A model of Virtual Resource Scheduling in Cloud Computing and Its


 Ambrose Greene
 1 years ago
 Views:
Transcription
1 A model of Virtual Resource Schedulig i Cloud Computig ad Its Solutio usig EDAs 1 Jiafeg Zhao, 2 Wehua Zeg, 3 Miu Liu, 4 Guagmig Li 1, First Author, 3 Cogitive Sciece Departmet, Xiame Uiversity, Xiame, Fujia, Chia; Fujia Key Laboratory of the Brailike Itelliget Systems(Xiame Uiversity), Xiame, Fujia, Chia, *2,Correspodig Author, 4 Fujia Key Laboratory of the Brailike Itelliget Systems(Xiame Uiversity), Xiame, Fujia, Chia; Software School of Xiame Uiversity, Xiame, Fujia, Chia, Abstract Resource schedulig becomes more complex as the itroductio of virtualizatio techology i cloud computig. This paper proposed a resource schedulig model usig the cocept of resource service ratio as a object fuctio ad employig Estimatio of Distributio Algorithms (EDAs) to solve this model. I schedulig model, the resource was abstracted to odes with attributes, the schedulig result was evaluated by resource service ratio istead of the tasks completio time. I EDAs, two ovel factors were itroduced, oe was the iteratio times of best idividual uchaged for reducig the iteratio time, ad aother was share probability for improvig the fitess. I experimet, whe the tasks umber is betwee 5 ad 55 ad the load rate is betwee 0.5 ad 1.5, comparig with Maxmi algorithm, static algorithm ad radom algorithm, the resource service ratio of EDA algorithm is improved o average by at least ad at most times. Keywords: Resource Schedulig, Cloud Computig, Estimatio Of Distributio Algorithms 1. Itroductio Schedulig problem is a widely existed problem i real world, for example, jobshop schedulig problem [13], schedulig i heterogeeous distributed systems [4], tasks schedulig i grid eviromet [57], vehicle schedulig [8, 9], ad so o. This problem also exists i cloud computig eviromet, which plays a importat role i cloud computig. Cloud computig [10] is a kid of ovel largerscale distributioal computer ceter i recet year. Although the exactly defiitio is ot clear [1114], there were hudreds of products amed cloud computig, such as Amazo EC2, IBM smart cloud, Google App Egie ad so o. Differet with the traditioal computer ceter, virtualizatio is putted i cloud computig. The compute is sold as a service ot a product due to the itroductio of virtualizatio, which allows users to purchase compute odemad. However, the applicatio of virtual techology makes the resource schedulig more complex compared with the traditio distributed computatio. At the same time, the result of resource schedulig is critical for cloud computer i virtue of it decides the complete time of tasks. Therefore, the resource schedulig is a hot topic i cloud computig. How to schedule resource i cloud computig eviromet, differet methods were proposed by scholars. For example, Sotomayor [15] et. al proposed a resource lease maager called Haizea, which ca act as a schedulig back ed for OpeNebula [16]. Itel Corporatio [17] ivestigated the shared resource cotetio problem for virtual machies, modeled the effect o each virtual machies s performace. Kog [18] et. al proposed a efficiet dyamic task schedulig scheme based o fuzzy logic systems for cloud computig, i which the availability ad resposiveess preformace was formulated as a twoobjective optimizatio. Sog [19] et. al proposed a multitiered resource schedulig scheme which automatically provides odemad capacities to the hosted services via resource flowig amog VMs. Li [20] et. al propose a hybrid eergyefficiet schedulig algorithm usig dyamic migratio. All above the schudulig algorithms are the sigle task schedulig algorithm. The advatage of sigle task schedulig algorithm is that it s easy to implemet. The disavatage is that it ca t implemet the global ifromatio. This paper modeled the virtual resource schedulig o accout of Iteratioal Joural of Digital Cotet Techology ad its Applicatios(JDCTA) Volume6,Number4,March 2012 doi: /jdcta.vol6.issue
2 the detailed aalysis for the schedulig process i cloud computig ad the requirmet i reality. A ovel cocept amed resource service rate was proposed ad a optimazatio model was established based o this cocept. The Estimatio of distributio algorithms (EDAs) was used to solve this problem, at the same time, two factors were itroduced i EDAs for reducig the iteratio times ad improvig the solutio fitess. Comparig with the radom algorithm, Static algorithm ad Maxmi algorithm, EDAs ca solve this model ad better resource service ratio was obtaied. 2. Cloud computig ad resource schedulig Cloud computig is a popular ou i recetly year. It was arguably first popularized i 2006 by Amazo s Elastic Compute Cloud (EC2) [15] which is represetative of Cloud computig. However, there is still o a widely accepted cocept about cloud computig. After researchig more tha 20 defiitios, Vaquero [10] et. al provided a defiitio that clouds are a large pool of easily usable ad accessible virtualized resource (such as hardware, developmet platforms ad/or services). Cloud computig becomes the elite amog larger scale distributed computig methods thaks to its lowpriced ad usability brought by virtual techology i recet years. Task Task Task Device Device Device a.traditioal distributed eviromet Task Task Task VM VM VM VM VM VM Device Device Device b. Cloud computig eviromet Figure 1. A illustratio of resource schedulig The itroductio of virtualizatio techology brigs huge beefits for cloud computig, whereas the challege of resource schedulig is greater. Differet with the traditioal distributed system, a virtual resource layer is added as show i figure 1. The traditioal distributed schedulig decisio system is illustrated i figure 1.a. At first, a task is received by scheduler, the schedules it to a physical device. It s usually oeooe schedulig, amely whe a task is assiged to a physical device, if this device is buy, the the task waits util other users release this device. The schedulig i cloud computig is explaied i figure 1.b. Whe the user s requiremets are received by system, the decisio system assigs immediately a lot of virtual resource to user, which oe task is assiged usually more tha oe virtual resource. The the scheduler schedules the virtual resource to physical device, which oe physical device is shared by more tha oe virtual resource. Therefore, the schedulig decisio is more complex as the itroductio virtual techology i cloud computig. A mathematic defiitio for resource schedulig i cloud computig is show as followig. Defiitio 1. The resource schedulig i cloud computig is that fid a virtual resource set V = [v i ], i = 1, 2,, m ad a best mappig L = {l 1, l 2,, l m for V to the physical resource set P = [p j ], j = 1, 2,, i a certai time after the tasks set are received. From the defiitio we kow that the key of resource schedulig is to fid a virtual resource set V ad a mappig L. However, this problem is a hard problem. 103
