
More informationENABLING INFORMATION GATHERING PATTERNS FOR EMERGENCY RESPONSE WITH THE OPENKNOWLEDGE SYSTEM
Computing and Infomatics, Vol. 29, 2010, 537 555 ENABLING INFORMATION GATHERING PATTERNS FOR EMERGENCY RESPONSE WITH THE OPENKNOWLEDGE SYSTEM Gaia Tecaichi, Veonica Rizzi, Mauizio Machese Depatment of
More informationQoSConstrained Resource Allocation for a GridBased Multiple Source Electrocardiogram Application
QoSConstained Resouce Allocation fo a GidBased Multiple Souce Electocadiogam Application Dong Su Nam 1,5, ChanHyun Youn 1,3, Bong Hwan Lee 2, Gai Cliffod 3, and Jennife Healey 4 1 School of Engineeing,
More informationON THE (Q, R) POLICY IN PRODUCTIONINVENTORY SYSTEMS
ON THE R POLICY IN PRODUCTIONINVENTORY SYSTEMS Saifallah Benjaafa and JoonSeok Kim Depatment of Mechanical Engineeing Univesity of Minnesota Minneapolis MN 55455 Abstact We conside a poductioninventoy
More informationSelfAdaptive and ResourceEfficient SLA Enactment for Cloud Computing Infrastructures
2012 IEEE Fifth Intenational Confeence on Cloud Computing SelfAdaptive and ResouceEfficient SLA Enactment fo Cloud Computing Infastuctues Michael Maue, Ivona Bandic Distibuted Systems Goup Vienna Univesity
More informationAnalyzing Ballistic Missile Defense System Effectiveness Based on Functional Dependency Network Analysis
Send Odes fo Repints to epints@benthamscience.ae 678 The Open Cybenetics & Systemics Jounal, 2015, 9, 678682 Open Access Analyzing Ballistic Missile Defense System Effectiveness Based on Functional Dependency
More informationAN IMPLEMENTATION OF BINARY AND FLOATING POINT CHROMOSOME REPRESENTATION IN GENETIC ALGORITHM
AN IMPLEMENTATION OF BINARY AND FLOATING POINT CHROMOSOME REPRESENTATION IN GENETIC ALGORITHM Main Golub Faculty of Electical Engineeing and Computing, Univesity of Zageb Depatment of Electonics, Micoelectonics,
More informationAn Efficient Group Key Agreement Protocol for Ad hoc Networks
An Efficient Goup Key Ageement Potocol fo Ad hoc Netwoks Daniel Augot, Raghav haska, Valéie Issany and Daniele Sacchetti INRIA Rocquencout 78153 Le Chesnay Fance {Daniel.Augot, Raghav.haska, Valéie.Issany,
More informationHubs, Bridges, and Switches
Hubs, Bidges, and Switches Used fo extending LANs in tems of geogaphical coveage, numbe of nodes, administation capabilities, etc. Diffe in egads to: m collision domain isolation m laye at which they opeate
More informationEnergy Efficient Cache Invalidation in a Mobile Environment
Enegy Efficient Cache Invalidation in a Mobile Envionment Naottam Chand, Ramesh Chanda Joshi, Manoj Misa Electonics & Compute Engineeing Depatment Indian Institute of Technology, Rookee  247 667. INDIA
More informationAutomatic Testing of Neighbor Discovery Protocol Based on FSM and TTCN*
Automatic Testing of Neighbo Discovey Potocol Based on FSM and TTCN* Zhiliang Wang, Xia Yin, Haibin Wang, and Jianping Wu Depatment of Compute Science, Tsinghua Univesity Beijing, P. R. China, 100084 Email:
More informationIlona V. Tregub, ScD., Professor
Investment Potfolio Fomation fo the Pension Fund of Russia Ilona V. egub, ScD., Pofesso Mathematical Modeling of Economic Pocesses Depatment he Financial Univesity unde the Govenment of the Russian Fedeation
More informationTracking/Fusion and Deghosting with Doppler Frequency from Two Passive Acoustic Sensors
Tacking/Fusion and Deghosting with Dopple Fequency fom Two Passive Acoustic Sensos Rong Yang, Gee Wah Ng DSO National Laboatoies 2 Science Pak Dive Singapoe 11823 Emails: yong@dso.og.sg, ngeewah@dso.og.sg
More informationReview Graph based Online Store Review Spammer Detection
Review Gaph based Online Stoe Review Spamme Detection Guan Wang, Sihong Xie, Bing Liu, Philip S. Yu Univesity of Illinois at Chicago Chicago, USA gwang26@uic.edu sxie6@uic.edu liub@uic.edu psyu@uic.edu
More informationON NEW CHALLENGES FOR CFD SIMULATION IN FILTRATION
ON NEW CHALLENGES FOR CFD SIMULATION IN FILTRATION Michael Dedeing, Wolfgang Stausbeg, IBS Filtan, Industiestasse 19 D51597 MosbachLichtenbeg, Gemany. Oleg Iliev(*), Zaha Lakdawala, Faunhofe Institut
More informationHow to recover your Exchange 2003/2007 mailboxes and emails if all you have available are your PRIV1.EDB and PRIV1.STM Information Store database
AnswesThatWok TM Recoveing Emails and Mailboxes fom a PRIV1.EDB Exchange 2003 IS database How to ecove you Exchange 2003/2007 mailboxes and emails if all you have available ae you PRIV1.EDB and PRIV1.STM
More informationScheduling Hadoop Jobs to Meet Deadlines
Scheduling Hadoop Jobs to Meet Deadlines Kamal Kc, Kemafo Anyanwu Depatment of Compute Science Noth Caolina State Univesity {kkc,kogan}@ncsu.edu Abstact Use constaints such as deadlines ae impotant equiements
More informationConverting knowledge Into Practice
Conveting knowledge Into Pactice Boke Nightmae srs Tend Ride By Vladimi Ribakov Ceato of Pips Caie 20 of June 2010 2 0 1 0 C o p y i g h t s V l a d i m i R i b a k o v 1 Disclaime and Risk Wanings Tading
More informationIBM Research Smarter Transportation Analytics
IBM Reseach Smate Tanspotation Analytics Laua Wynte PhD, Senio Reseach Scientist, IBM Watson Reseach Cente lwynte@us.ibm.com INSTRUMENTED We now have the ability to measue, sense and see the exact condition
More informationA framework for the selection of enterprise resource planning (ERP) system based on fuzzy decision making methods
A famewok fo the selection of entepise esouce planning (ERP) system based on fuzzy decision making methods Omid Golshan Tafti M.s student in Industial Management, Univesity of Yazd Omidgolshan87@yahoo.com
More informationTowards Realizing a Low Cost and Highly Available Datacenter Power Infrastructure
Towads Realizing a Low Cost and Highly Available Datacente Powe Infastuctue Siam Govindan, Di Wang, Lydia Chen, Anand Sivasubamaniam, and Bhuvan Ugaonka The Pennsylvania State Univesity. IBM Reseach Zuich
More informationComparing Availability of Various Rack Power Redundancy Configurations
Compaing Availability of Vaious Rack Powe Redundancy Configuations White Pape 48 Revision by Victo Avela > Executive summay Tansfe switches and dualpath powe distibution to IT equipment ae used to enhance
More informationPower Monitoring and Control for Electric Home Appliances Based on Power Line Communication
I²MTC 2008 IEEE Intenational Instumentation and Measuement Technology Confeence Victoia, Vancouve Island, Canada, May 12 15, 2008 Powe Monitoing and Contol fo Electic Home Appliances Based on Powe Line
More informationReferral service and customer incentive in online retail supply Chain
Refeal sevice and custome incentive in online etail supply Chain Y. G. Chen 1, W. Y. Zhang, S. Q. Yang 3, Z. J. Wang 4 and S. F. Chen 5 1,,3,4 School of Infomation Zhejiang Univesity of Finance and Economics
More informationAn Infrastructure Cost Evaluation of Single and MultiAccess Networks with Heterogeneous Traffic Density
An Infastuctue Cost Evaluation of Single and MultiAccess Netwoks with Heteogeneous Taffic Density Andes Fuuskä and Magnus Almgen Wieless Access Netwoks Eicsson Reseach Kista, Sweden [andes.fuuska, magnus.almgen]@eicsson.com