3 3. The resource schedulig model ad its solutio 3.1. The resource schedulig model At first, this paper aalyses the process of schedulig i cloud computig, the model for the virtual resource schedulig i cloud computig. Figure 2 describes the process of resource schedulig i cloud computig. At begi, the users access the cloud computig ceter by explorer or others remote termial, put the tasks to cloud computig ceter. Whe the cloud computig ceter receives the tasks, the virtual resource will be assiged immediately by cloud system. The the virtual resource is scheduled to the physical resource by scheduler. Iteret Figure 2. The process of tasks requiremet i cloud computig I practice, the completio time of task is foremost cocer of users whe a task is put to cloud computig. So, may scholars [18, 19] proposed usig completio time as the evaluatio idex to evaluate the schedulig result. However, the task completio time is related with the resource assiged to it, therefore, if employ the completio time as the evaluatio idex, the relatioship of give resource ad tasks completio time must be established. Whereas, it s a hard work to establish this relatioship thiks to differet kid of tasks has the differet ruig time whe the same resource is obtaied by the differet kid of tasks. Various tasks have to be tested for established this relatioship, it s a hard work ad the result is affected by the elemet of artificiality. From the aalysis we kow that there is a positive correlatio betwee the tasks completio time ad assiged resource, amely, if a task obtai more resource, the the completio time is shorter. Ispired by this opiio, we proposed a cocept called resource service ratio. Defie 2. Resource Service Ratio is the ratio of the assiged resource ad required resource for a give task i cloud computig system. Usig resource service ratio to evaluate the schedulig result, the complexity of schedulig model will be simplified ad the iterferece of experiece fuctio will be reduced. From the cocept, we kow that oly the required resource ad the assiged resource are ecessary. It s easy to obtai those two values i cloud system. I the cloud computig system, the required resource of tasks, virtual resource ad physical resource ca be abstracted some odes with attributes. I the cloud computig system, CPU, RAM ad badwidth are the commo three attributes, this paper uses those three attributes to build model. Set tasks set T = {t i, i = 1, 2,, m, where t i = (c ti, m ti, b ti ), c ti, m ti, b ti is the value of CPU, RAM ad badwidth of task i; A virtual resource set is V = [v i ], i = 1, 2,, m, where, v i = (c vi, m vi, b vi ), c vi, m vi, b vi is the value of CPU, RAM ad badwidth of virtual resource i; Similarly, the physical resource set is P = {p j, j = 1, 2,,, where p j = (c pj, m pj, b pj ), c pj, m pj, b pj is the remat value of CPU, RAM ad badwidth of a physical device ode. A typical example of cloud computig is show as table 1. The formal descriptio of resource service ratio is as followig. 104
4 Table 1. The abstract task ad resource Task set T Virtual resource set V Physical resource set P (1.0, 200, 20) 1.00, 200, 20.0 (3.0, 1000, 80) (0.8, 500, 40) 0.80, 370, 35.1 (20, 700, 20) (2.0,80, 0.5) 0.57, 80, 0.5 (1.0, 400, 20) (0.3, 20, 0.2) 0.30, 20, 0.2 (1.0, 400, 10) (0.4, 50, 0.2) 0.40, 50, 0.2 (0.7, 700, 50) 0.70, 518, 43.7 (1.5, 200, 2) 0.43, 200, 2.0 (1.2, 150, 1) 1.20, 111,0.9 (1.3, 600, 10) 1.30, 600, 10.0 Set S ci is the cpu resource service ratio of task t i, therefore: S c /c (1) Set S c is the total cpu resource service ratio, the: ci vi ci m S s / m (2) c ci Similarly, the RAM resource service ratio S m ad the badwidth resource ratio S b are: m S s / m (3) m mi m S s / m (4) b bi Therefore, the resource service ratio is: S S c S m S b / 3 (5) Set cj is the reality assiged cpu resource o physical device j, the: cj c r (6) vi 1 vi j where, r. The mea of r is that if the task i was assiged to physical device j, the 0 others sum the c vi to cj. Similarly, the reality assiged RAM resource is: mj mvi r (7) the reality assiged badwidth resource is: bj r (8) bbi Therefore, the optimizatio model is: Obtai the best solutio of S ad meet the costraits: cj c j, mj m j, bj b j (9) 3.2. The desig of EDAs EDAs is a ovel evolutioal algorithm i recet years [21]. The mai idea of evolutioal algorithm 105
5 is that fid out the object fuctio extreme value by iteratig people. The object fuctio is formula 5 i this paper ad the extreme value of S is the maximum value of S. People cosist of idividuals, a idividual correspod a solutio of object fuctio. Iteratio is geeratig ext people usig curret people accordig some rules. Differet evolutioal algorithms have differet rules. A whole EDAs evolutioal algorithm flow chart is show as figure 3. At first, geerate the people radomly, the calculate the fitess of idividuals, at last, geerate the ext geeratio based o the probability model, amely sample every code of every idividuals deped o this probability model. Therefore, that there are two key poits i EDAs algorithm: 1) the idividual code; 2) the probability model. We will itroduce these two poits separately The idividual code From the defiite 1 we kow that: the resource schedulig i cloud computig is to fid V ad L. For a give V ad L, firstly it s ecessary to judge whether the V meet the costraits i optimizatio model, the obtai the resource service ratio S by solutio accordig with formula 5. Solvig Costraied optimizatio problems (COPs) has become a importat research area of evolutioary computatio i recet years [22]. This paper uses the followig rule to give the correspodig V: Rule 1. For a give L, the assigmet method of cpu resource is: if cpj cti r, the c vi = c ti ; if pj c c r, the ti cti cvi mi{ cti, cpj, at this time if c pj cvi r, the sort the c r ti tasks accordig to the size of tasks, assig the remaider resource to the smaller tasks. Similarly, the memory resource ad badwidth resource allocatio ca use the same rule to allocatio. Begi Radomly Geerate P idividuals Meet Ls or L? Calculates idividuals fitess Costruct probability model based o statistic method Geerate ext geeratio based o probability model Figure 3. The flow chart of EDA algorithm Usig this rule to assig resource, although the value of S will be affected, the complex degree of algorithm is reduced. The costraits are assured by the rule with less calculatio. O the situatio of usig resource assigmet rule, the schedulig of virtual resource is chaged to fid a mappig L. Therefore, for a give cloud computig system, we idetify each physical ode, there is a ID correspodig with each physical device. Set the ID set is D, the for a task set T, there is a code L = { l i l i D correspodig with it. For example, set T = m, m = 8, the L = {1,3,1,5,9,3,5,7 Ed 106
6 is a mappig of T, which represets that the first task is assiged to physical ode 1, the secod task is assiged to physical ode 3, ad so o The probability model The mai idea of this model i this paper is from Uivariate Margial Distributio Algorithm (UMDA) [21], amely select some best idividuals, the usig those idividuals to costruct probability model. Set P is the umber of idividuals i people, Sp is the selectio possibility, amely how much idividuals should be selected from the people. Therefore the probability model is: S l l l i N S j( Xi xi Dl ) j1 p '( x) p'( x D ) p '( x ) N where, is the umber of object fuctios, as there is oly oe fitess fuctio i this paper, so =1; N P* Sp (11) S 1 X i xi j( Xi xi Dl ) (12) 0 others s D l is the subpeople selected from l geeratio which icludes N idividuals. This paper itroduces a parameter Pm to the EDAs algorithm. The mai purpose of Pm is to avoid the gee with lower possibility beig weeded out too early. From the formula 10 we kow that if a gee does t appear i oce iteratio, the the appearace possibility of this gee i ext geeratio is 0. For avoidig this ureasoable situatio, we itroduce shared factor Pm i EDAs. As show i formula 13, if there is a gee does t appear i oce iteratio, the the appearace possibility of this gee i ext geeratio is Pm/m. pl( x) pm / m(1 pm)* pl '( x) (13) The algorithm of geeratig ext geeratio people is show as followig. Alg. 1 The algorithm of costructig probability model i EDAs cal_poss(selected subpeople D ){ s l for(i<1; i<; i++){ for(j<1; j<m;i++){ possibility_model(i,j)=calculate depedig o formula 10 ad 13; retur possibility_model; Alg. 2 The algorithm of geeratig ext geeratio people i EDAs Eda(People) { Sortig the idividuals accordig with the idividual fitess i people; Select the top Sp*P idividuals to costruct possibility_model = cal_poss( D ); s l D ; for(i=1;i<p;i++){ for(j=1; j<the legth of idividual; i++){ ext_people(i,j) = sample from the probability model; retur ext_people; s l (10) 107