More informationComparing Availability of Various Rack Power Redundancy Configurations
Compaing Availability of Vaious Rack Powe Redundancy Configuations By Victo Avela White Pape #48 Executive Summay Tansfe switches and dualpath powe distibution to IT equipment ae used to enhance the availability
More informationA Comparative Analysis of Data Center Network Architectures
A Compaative Analysis of Data Cente Netwok Achitectues Fan Yao, Jingxin Wu, Guu Venkataamani, Suesh Subamaniam Depatment of Electical and Compute Engineeing, The Geoge Washington Univesity, Washington,
More informationMemoryAware Sizing for InMemory Databases
MemoyAwae Sizing fo InMemoy Databases Kasten Molka, Giuliano Casale, Thomas Molka, Laua Mooe Depatment of Computing, Impeial College London, United Kingdom {k.molka3, g.casale}@impeial.ac.uk SAP HANA
More informationEfficient Redundancy Techniques for Latency Reduction in Cloud Systems
Efficient Redundancy Techniques fo Latency Reduction in Cloud Systems 1 Gaui Joshi, Emina Soljanin, and Gegoy Wonell Abstact In cloud computing systems, assigning a task to multiple seves and waiting fo
More informationHow to create RAID 1 mirroring with a hard disk that already has data or an operating system on it
AnswesThatWok TM How to set up a RAID1 mio with a dive which aleady has Windows installed How to ceate RAID 1 mioing with a had disk that aleady has data o an opeating system on it Date Company PC / Seve
More informationDistributed Computing and Big Data: Hadoop and MapReduce
Distibuted Computing and Big Data: Hadoop and Map Bill Keenan, Diecto Tey Heinze, Achitect Thomson Reutes Reseach & Development Agenda R&D Oveview Hadoop and Map Oveview Use Case: Clusteing Legal Documents
More informationModeling and Verifying a Price Model for Congestion Control in Computer Networks Using PROMELA/SPIN
Modeling and Veifying a Pice Model fo Congestion Contol in Compute Netwoks Using PROMELA/SPIN Clement Yuen and Wei Tjioe Depatment of Compute Science Univesity of Toonto 1 King s College Road, Toonto,
More informationOptimal Peer Selection in a FreeMarket PeerResource Economy
Optimal Pee Selection in a FeeMaket PeeResouce Economy Micah Adle, Rakesh Kuma, Keith Ross, Dan Rubenstein, David Tune and David D Yao Dept of Compute Science Univesity of Massachusetts Amhest, MA; Email:
More informationSmarter Transportation: The power of Big Data and Analytics
Smate Tanspotation: The powe of Big Data and Analytics EicMak Huitema, Global Smate Tanspotation Leade IBM 1 Intelligent Tanspot Systems (ITS) fo the futue 2 BECAUSE WE WANT IT FOR THE FUTURE. How? The
More informationElectricity transmission network optimization model of supply and demand the case in Taiwan electricity transmission system
Electicity tansmission netwok optimization model of supply and demand the case in Taiwan electicity tansmission system MiaoSheng Chen a ChienLiang Wang b,c, ShengChuan Wang d,e a Taichung Banch Gaduate
More informationUncertain Version Control in Open Collaborative Editing of TreeStructured Documents
Uncetain Vesion Contol in Open Collaboative Editing of TeeStuctued Documents M. Lamine Ba Institut Mines Télécom; Télécom PaisTech; LTCI Pais, Fance mouhamadou.ba@ telecompaistech.f Talel Abdessalem
More informationest using the formula I = Prt, where I is the interest earned, P is the principal, r is the interest rate, and t is the time in years.
9.2 Inteest Objectives 1. Undestand the simple inteest fomula. 2. Use the compound inteest fomula to find futue value. 3. Solve the compound inteest fomula fo diffeent unknowns, such as the pesent value,
More informationApproximation Algorithms for Data Management in Networks
Appoximation Algoithms fo Data Management in Netwoks Chistof Kick Heinz Nixdof Institute and Depatment of Mathematics & Compute Science adebon Univesity Gemany kueke@upb.de Haald Räcke Heinz Nixdof Institute
More informationSTUDENT RESPONSE TO ANNUITY FORMULA DERIVATION
Page 1 STUDENT RESPONSE TO ANNUITY FORMULA DERIVATION C. Alan Blaylock, Hendeson State Univesity ABSTRACT This pape pesents an intuitive appoach to deiving annuity fomulas fo classoom use and attempts
More informationTHE DISTRIBUTED LOCATION RESOLUTION PROBLEM AND ITS EFFICIENT SOLUTION
IADIS Intenational Confeence Applied Computing 2006 THE DISTRIBUTED LOCATION RESOLUTION PROBLEM AND ITS EFFICIENT SOLUTION Jög Roth Univesity of Hagen 58084 Hagen, Gemany Joeg.Roth@Fenunihagen.de ABSTRACT
More informationDatabase Management Systems
Contents Database Management Systems (COP 5725) D. Makus Schneide Depatment of Compute & Infomation Science & Engineeing (CISE) Database Systems Reseach & Development Cente Couse Syllabus 1 Sping 2012
More informationData Center Demand Response: Avoiding the Coincident Peak via Workload Shifting and Local Generation
(213) 1 28 Data Cente Demand Response: Avoiding the Coincident Peak via Wokload Shifting and Local Geneation Zhenhua Liu 1, Adam Wieman 1, Yuan Chen 2, Benjamin Razon 1, Niangjun Chen 1 1 Califonia Institute
More informationResearch on Risk Assessment of the Transformer Based on Life Cycle Cost
ntenational Jounal of Smat Gid and lean Enegy eseach on isk Assessment of the Tansfome Based on Life ycle ost Hui Zhou a, Guowei Wu a, Weiwei Pan a, Yunhe Hou b, hong Wang b * a Zhejiang Electic Powe opoation,
More informationHigh Availability Replication Strategy for Deduplication Storage System
Zhengda Zhou, Jingli Zhou College of Compute Science and Technology, Huazhong Univesity of Science and Technology, *, zhouzd@smail.hust.edu.cn jlzhou@mail.hust.edu.cn Abstact As the amount of digital data
More informationOptimizing Content Retrieval Delay for LTbased Distributed Cloud Storage Systems
Optimizing Content Retieval Delay fo LTbased Distibuted Cloud Stoage Systems Haifeng Lu, Chuan Heng Foh, Yonggang Wen, and Jianfei Cai School of Compute Engineeing, Nanyang Technological Univesity, Singapoe
More informationAn Analysis of Manufacturer Benefits under Vendor Managed Systems
An Analysis of Manufactue Benefits unde Vendo Managed Systems Seçil Savaşaneil Depatment of Industial Engineeing, Middle East Technical Univesity, 06531, Ankaa, TURKEY secil@ie.metu.edu.t Nesim Ekip 1
More informationChapter 2 Valiant LoadBalancing: Building Networks That Can Support All Traffic Matrices
Chapte 2 Valiant LoadBalancing: Building etwoks That Can Suppot All Taffic Matices Rui ZhangShen Abstact This pape is a bief suvey on how Valiant loadbalancing (VLB) can be used to build netwoks that
More informationChannel selection in ecommerce age: A strategic analysis of coop advertising models
Jounal of Industial Engineeing and Management JIEM, 013 6(1):89103 Online ISSN: 0130953 Pint ISSN: 013843 http://dx.doi.og/10.396/jiem.664 Channel selection in ecommece age: A stategic analysis of
More informationFinancial Derivatives for Computer Network Capacity Markets with QualityofService Guarantees