7 The parameters ad their values are show i table 2. Table 2. The parameters, their meaig ad value i EDA Parameters meaig value L The most iteratio time 150 Ls uchaged iteratio umber of best idividual 70 P the umber of people 50 Sp ratio of chose 0.15 Pm miimal possibility shared by all services The experimet ad discussio 4.1. The eviromet of experimet For verifyig the result of algorithms without the iterferece of the oise, this paper uses the simulator to test. How to simulate a cloud computig? I practical, the schedulig result is affected by two poits: oe is the umber of tasks arrived at the same time; aother is ratio of required resource ad physical resource amed load ratio. If the umber of tasks arrived at the same time is large the schedulig will become more complex. Besides if the load ratio is large, it meas there are may tasks ca t be service. I view of those two poits, we give some fixed parameters to represet a existed cloud computig system at first, the we adjust the umber of arrived tasks ad the load ratio to simulate the differet sese of resource schedulig i cloud computig system. I the simulator, the geeratio scopes of physical resource are: CPU (0.5~1.5), RAM (500~1500), badwidth (1~9), the quatity of physical resource is 20. The geeratio of tasks is decided by two poits: oe is the umber of physical resource ad the umber of tasks; aother is the load ratio. So the geeratio method of tasks is show as formula 14. resource radom*( sum( p)* ratio / m)*2 (14) Where, resource is a attribute of the task, such as CPU. radom is a radom. sum(p) is the sum of physical resource correspodig with the attribute of the task, amelysum p = p ( attr). ratio is the ratio of the total required resource ad the total physical resource, m is the umber of tasks. For example, set we wat to radom geerate the CPU attribute of a tasks set. For a give task, set the radom is 0.6, sum(p) is 20.7, which is sum of all physical resource CPU value, the ratio is 0.9, the m is 40, those two parameters value are appoited by experimet, therefore, the CPU value of a task is The desig of other algorithms: 1) Maxmi algorithm Priciple: For a give task, retur the physical resource with the largest resource ratio as the schedulig solutio. The pseudo code is show as followig. Alg. 3 The pseudo code of Maxmi Solutio L Max_mi(t,p){ for (ti t) { for (pi p) { res=ti.cpu/pi.cpu + ti.ram/pi.ram + ti.badwidth/pi.badwidth; Record the largest res as the physical resource assiged to ti; L(i) = ti.serial; retur L; i 108
8 2) Static algorithm Priciple: Assig the task to the physical resource with same ID umber. If the task ID is lager tha the largest physical resource ID, the use the remaider of task ID divided the largest physical resource ID, amely the schedulig solutio is similarly with (1, 2, ). 3) Radom algorithm Priciple: Radomly assig the task to physical resource Experimet result ad discuss Solve a istace Table 3 shows the solutio of the table 1 usig differet algorithms. From the table we kow that the resource service ratio of EDA algorithm is better tha other three algorithms. Table 4 displays the total virtual resource assiged to the tasks set by differet algorithms. From the table 4 we kow that EDA ca maximize the efficiecy of physical resource. Table 3. The solutios of resource the istace i table 1 usig differet algorithms Alg. Solutio s s c s m s b EDA Maxmi Static Radom Table 4. The total virtual resource assiged to tasks set by differet algorithms Attribute EDA Maxmi Static Radom CPU RAM Badwidth Table 5. The cocrete assigmet of each physical device with differet algorithms No. EDA Maxmi Static Radom P set 1 (2.70, , 80.00) (2.50, , 80.00) (2.70, , 30.20) (3.00, , 80.00) (3.0, 1000, 80) 2 (2.00, , 10.40) (2.00, , 2.70) (1.50, , 20.00) (2.00, , 12.20) (2.0, 700, 20) 3 (1.00, , 20.00) (1.00, , 1.20) (1.00, , 2.50) (0.30, 20.00, 0.20) (1.0, 400, 20) 4 (1.00, , 2.50) (1.00, , 10.00) (1.00, , 1.20) (1.00, 80.00, 0.50) (1.0, 400, 10) Table 6. The cocrete assigmet of each task with differet algorithms No. EDA Maxmi Static Radom T set 1 (1.00, , 20.00) (1.00, , 14.55) (1.00, , 20.00) (0.81, , 14.41) (1.0, 200, 20) 2 (0.80, , 35.16) (0.80, , 29.09) (0.80, , 8.89) (0.65, , 28.83) (0.8, 500, 40) 3 (0.57, 80.00, 0.50) (1.05, 80.00, 0.50) (0.57, 80.00, 0.50) (1.00, 80.00, 0.50) (2.0,80, 0.5) 4 (0.30, 20.00, 0.20) (0.16, 20.00, 0.20) (0.20, 20.00, 0.20) (0.30, 20.00, 0.20) (0.3, 20, 0.2) 5 (0.40, 50.00, 0.20) (0.25, 50.00, 0.20) (0.40, 50.00, 0.20) (0.25, 41.18, 0.20) (0.4, 50, 0.2) 6 (0.70, , 43.96) (0.70, , 36.36) (0.70, , 11.11) (0.57, , 36.04) (0.7, 700, 50) 7 (0.43, , 2.00) (0.79, , 2.00) (0.43, , 2.00) (0.94, , 2.00) (1.5, 200, 2) 8 (1.20, , 0.88) (0.75, , 1.00) (0.80, , 1.00) (0.97, 96.77, 0.72) (1.2, 150, 1) 9 (1.30, , 10.00) (1.00, , 10.00) (1.30, , 10.00) (0.81, , 10.00) (1.3, 600, 10) Table 5 shows the cocrete assigmet of physical devices. For physical devices, the max value of assiged resource ca t be over the devices value. If the assiged resource is greater tha devices value, 109
9 the performace of devices will drop dow. From the table 5, all algorithms are t greater tha the devices value. Table 6 shows the cocrete value assiged to each task. Ideally, every task ca obtai the resource they eeds. However, it s hard to reach this goal for the costrait of physical devices. From the table we kow that EDA algorithm is better tha other algorithms due to it ca more fulfill the tasks. Figure 4. The chage of s with the iteratio time i oce solutio of EDA algorithm a. The chage of s c accordig to the umber of tasks b. The chage of s r accordig to the umber of tasks c. The chage of s b accordig to the umber of tasks d. The chage of s accordig to the umber of tasks Figure 5. The chage of object fuctio accordig to the umber of tasks Figure 4 displays oce iteratio process of EDA algorithm. The abscissa axis is iteratio time ad the vertical axis is the value of s, amely the resource service ratio. From the figure we kow that the iteratio time is 20. As the iteratio time icreasig, the average fitess of people icreases rapidly firstly, the icreases slowly with fluctuated. The best idividual fitess always fluctuates as the iteratio time icreasig ad obtais best value at 7 th iteratio time. The reaso is that the problem 110
10 scale is small (9^4), so the iteratio time is small ad the fitess of best idividual is little chaged Algorithm compariso o differet umber of tasks Figure 5 shows the variety of s value as the task set icludig differet umber of tasks. The s value is average value after ruig 25 times. I the experimet, the load ratio is 1.0, the physical resource is geerated radomly ad the task set is geerated depedig o the formula 14. It ca be observed that EDA is better tha other algorithms. Specially, EDA algorithm is obviously better tha other algorithms whe the tasks are small. If the load ratio is fixed, the total required resource is fixed for a give cloud system, therefore, the required resource of sigle task will be larger whe the umber of tasks is small. The schedulig will become more complex ad the advatage of EDA algorithm become more obvious o this situatio. From calculatio it ca be obtaied that the average resource service ratio s of EDA algorithm is 1.060, 1.039, times respectively of Maxmi algorithm, static algorithm ad radom algorithm. a. The chage of s c accordig to the load ratio b. The chage of s r accordig to the load ratio c. The chage of s b accordig to the load ratio d. The chage of s accordig to the load ratio Figure 6. The chage of object fuctio accordig to the load ratio Algorithm compariso o differet load ratio The, we will study the schedulig o differet load ratio. I this situatio, as the tasks umber is ofte more tha devices umber i reality, set m=20, =40. Figure 6 shows the average result of s after ruig 25 times whe the load ratio from 0.5 to 1.5. From the figure we ca see that s of EDA algorithm is 1 whe the load ratio is low, s desceds as the load ratio icreasig. The reaso is there are more tasks ca t be service as the load ratio icreasig. From the figure it also ca be cocluded that EDA algorithm is better tha other algorithms. Accordig the calculatio, the average s of EDA algorithm is better 1.065, 1.004, times tha Maxmi algorithm, static algorithm ad radom algorithm. 111
11 5. Coclusio This paper studies the resource schedulig algorithm i cloud computig, proposes a resource schedulig optimizatio model usig resource service as the object fuctio ad employs EDAs to solve this model. At first, a detail aalysis of resource schedulig o cloud computig eviromet is give, usig resource service replaces completio time to evaluate the schedulig result, models the resource schedulig based o this cocept; the, usig EDA algorithm to solve this problem; at last, a experimet is desiged to verify the feasible of this model. I experimet, a small istatiate is solved to verify the correctess of the model ad lear the solvig process i detail. Besides of those, compares with the Maxmi algorithm, static algorithm ad radom algorithm. The result shows: EDA algorithm is averagely better tha other algorithms at least ad at most whe the umber of tasks from 5 to 55 or the load ratio from 0.5 to Ackowledgmet This work was supported by the Fujia Key Laboratory of the Brailike Itelliget Systems (Xiame Uiversity), P. R. Chia, the Natioal Natural Sciece Foudatio of Chia (Grat No , , ). 7. Referece [1] Rui Zhag, "A geetic local search algorithm based o isertio eighborhood for the job shop schedulig problem," Advaces i Iformatio Scieces ad Service Scieces, vol. 3, o. 5, pp , [2] Sog CuLi, Liu XiaoBig, Wag Wei,Xi Bai, "A hybrid particle swarm optimizatio algorithm for jobshop schedulig problem," Iteratioal Joural of Advacemets i Computig Techology, vol. 3, o. 4, pp , [3] Zhag Rui, "A particle swarm optimizatio algorithm based o local perturbatios for the job shop schedulig problem," Iteratioal Joural of Advacemets i Computig Techology, vol. 3, o. 4, pp , [4] Jig Weipeg, Liu Yaqiu,Wu Qu, "Commuicatioaware faulttolerat schedulig strategy for precedece costraied tasks i heterogeeous distributed systems," Iteratioal Joural of Digital Cotet Techology ad its Applicatios, vol. 5, o. 6, pp , [5] XuYi Re, Zheghua Qi, Ruchua Wag,Xiaodog Ma, "Rough set ad A* based tasks schedulig i gird eviromet," Iteratioal Joural of Digital Cotet Techology ad its Applicatios, vol. 4, o. 4, pp , [6] Dr.K.Vivekaada, D.Ramyachitra, B.Abu, "Artificial bee coloy algorithm for grid schedulig," Joural of Covergece Iformatio Techology, vol. 6, o. 7, pp , [7] Sog Kufag, Rua Shufe,Jiag Mighua, "A flexible grid task schedulig algorithm based o QoS similarity," Joural of Covergece Iformatio Techology, vol. 5, o. 7, pp , [8] Che Jiawei, Zhag YuaBiao,Wag GegJia, "A ew algorithm for a fuzzy vehicle routig ad schedulig problem: Imperialist competitive algorithm," Joural of Covergece Iformatio Techology, vol. 6, o. 7, pp , [9] Wag Gegjia, Zhag YuaBiao,Che JiaWei, "A ovel algorithm to solve the vehicle routig problem with time widows: Imperialist competitive algorithm," Advaces i Iformatio Scieces ad Service Scieces, vol. 3, o. 5, pp , [10]Luis M. Vaquero, Luis RoderoMerio, Jua Caceres, Maik Lider, "A break i the clouds: towards a cloud defiitio," ACM SIGCOMM Computer Commuicatio Review, vol. 39, o. 1, pp , [11]Armbrust Michael, Fox Armado, Griffith Rea, Joseph Athoy D., Katz Rady H., Kowiski Adrew, Lee Guho, Patterso David A., Rabki Ariel, Stoica Io, Zaharia Matei, "Above the clouds: A berkeley view of cloud computig," EECS Departmet, Uiversity of Califoria, Berkeley, Tech. Rep. UCB/EECS , [12] Erik Bryjolfsso, Paul Hofma, Joh Jorda, "Cloud Computig ad Electricity: Beyod the Utility Model," Commuicatios of the Acm, vol. 53, o. 5, pp ,
12 [13] Rajkumar Buyya, Chee Shi Yeo, Srikumar Veugopal, James Broberg, Ivoa Bradic, "Cloud computig ad emergig IT platforms: Visio, hype, ad reality for deliverig computig as the 5th utility," Future Geeratio Computer Systemsthe Iteratioal Joural of Grid ComputigTheory Methods ad Applicatios, vol. 25, o. 6, pp , [14] Michael Armbrust, Armado Fox, Rea Griffith, Athoy D. Joseph, Rady Katz, Ady Kowiski, Guho Lee, David Patterso, Ariel Rabki, Io Stoica, Matei Zaharia, "A View of Cloud Computig," Commuicatios of the Acm, vol. 53, o. 4, pp , [15] Borja Sotomayor,, Rubé S. Motero, Igacio M. Llorete, Ia Foster, "Virtual Ifrastructure Maagemet i Private ad Hybrid Clouds," Ieee Iteret Computig, vol. 13, o. 5, pp , [16] Peter Sempoliski, Douglas Thai, "A Compariso ad Critique of Eucalyptus, OpeNebula ad Nimbus," i Cloud Computig Techology ad Sciece (CloudCom), 2010 IEEE Secod Iteratioal Coferece o, pp , [17] Ravi Iyer, Ramesh Illikkal, Omesh Tickoo, Li Zhao, Padma Apparao, Do Newell, "VM3: Measurig, modelig ad maagig VM shared resources," Computer Networks, vol. 53, o. 17, pp , [18] Xiagzhe Kog, Chuag Li, Yixi Jiag, Wei Ya, Xiaowe Chu, "Efficiet dyamic task schedulig i virtualized data ceters with fuzzy predictio," Joural of Network ad Computer Applicatios, vol. 34, o. 4, pp , [19] Sog Yig, Wag Hui, Li Yaqiog, Feg Biqua,Su Yuzhog, "MultiTiered ODemad Resource Schedulig for VMBased Data Ceter," i Proceedigs of the th IEEE/ACM Iteratioal Symposium o Cluster Computig ad the Grid, pp , [20] Li Jiadu, Peg Jujie, Zhag Wu, "A schedulig algorithm for private clouds," Joural of Covergece Iformatio Techology, vol. 6, o. 7, pp. 19, [21] Zhou Shude, Su Zegqi, "A survey o Estimatio of Distributio Algorithms," ACTA automatic siica, vol. 33, o. 2, pp , [22] Wag Yog, Cai Zixig, Zhou Yure, Xiao Chixi, "Costraied optimizatio evolutioary algorithms," Joural of Software, vol. 20, o. 1, pp ,
Modified Line Search Method for Global Optimization
Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o
More informationVirtual Machine Scheduling Management on Cloud Computing Using Artificial Bee Colony
, March 1214, 2014, Hog Kog Virtual Machie Schedulig Maagemet o Cloud Computig Usig Artificial Bee Coloy B. Kruekaew ad W. Kimpa Abstract Resource schedulig maagemet desig o Cloud computig is a importat
More information(VCP310) 18004186789
Maual VMware Lesso 1: Uderstadig the VMware Product Lie I this lesso, you will first lear what virtualizatio is. Next, you ll explore the products offered by VMware that provide virtualizatio services.