Financial Deivatives fo Compute Netwok Capacity Makets with QualityofSevice Guaantees Pette Pettesson pp@kth.se Febuay 2003 SICS Technical Repot T2003:03 Keywods Netwoking and Intenet Achitectue. Abstact
More informationAn application of stochastic programming in solving capacity allocation and migration planning problem under uncertainty
An application of stochastic pogamming in solving capacity allocation and migation planning poblem unde uncetainty YinYann Chen * and HsiaoYao Fan Depatment of Industial Management, National Fomosa Univesity,
More informationReduced Pattern Training Based on Task Decomposition Using Pattern Distributor
> PNN05P762 < Reduced Patten Taining Based on Task Decomposition Using Patten Distibuto ShengUei Guan, Chunyu Bao, and TseNgee Neo Abstact Task Decomposition with Patten Distibuto (PD) is a new task
More informationCollege of Engineering Bachelor of Computer Science
2 0 0 7 w w w. c n u a s. e d u College of Engineeing Bachelo of Compute Science This bochue Details the BACHELOR OF COMPUTER SCIENCE PROGRAM available though CNU s College of Engineeing. Fo ou most uptodate
More informationEpisode 401: Newton s law of universal gravitation
Episode 401: Newton s law of univesal gavitation This episode intoduces Newton s law of univesal gavitation fo point masses, and fo spheical masses, and gets students pactising calculations of the foce
More informationTop K Nearest Keyword Search on Large Graphs
Top K Neaest Keywod Seach on Lage Gaphs Miao Qiao, Lu Qin, Hong Cheng, Jeffey Xu Yu, Wentao Tian The Chinese Univesity of Hong Kong, Hong Kong, China {mqiao,lqin,hcheng,yu,wttian}@se.cuhk.edu.hk ABSTRACT
More informationFarming: It s a Fact! Career & Technical Education, Introduction
Faming: It s a Fact! Caee & Technical Education, Intoduction Whee Does You Food Dolla Go? Mateials Compute Lab o Compute & Pojecto fo Pesentation Compute Speakes o Headphones Compute Intenet Access o Agicultual
More informationThe Impacts of Congestion on Commercial Vehicle Tours
Figliozzi 1 The Impacts of Congestion on Commecial Vehicle Tous Miguel Andes Figliozzi Potland State Univesity Maseeh College of Engineeing and Compute Science figliozzi@pdx.edu 5124 wods + 7 Tables +
More informationFixed Income Attribution: Introduction
18th & 19th Febuay 2015, Cental London Fixed Income Attibution: A compehensive undestanding of Fixed Income Attibution and the challenging data issues aound this topic Delegates attending this twoday
More informationTowards Automatic Update of Access Control Policy
Towads Automatic Update of Access Contol Policy Jinwei Hu, Yan Zhang, and Ruixuan Li Intelligent Systems Laboatoy, School of Computing and Mathematics Univesity of Westen Sydney, Sydney 1797, Austalia
More informationSTOCHASTIC MARKOV MODEL APPROACH FOR EFFICIENT VIRTUAL MACHINES SCHEDULING ON PRIVATE CLOUD
Intenational Jounal on Cloud Comuting: Sevices and Achitectue(IJCCSA,Vol., No.3,Novembe 20 STOCHASTIC MARKOV MODEL APPROACH FOR EFFICIENT VIRTUAL MACHINES SCHEDULING ON PRIVATE CLOUD Hsu Mon Kyi and Thinn
More informationThe impact of migration on the provision. of UK public services (SRG.10.039.4) Final Report. December 2011
The impact of migation on the povision of UK public sevices (SRG.10.039.4) Final Repot Decembe 2011 The obustness The obustness of the analysis of the is analysis the esponsibility is the esponsibility
More informationSome text, some maths and going loopy. This is a fun chapter as we get to start real programming!
Chapte Two Some text, some maths and going loopy In this Chapte you ae going to: Lean how to do some moe with text. Get Python to do some maths fo you. Lean about how loops wok. Lean lots of useful opeatos.