More informationEvaluating Model for B2C E commerce Enterprise Development Based on DEA
, pp.180184 http://dx.doi.org/10.14257/astl.2014.53.39 Evaluatig Model for B2C E commerce Eterprise Developmet Based o DEA Weli Geg, Jig Ta Computer ad iformatio egieerig Istitute, Harbi Uiversity of
More informationApplication and research of fuzzy clustering analysis algorithm under microlecture English teaching mode
SHS Web of Cofereces 25, shscof/20162501018 Applicatio ad research of fuzzy clusterig aalysis algorithm uder microlecture Eglish teachig mode Yig Shi, Wei Dog, Chuyi Lou & Ya Dig Qihuagdao Istitute of
More informationRecovery time guaranteed heuristic routing for improving computation complexity in survivable WDM networks
Computer Commuicatios 30 (2007) 1331 1336 wwwelseviercom/locate/comcom Recovery time guarateed heuristic routig for improvig computatio complexity i survivable WDM etworks Lei Guo * College of Iformatio
More informationReliability Analysis in HPC clusters
Reliability Aalysis i HPC clusters Narasimha Raju, Gottumukkala, Yuda Liu, Chokchai Box Leagsuksu 1, Raja Nassar, Stephe Scott 2 College of Egieerig & Sciece, Louisiaa ech Uiversity Oak Ridge Natioal Lab
More informationResearch Method (I) Knowledge on Sampling (Simple Random Sampling)
Research Method (I) Kowledge o Samplig (Simple Radom Samplig) 1. Itroductio to samplig 1.1 Defiitio of samplig Samplig ca be defied as selectig part of the elemets i a populatio. It results i the fact
More informationResearch Article Sign Data Derivative Recovery
Iteratioal Scholarly Research Network ISRN Applied Mathematics Volume 0, Article ID 63070, 7 pages doi:0.540/0/63070 Research Article Sig Data Derivative Recovery L. M. Housto, G. A. Glass, ad A. D. Dymikov
More informationSystems Design Project: Indoor Location of Wireless Devices
Systems Desig Project: Idoor Locatio of Wireless Devices Prepared By: Bria Murphy Seior Systems Sciece ad Egieerig Washigto Uiversity i St. Louis Phoe: (805) 6985295 Email: bcm1@cec.wustl.edu Supervised
More informationDAME  Microsoft Excel addin for solving multicriteria decision problems with scenarios Radomir Perzina 1, Jaroslav Ramik 2
Itroductio DAME  Microsoft Excel addi for solvig multicriteria decisio problems with scearios Radomir Perzia, Jaroslav Ramik 2 Abstract. The mai goal of every ecoomic aget is to make a good decisio,
More informationA Combined Continuous/Binary Genetic Algorithm for Microstrip Antenna Design
A Combied Cotiuous/Biary Geetic Algorithm for Microstrip Atea Desig Rady L. Haupt The Pesylvaia State Uiversity Applied Research Laboratory P. O. Box 30 State College, PA 168040030 haupt@ieee.org Abstract:
More informationDomain 1: Designing a SQL Server Instance and a Database Solution
Maual SQL Server 2008 Desig, Optimize ad Maitai (70450) 18004186789 Domai 1: Desigig a SQL Server Istace ad a Database Solutio Desigig for CPU, Memory ad Storage Capacity Requiremets Whe desigig a
More informationDetermining the sample size
Determiig the sample size Oe of the most commo questios ay statisticia gets asked is How large a sample size do I eed? Researchers are ofte surprised to fid out that the aswer depeds o a umber of factors
More informationThe analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection
The aalysis of the Courot oligopoly model cosiderig the subjective motive i the strategy selectio Shigehito Furuyama Teruhisa Nakai Departmet of Systems Maagemet Egieerig Faculty of Egieerig Kasai Uiversity
More informationStudy on the application of the software phaselocked loop in tracking and filtering of pulse signal
Advaced Sciece ad Techology Letters, pp.3135 http://dx.doi.org/10.14257/astl.2014.78.06 Study o the applicatio of the software phaselocked loop i trackig ad filterig of pulse sigal Sog Wei Xia 1 (College
More informationOverview. Learning Objectives. Point Estimate. Estimation. Estimating the Value of a Parameter Using Confidence Intervals
Overview Estimatig the Value of a Parameter Usig Cofidece Itervals We apply the results about the sample mea the problem of estimatio Estimatio is the process of usig sample data estimate the value of
More informationSECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES
SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,
More informationHeterogeneous Vehicle Routing Problem with profits Dynamic solving by Clustering Genetic Algorithm
IJCSI Iteratioal Joural of Computer Sciece Issues, Vol. 10, Issue 4, No 1, July 2013 ISSN (Prit): 16940814 ISSN (Olie): 16940784 www.ijcsi.org 247 Heterogeeous Vehicle Routig Problem with profits Dyamic
More informationComparative Analysis of Round Robin VM Load Balancing With Modified Round Robin VM Load Balancing Algorithms in Cloud Computing
Iteratioal Joural of Egieerig, Maagemet & Scieces (IJEMS) Comparative Aalysis of Roud Robi Balacig With Modified Roud Robi Balacig s i Cloud Computig Areeba Samee, D.K Budhwat Abstract Cloud computig is
More informationIn nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008
I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces
More informationInstallment Joint Life Insurance Actuarial Models with the Stochastic Interest Rate
Iteratioal Coferece o Maagemet Sciece ad Maagemet Iovatio (MSMI 4) Istallmet Joit Life Isurace ctuarial Models with the Stochastic Iterest Rate NiaNia JI a,*, Yue LI, DogHui WNG College of Sciece, Harbi
More informationwhere: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return
EVALUATING ALTERNATIVE CAPITAL INVESTMENT PROGRAMS By Ke D. Duft, Extesio Ecoomist I the March 98 issue of this publicatio we reviewed the procedure by which a capital ivestmet project was assessed. The
More informationResearch Article Allocating Freight Empty Cars in Railway Networks with Dynamic Demands
Discrete Dyamics i Nature ad Society, Article ID 349341, 12 pages http://dx.doi.org/10.1155/2014/349341 Research Article Allocatig Freight Empty Cars i Railway Networks with Dyamic Demads Ce Zhao, Lixig
More informationCOMPUSOFT, An international journal of advanced computer technology, 3 (3), March2014 (VolumeIII, IssueIII)
COMPUSOFT, A iteratioal joural of advaced computer techology, 3 (3), March2014 (VolumeIII, IssueIII) ISSN:23200790 Adaptive Workload Maagemet for Efficiet Eergy Utilizatio o Cloud M.Prabakara 1, M.
More informationA Method for Trust Quantificationin Cloud Computing Environments
A Method for rust Quatificatioi Cloud Computig Eviromets Xiaohui Li,3, Jigsha He 2*,Bi Zhao 2, Jig Fag 2, Yixua Zhag 2, Hogxig Liag 4 College of Computer Sciece ad echology, Beiig Uiversity of echology
More informationProject Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments
Project Deliverables CS 361, Lecture 28 Jared Saia Uiversity of New Mexico Each Group should tur i oe group project cosistig of: About 612 pages of text (ca be loger with appedix) 612 figures (please
More informationLearning outcomes. Algorithms and Data Structures. Time Complexity Analysis. Time Complexity Analysis How fast is the algorithm? Prof. Dr.
Algorithms ad Data Structures Algorithm efficiecy Learig outcomes Able to carry out simple asymptotic aalysisof algorithms Prof. Dr. Qi Xi 2 Time Complexity Aalysis How fast is the algorithm? Code the
More informationADAPTIVE NETWORKS SAFETY CONTROL ON FUZZY LOGIC
8 th Iteratioal Coferece o DEVELOPMENT AND APPLICATION SYSTEMS S u c e a v a, R o m a i a, M a y 25 27, 2 6 ADAPTIVE NETWORKS SAFETY CONTROL ON FUZZY LOGIC Vadim MUKHIN 1, Elea PAVLENKO 2 Natioal Techical
More information1 Computing the Standard Deviation of Sample Means
Computig the Stadard Deviatio of Sample Meas Quality cotrol charts are based o sample meas ot o idividual values withi a sample. A sample is a group of items, which are cosidered all together for our aalysis.
More informationConfidence Intervals for One Mean
Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a
More informationData Analysis and Statistical Behaviors of Stock Market Fluctuations
44 JOURNAL OF COMPUTERS, VOL. 3, NO. 0, OCTOBER 2008 Data Aalysis ad Statistical Behaviors of Stock Market Fluctuatios Ju Wag Departmet of Mathematics, Beijig Jiaotog Uiversity, Beijig 00044, Chia Email:
More informationChapter 6: Variance, the law of large numbers and the MonteCarlo method
Chapter 6: Variace, the law of large umbers ad the MoteCarlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value
More informationLesson 17 Pearson s Correlation Coefficient
Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) types of data scatter plots measure of directio measure of stregth Computatio covariatio of X ad Y uique variatio i X ad Y measurig
More informationMeasures of Spread and Boxplots Discrete Math, Section 9.4
Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,
More informationEvaluation of Different Fitness Functions for the Evolutionary Testing of an Autonomous Parking System
Evaluatio of Differet Fitess Fuctios for the Evolutioary Testig of a Autoomous Parkig System Joachim Wegeer 1, Oliver Bühler 2 1 DaimlerChrysler AG, Research ad Techology, AltMoabit 96 a, D1559 Berli,
More informationC.Yaashuwanth Department of Electrical and Electronics Engineering, Anna University Chennai, Chennai 600 025, India..
(IJCSIS) Iteratioal Joural of Computer Sciece ad Iformatio Security, A New Schedulig Algorithms for Real Time Tasks C.Yaashuwath Departmet of Electrical ad Electroics Egieerig, Aa Uiversity Cheai, Cheai
More information76 SYSTEMICS, CYBERNETICS AND INFORMATICS VOLUME 9  NUMBER 1  YEAR 2011 ISSN: 16904524
The Fuzzy ad Compartmet System Cocept for the Commuicatio System takig accout of the Hadicapped situatio M asahiroaruga DepartmetofHuma ad Iformatio Sciece,School ofiformatio Sciecead Techology,TokaiUiversity
More informationDesktop Management. Desktop Management Tools
Desktop Maagemet 9 Desktop Maagemet Tools Mac OS X icludes three desktop maagemet tools that you might fid helpful to work more efficietly ad productively: u Stacks puts expadable folders i the Dock. Clickig
More information*The most important feature of MRP as compared with ordinary inventory control analysis is its time phasing feature.
Itegrated Productio ad Ivetory Cotrol System MRP ad MRP II Framework of Maufacturig System Ivetory cotrol, productio schedulig, capacity plaig ad fiacial ad busiess decisios i a productio system are iterrelated.
More informationA probabilistic proof of a binomial identity
A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two
More informationEnhancing Oracle Business Intelligence with cubus EV How users of Oracle BI on Essbase cubes can benefit from cubus outperform EV Analytics (cubus EV)
Ehacig Oracle Busiess Itelligece with cubus EV How users of Oracle BI o Essbase cubes ca beefit from cubus outperform EV Aalytics (cubus EV) CONTENT 01 cubus EV as a ehacemet to Oracle BI o Essbase 02
More informationAnalyzing Longitudinal Data from Complex Surveys Using SUDAAN
Aalyzig Logitudial Data from Complex Surveys Usig SUDAAN Darryl Creel Statistics ad Epidemiology, RTI Iteratioal, 312 Trotter Farm Drive, Rockville, MD, 20850 Abstract SUDAAN: Software for the Statistical
More informationSubject CT5 Contingencies Core Technical Syllabus
Subject CT5 Cotigecies Core Techical Syllabus for the 2015 exams 1 Jue 2014 Aim The aim of the Cotigecies subject is to provide a groudig i the mathematical techiques which ca be used to model ad value
More informationResearch Article Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine
Abstract ad Applied Aalysis Volume 2013, Article ID 528678, 7 pages http://dx.doi.org/10.1155/2013/528678 Research Article Crude Oil Price Predictio Based o a Dyamic Correctig Support Vector Regressio
More informationJOURNAL OF SOFTWARE, VOL. 8, NO. 2, FEBRUARY 2013 481
480 JOURNAL OF SOFTWARE, VOL. 8, NO. 2, FEBRUARY 2013 Surve o Resource Allocatio Polic ad Job Schedulig Algorithms of Cloud Computig 1 Lu Huag Software School of Xiame Uiversit, Xiame, Chia Email: bagbag_4391@qq.com
More informationAn Optimization Approach for Utilizing Cloud Services for Mobile Devices in Cloud Environment
INFORMATICA, 2015, Vol. 26, No. 1, 89 110 89 2015 Vilius Uiversity DOI: http://dx.doi.org/10.15388/iformatica.2015.40 A Optimizatio Approach for Utilizig Cloud Services for Mobile Devices i Cloud Eviromet
More informationCapacity of Wireless Networks with Heterogeneous Traffic
Capacity of Wireless Networks with Heterogeeous Traffic Migyue Ji, Zheg Wag, Hamid R. Sadjadpour, J.J. GarciaLuaAceves Departmet of Electrical Egieerig ad Computer Egieerig Uiversity of Califoria, Sata
More informationCenter, Spread, and Shape in Inference: Claims, Caveats, and Insights
Ceter, Spread, ad Shape i Iferece: Claims, Caveats, ad Isights Dr. Nacy Pfeig (Uiversity of Pittsburgh) AMATYC November 2008 Prelimiary Activities 1. I would like to produce a iterval estimate for the
More informationPlugin martingales for testing exchangeability online
Plugi martigales for testig exchageability olie Valetia Fedorova, Alex Gammerma, Ilia Nouretdiov, ad Vladimir Vovk Computer Learig Research Cetre Royal Holloway, Uiversity of Lodo, UK {valetia,ilia,alex,vovk}@cs.rhul.ac.uk
More informationCluster Validity Measurement Techniques
Cluster Validity Measuremet Techiques Ferec Kovács, Csaba Legáy, Attila Babos Departmet of Automatio ad Applied Iformatics Budapest Uiversity of Techology ad Ecoomics Goldma György tér 3, H Budapest,
More informationIdeate, Inc. Training Solutions to Give you the Leading Edge
Ideate, Ic. Traiig News 2014v1 Ideate, Ic. Traiig Solutios to Give you the Leadig Edge New Packages For All Your Traiig Needs! Bill Johso Seior MEP  Applicatio Specialist Revit MEP Fudametals Ad More!