More informationPeertoPeer File Sharing Game using Correlated Equilibrium
PeetoPee File Shaing Game using Coelated Equilibium Beibei Wang, Zhu Han, and K. J. Ray Liu Depatment of Electical and Compute Engineeing and Institute fo Systems Reseach, Univesity of Mayland, College
More informationA formalism of ontology to support a software maintenance knowledgebased system
A fomalism of ontology to suppot a softwae maintenance knowledgebased system Alain Apil 1, JeanMac Deshanais 1, and Reine Dumke 2 1 École de Technologie Supéieue, 1100 NoteDame West, Monteal, Canada
More informationThey aim to select the best services that satisfy the user s. other providers infrastructures and utility services to run
EndtoEnd Qo Mapping and Aggegation fo electing Cloud evices Raed Kaim, Chen Ding, Ali Mii Depatment of Compute cience Ryeson Univesity, Toonto, Canada 2kaim@yeson.ca, cding@scs.yeson.ca, ali.mii@yeson.ca
More informationMATHEMATICAL SIMULATION OF MASS SPECTRUM
MATHEMATICA SIMUATION OF MASS SPECTUM.Beánek, J.Knížek, Z. Pulpán 3, M. Hubálek 4, V. Novák Univesity of South Bohemia, Ceske Budejovice, Chales Univesity, Hadec Kalove, 3 Univesity of Hadec Kalove, Hadec
More informationEvidence for the exponential distribution of income in the USA
Eu. Phys. J. B 2, 585 589 (21) THE EUROPEAN PHYSICAL JOURNAL B c EDP Sciences Società Italiana di Fisica SpingeVelag 21 Evidence fo the exponential distibution of income in the USA A. Dăgulescu and V.M.
More informationSIMULATION OF GAS TURBINES OPERATING IN OFFDESIGN CONDITION
SIMULAION OF GAS URBINES OPERAING IN OFFDESIGN CONDIION Analdo Walte: awalte@fem.unicamp.b Univesity of Campinas Dept. of Enegy DE/FEM/Unicamp P.O. Box 6122  ZIP code 13083970  Bazil Abstact. In many
More informationOffice of Family Assistance. Evaluation Resource Guide for Responsible Fatherhood Programs
Office of Family Assistance Evaluation Resouce Guide fo Responsible Fathehood Pogams Contents Intoduction........................................................ 4 Backgound..........................................................
More informationFinancing Terms in the EOQ Model
Financing Tems in the EOQ Model Habone W. Stuat, J. Columbia Business School New Yok, NY 1007 hws7@columbia.edu August 6, 004 1 Intoduction This note discusses two tems that ae often omitted fom the standad
More informationExam #1 Review Answers
xam #1 Review Answes 1. Given the following pobability distibution, calculate the expected etun, vaiance and standad deviation fo Secuity J. State Pob (R) 1 0.2 10% 2 0.6 15 3 0.2 20 xpected etun = 0.2*10%
More informationOverencryption: Management of Access Control Evolution on Outsourced Data
Oveencyption: Management of Access Contol Evolution on Outsouced Data Sabina De Capitani di Vimecati DTI  Univesità di Milano 26013 Cema  Italy decapita@dti.unimi.it Stefano Paaboschi DIIMM  Univesità
More informationThe Predictive Power of Dividend Yields for Stock Returns: Risk Pricing or Mispricing?
The Pedictive Powe of Dividend Yields fo Stock Retuns: Risk Picing o Mispicing? Glenn Boyle Depatment of Economics and Finance Univesity of Cantebuy Yanhui Li Depatment of Economics and Finance Univesity
More informationYARN PROPERTIES MEASUREMENT: AN OPTICAL APPROACH
nd INTERNATIONAL TEXTILE, CLOTHING & ESIGN CONFERENCE Magic Wold of Textiles Octobe 03 d to 06 th 004, UBROVNIK, CROATIA YARN PROPERTIES MEASUREMENT: AN OPTICAL APPROACH Jana VOBOROVA; Ashish GARG; Bohuslav
More informationDo Vibrations Make Sound?