More informationTaking DCOP to the Real World: Efficient Complete Solutions for Distributed MultiEvent Scheduling
Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed MultiEvet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria
More informationINVESTMENT PERFORMANCE COUNCIL (IPC) Guidance Statement on Calculation Methodology
Adoptio Date: 4 March 2004 Effective Date: 1 Jue 2004 Retroactive Applicatio: No Public Commet Period: Aug Nov 2002 INVESTMENT PERFORMANCE COUNCIL (IPC) Preface Guidace Statemet o Calculatio Methodology
More informationCHAPTER 3 THE TIME VALUE OF MONEY
CHAPTER 3 THE TIME VALUE OF MONEY OVERVIEW A dollar i the had today is worth more tha a dollar to be received i the future because, if you had it ow, you could ivest that dollar ad ear iterest. Of all
More information.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth
Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,
More informationVladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT
Keywords: project maagemet, resource allocatio, etwork plaig Vladimir N Burkov, Dmitri A Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT The paper deals with the problems of resource allocatio betwee
More informationOn the Capacity of Hybrid Wireless Networks
O the Capacity of Hybrid ireless Networks Beyua Liu,ZheLiu +,DoTowsley Departmet of Computer Sciece Uiversity of Massachusetts Amherst, MA 0002 + IBM T.J. atso Research Ceter P.O. Box 704 Yorktow Heights,
More informationA Software System for Optimal Virtualization of a Server Farm 1
БЪЛГАРСКА АКАДЕМИЯ НА НАУКИТЕ BULGARIAN ACADEMY OF SCIENCES ПРОБЛЕМИ НА ТЕХНИЧЕСКАТА КИБЕРНЕТИКА И РОБОТИКАТА, 60 PROBLEMS OF ENGINEERING CYBERNETICS AND ROBOTICS, 60 София 2009 Sofia A Software System
More informationPROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUSMALUS SYSTEM
PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY Physical ad Mathematical Scieces 2015, 1, p. 15 19 M a t h e m a t i c s AN ALTERNATIVE MODEL FOR BONUSMALUS SYSTEM A. G. GULYAN Chair of Actuarial Mathematics
More informationTrading the randomness  Designing an optimal trading strategy under a drifted random walk price model
Tradig the radomess  Desigig a optimal tradig strategy uder a drifted radom walk price model Yuao Wu Math 20 Project Paper Professor Zachary Hamaker Abstract: I this paper the author iteds to explore
More informationClustering Algorithm Analysis of Web Users with Dissimilarity and SOM Neural Networks
JONAL OF SOFTWARE, VOL. 7, NO., NOVEMBER 533 Clusterig Algorithm Aalysis of Web Users with Dissimilarity ad SOM Neal Networks Xiao Qiag School of Ecoomics ad maagemet, Lazhou Jiaotog Uiversity, Lazhou;
More informationDesign of Auctions for Electronic Business
Desig of Auctios for Electroic Busiess Article Ifo: Maagemet Iformatio Systems, Vol. 5 (200), No., pp. 037042 Received 8 September 2009 Accepted 7 April 200 UDC 004.738.5:339]::005; 005.52/.53 ; 347.45.6
More informationLOAD BALANCING IN PUBLIC CLOUD COMBINING THE CONCEPTS OF DATA MINING AND NETWORKING
LOAD BALACIG I PUBLIC CLOUD COMBIIG THE COCEPTS OF DATA MIIG AD ETWORKIG Priyaka R M. Tech Studet, Dept. of Computer Sciece ad Egieerig, AIET, Karataka, Idia Abstract Load balacig i the cloud computig
More informationDivide and Conquer. Maximum/minimum. Integer Multiplication. CS125 Lecture 4 Fall 2015
CS125 Lecture 4 Fall 2015 Divide ad Coquer We have see oe geeral paradigm for fidig algorithms: the greedy approach. We ow cosider aother geeral paradigm, kow as divide ad coquer. We have already see a
More informationAsymptotic Growth of Functions
CMPS Itroductio to Aalysis of Algorithms Fall 3 Asymptotic Growth of Fuctios We itroduce several types of asymptotic otatio which are used to compare the performace ad efficiecy of algorithms As we ll
More informationRainbow options. A rainbow is an option on a basket that pays in its most common form, a nonequally
Raibow optios INRODUCION A raibow is a optio o a basket that pays i its most commo form, a oequally weighted average of the assets of the basket accordig to their performace. he umber of assets is called
More informationCREATIVE MARKETING PROJECT 2016
CREATIVE MARKETING PROJECT 2016 The Creative Marketig Project is a chapter project that develops i chapter members a aalytical ad creative approach to the marketig process, actively egages chapter members
More informationOpen Access Nonoperating Urban Infrastructure Project Management Maturity Model on Agent Construction Based on the Evolutionary Algorithm
Sed Orders for Reprits to reprits@bethamsciece.ae 112 The Ope Costructio ad Buildig Techology Joural, 2015, 9, 112116 Ope Access Nooperatig Urba Ifrastructure Project Maagemet Maturity Model o Aget Costructio
More informationCCH Accountants Starter Pack
CCH Accoutats Starter Pack We may be a bit smaller, but fudametally we re o differet to ay other accoutig practice. Util ow, smaller firms have faced a stark choice: Buy cheaply, kowig that the practice
More informationMaximum Likelihood Estimators.
Lecture 2 Maximum Likelihood Estimators. Matlab example. As a motivatio, let us look at oe Matlab example. Let us geerate a radom sample of size 00 from beta distributio Beta(5, 2). We will lear the defiitio
More informationBusiness RulesDriven SOA. A Framework for MultiTenant Cloud Computing
Lect. Phd. Liviu Gabriel CRETU / SPRERS evet Traiig o software services, Timisoara, Romaia, 610 dec 2010 www.feaa.uaic.ro Busiess RulesDrive SOA. A Framework for MultiTeat Cloud Computig Lect. Ph.D.