Do Vibations Make Sound? Gade 1: Sound Pobe Aligned with National Standads oveview Students will lean about sound and vibations. This activity will allow students to see and hea how vibations do in fact
More informationLAL Update. Letter From the President. Dear LAL User:
LAL Update ASSOCIATES OF CAPE COD, INCORPORATED OCTOBER 00 VOLUME 0, NO. Lette Fom the Pesident Dea LAL Use: This Update will claify some of the statistics used with tubidimetic and chomogenic LAL tests.
More informationSUPPORT VECTOR MACHINE FOR BANDWIDTH ANALYSIS OF SLOTTED MICROSTRIP ANTENNA
Intenational Jounal of Compute Science, Systems Engineeing and Infomation Technology, 4(), 20, pp. 677 SUPPORT VECTOR MACHIE FOR BADWIDTH AALYSIS OF SLOTTED MICROSTRIP ATEA Venmathi A.R. & Vanitha L.
More informationAlternative Formulas for Rating Prediction Using Collaborative Filtering
Altenative Fomulas fo Rating Pediction Using Collaboative Filteing Ama Saic, Misad Hadziadic, David Wilson College of Computing and Infomatics The Univesity of Noth Caolina at Chalotte, 901 Univesity City
More informationAn Introduction to Omega
An Intoduction to Omega Con Keating and William F. Shadwick These distibutions have the same mean and vaiance. Ae you indiffeent to thei iskewad chaacteistics? The Finance Development Cente 2002 1 Fom
More informationEXPERIENCE OF USING A CFD CODE FOR ESTIMATING THE NOISE GENERATED BY GUSTS ALONG THE SUN ROOF OF A CAR
EXPERIENCE OF USING A CFD CODE FOR ESTIMATING THE NOISE GENERATED BY GUSTS ALONG THE SUN ROOF OF A CAR Liang S. Lai* 1, Geogi S. Djambazov 1, Choi H. Lai 1, Koulis A. Peicleous 1, and Fédéic Magoulès
More informationQuestions for Review. By buying bonds This period you save s, next period you get s(1+r)
MACROECONOMICS 2006 Week 5 Semina Questions Questions fo Review 1. How do consumes save in the twopeiod model? By buying bonds This peiod you save s, next peiod you get s() 2. What is the slope of a consume
More informationChapter 6. GraduallyVaried Flow in Open Channels
Chapte 6 GaduallyVaied Flow in Open Channels 6.. Intoduction A stea nonunifom flow in a pismatic channel with gadual changes in its watesuface elevation is named as gaduallyvaied flow (GVF). The backwate
More informationStrength Analysis and Optimization Design about the key parts of the Robot
Intenational Jounal of Reseach in Engineeing and Science (IJRES) ISSN (Online): 23209364, ISSN (Pint): 23209356 www.ijes.og Volume 3 Issue 3 ǁ Mach 2015 ǁ PP.2529 Stength Analysis and Optimization Design
More informationFirstmark Credit Union Commercial Loan Department
Fistmak Cedit Union Commecial Loan Depatment Thank you fo consideing Fistmak Cedit Union as a tusted souce to meet the needs of you business. Fistmak Cedit Union offes a wide aay of business loans and
More informationBA 351 CORPORATE FINANCE LECTURE 4 TAXES AND THE MARGINAL INVESTOR. John R. Graham Adapted from S. Viswanathan FUQUA SCHOOL OF BUSINESS
BA 351 CORPORATE FINANCE LECTURE 4 TAXES AND THE MARGINAL INVESTOR John R. Gaham Adapted fom S. Viswanathan FUQUA SCHOOL OF BUSINESS DUKE UNIVERSITY 1 In this lectue we conside the effect of govenment
More informationIntroduction to Electric Potential
Univesiti Teknologi MARA Fakulti Sains Gunaan Intoduction to Electic Potential : A Physical Science Activity Name: HP: Lab # 3: The goal of today s activity is fo you to exploe and descibe the electic
More informationThe Binomial Distribution
The Binomial Distibution A. It would be vey tedious if, evey time we had a slightly diffeent poblem, we had to detemine the pobability distibutions fom scatch. Luckily, thee ae enough similaities between
More information