More informationA Faster ClauseShortening Algorithm for SAT with No Restriction on Clause Length
Joural o Satisfiability, Boolea Modelig ad Computatio 1 2005) 4960 A Faster ClauseShorteig Algorithm for SAT with No Restrictio o Clause Legth Evgey Datsi Alexader Wolpert Departmet of Computer Sciece
More informationChapter 5 Unit 1. IET 350 Engineering Economics. Learning Objectives Chapter 5. Learning Objectives Unit 1. Annual Amount and Gradient Functions
Chapter 5 Uit Aual Amout ad Gradiet Fuctios IET 350 Egieerig Ecoomics Learig Objectives Chapter 5 Upo completio of this chapter you should uderstad: Calculatig future values from aual amouts. Calculatig
More informationoptimise your investment in Microsoft technology. Microsoft Consulting Services from CIBER
optimise your ivestmet i Microsoft techology. Microsoft Cosultig Services from Microsoft Cosultig Services from MICROSOFT CONSULTING SERVICES ca help with ay stage i the lifecycle of adoptig Microsoft
More informationChatpun Khamyat Department of Industrial Engineering, Kasetsart University, Bangkok, Thailand ocpky@hotmail.com
SOLVING THE OIL DELIVERY TRUCKS ROUTING PROBLEM WITH MODIFY MULTITRAVELING SALESMAN PROBLEM APPROACH CASE STUDY: THE SME'S OIL LOGISTIC COMPANY IN BANGKOK THAILAND Chatpu Khamyat Departmet of Idustrial
More informationThe Stable Marriage Problem
The Stable Marriage Problem William Hut Lae Departmet of Computer Sciece ad Electrical Egieerig, West Virgiia Uiversity, Morgatow, WV William.Hut@mail.wvu.edu 1 Itroductio Imagie you are a matchmaker,
More informationOptimization of Large Data in Cloud computing using Replication Methods
Optimizatio of Large Data i Cloud computig usig Replicatio Methods Vijaya KumarC, Dr. G.A. Ramachadhra Computer Sciece ad Techology, Sri Krishadevaraya Uiversity Aatapuramu, AdhraPradesh, Idia AbstractCloud
More informationEngineering Data Management
BaaERP 5.0c Maufacturig Egieerig Data Maagemet Module Procedure UP128A US Documetiformatio Documet Documet code : UP128A US Documet group : User Documetatio Documet title : Egieerig Data Maagemet Applicatio/Package
More informationIT Support. 020 8269 6878 n www.premierchoiceinternet.com n support@premierchoiceinternet.com. 30 Day FREE Trial. IT Support from 8p/user
IT Support IT Support Premier Choice Iteret has bee providig reliable, proactive & affordable IT Support solutios to compaies based i Lodo ad the South East of Eglad sice 2002. Our goal is to provide our
More informationCONTROL CHART BASED ON A MULTIPLICATIVEBINOMIAL DISTRIBUTION
www.arpapress.com/volumes/vol8issue2/ijrras_8_2_04.pdf CONTROL CHART BASED ON A MULTIPLICATIVEBINOMIAL DISTRIBUTION Elsayed A. E. Habib Departmet of Statistics ad Mathematics, Faculty of Commerce, Beha
More informationclient communication
CCH Portal cliet commuicatio facig today s challeges Like most accoutacy practices, we ow use email for most cliet commuicatio. It s quick ad easy, but we do worry about the security of sesitive data.
More informationCase Study. Normal and t Distributions. Density Plot. Normal Distributions
Case Study Normal ad t Distributios Bret Halo ad Bret Larget Departmet of Statistics Uiversity of Wiscosi Madiso October 11 13, 2011 Case Study Body temperature varies withi idividuals over time (it ca
More informationLECTURE 13: Crossvalidation
LECTURE 3: Crossvalidatio Resampli methods Cross Validatio Bootstrap Bias ad variace estimatio with the Bootstrap Threeway data partitioi Itroductio to Patter Aalysis Ricardo GutierrezOsua Texas A&M
More informationSaaS Resource Management Model and Architecture Research
Sed Orders for Reprits to reprits@bethamsciece.ae The Ope Cyberetics & Systemics Joural, 2015, 9, 433442 433 SaaS Resource Maagemet Model ad Architecture Research Ope Access Zhag Xiaodog 1,2,*, Zha Deche
More informationJournal of Chemical and Pharmaceutical Research, 2015, 7(3):11841190. Research Article
Available olie www.ocpr.com Joural of Chemical ad Pharmaceutical Research, 15, 7(3):1184119 Research Article ISSN : 9757384 CODEN(USA) : JCPRC5 Iformatio systems' buildig of small ad medium eterprises
More information5: Introduction to Estimation
5: Itroductio to Estimatio Cotets Acroyms ad symbols... 1 Statistical iferece... Estimatig µ with cofidece... 3 Samplig distributio of the mea... 3 Cofidece Iterval for μ whe σ is kow before had... 4 Sample
More informationAutomatic Tuning for FOREX Trading System Using Fuzzy Time Series
utomatic Tuig for FOREX Tradig System Usig Fuzzy Time Series Kraimo Maeesilp ad Pitihate Soorasa bstract Efficiecy of the automatic currecy tradig system is time depedet due to usig fixed parameters which
More informationHypergeometric Distributions
7.4 Hypergeometric Distributios Whe choosig the startig lieup for a game, a coach obviously has to choose a differet player for each positio. Similarly, whe a uio elects delegates for a covetio or you
More informationBiology 171L Environment and Ecology Lab Lab 2: Descriptive Statistics, Presenting Data and Graphing Relationships
Biology 171L Eviromet ad Ecology Lab Lab : Descriptive Statistics, Presetig Data ad Graphig Relatioships Itroductio Log lists of data are ofte ot very useful for idetifyig geeral treds i the data or the
More informationCOMPUTING EFFICIENCY METRICS FOR SYNERGIC INTELLIGENT TRANSPORTATION SYSTEMS
Trasport ad Teleuicatio Vol, No 4, 200 Trasport ad Teleuicatio, 200, Volume, No 4, 66 74 Trasport ad Teleuicatio Istitute, Lomoosova, Riga, LV09, Latvia COMPUTING EFFICIENCY METRICS FOR SYNERGIC INTELLIGENT
More informationChapter 7: Confidence Interval and Sample Size
Chapter 7: Cofidece Iterval ad Sample Size Learig Objectives Upo successful completio of Chapter 7, you will be able to: Fid the cofidece iterval for the mea, proportio, ad variace. Determie the miimum
More informationRunning Time ( 3.1) Analysis of Algorithms. Experimental Studies ( 3.1.1) Limitations of Experiments. Pseudocode ( 3.1.2) Theoretical Analysis
Ruig Time ( 3.) Aalysis of Algorithms Iput Algorithm Output A algorithm is a stepbystep procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.
More informationPage 1. Real Options for Engineering Systems. What are we up to? Today s agenda. J1: Real Options for Engineering Systems. Richard de Neufville
Real Optios for Egieerig Systems J: Real Optios for Egieerig Systems By (MIT) Stefa Scholtes (CU) Course website: http://msl.mit.edu/cmi/ardet_2002 Stefa Scholtes Judge Istitute of Maagemet, CU Slide What
More informationA Churnprevented Bandwidth Allocation Algorithm for Dynamic Demands In IaaS Cloud
A Churpreveted Badwidth Allocatio Algorithm for Dyamic Demads I IaaS Cloud Jilei Yag, Hui Xie ad Jiayu Li Departmet of Computer Sciece ad Techology, Tsighua Uiversity, Beijig, P.R. Chia Tsighua Natioal
More informationPatentability of Computer Software and Business Methods
WIPOMOST Itermediate Traiig Course o Practical Itellectual Property Issues i Busiess November 10 to 14, 2003 Patetability of Computer Software ad Busiess Methods Tomoko Miyamoto Patet Law Sectio Patet
More informationIs the Event Study Methodology Useful for Merger Analysis? A Comparison of Stock Market and Accounting Data
Discussio Paper No. 163 Is the Evet Study Methodology Useful for Merger Aalysis? A Compariso of Stock Market ad Accoutig Data Tomaso Duso* laus Gugler** Burci Yurtoglu*** September 2006 *Tomaso Duso Humboldt
More informationDomain 1: Identifying Cause of and Resolving Desktop Application Issues Identifying and Resolving New Software Installation Issues
Maual Widows 7 Eterprise Desktop Support Techicia (70685) 18004186789 Domai 1: Idetifyig Cause of ad Resolvig Desktop Applicatio Issues Idetifyig ad Resolvig New Software Istallatio Issues This sectio
More informationMARTINGALES AND A BASIC APPLICATION
MARTINGALES AND A BASIC APPLICATION TURNER SMITH Abstract. This paper will develop the measuretheoretic approach to probability i order to preset the defiitio of martigales. From there we will apply this
More informationProperties of MLE: consistency, asymptotic normality. Fisher information.
Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout
More information