A Knowledge Sharing Resource Library Platform Based on Multivariate Large Data Predictive Compensation

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1 A Knowledge Sharng Resource Lbrary Platform Based on Multvarate Large Data Predctve Compensaton Yng Wang Informaton Engneerng Insttute, Informaton Teachng Appled Technology Extenson Center, Chongqng Vocatonal Insttute of Engneerng, Chongqng, 4226, Chna Xuepng Yang Informaton Teachng Appled Technology Extenson Center, Chongqng Vocatonal Insttute of Engneerng, Chongqng, 4226, Chna Guangha Chen Informaton Teachng Appled Technology Extenson Center, Chongqng Vocatonal Insttute of Engneerng, Chongqng, 4226, Chna Abstract In order to mprove the ablty of multvarate large data predctve compensaton n knowledge sharng resource lbrary, ths paper proposes a knowledge sharng resource lbrary platform algorthm whch ntegrates multvarable large data predcton compensaton. The algorthm ncludes data predcton strategy and data compensaton strategy. The data predcton strategy dvdes the large data whch ncludes the conflcts n the shared resource lbrary nto multple data subsets, allocatng the relevant data subsets to the same knowledge sharng resource base as much as possble to avod the modulable loss caused by large data nterrupton between the resource lbrary platform ; Based on the large data relevance evaluaton results, the data compensaton strategy dvdes the data subsets that cannot be allocated to the same resource lbrary platform and moves the splt data to the lghtest load resource lbrary platform. The results show that the predcton compensaton ablty of the proposed algorthm s hgher than the smlar algorthm, and the system self-spn loss s lower than the smlar algorthm. Keywords: Multvarate Large Data, Predcted Not to Compensate, Knowledge Sharng, Resource Platform.. INTRODUCTION In order to gve full play to mult-varable large data predctve compensaton ablty of the multresource platform, we need conduct large data predcton and compensaton on dfferent resources platforms (Aras and Bae, 26). Mnmze the waste of resources caused by dependence between the large data of dfferent resources dstrbuted on the platform and mutual blockage of the system (Shen, L, Lu, and Grant, 25; L, Zhu, and Zhang, 25). Data predcton algorthm of mult-resource lbrary platform supposes large data s ndependent of each other, usng a heurstc algorthm whch s smlar to solve the "packng problem" for data predcton (Cheng and Ma, 26; Jn, Yu, and Sang, 25).Ths algorthm, although not costly, gnores the constrant relaton of mutual excluson between large data (Salovaara and Tuunanen, 25); Combned wth the mprecse calculaton model and the packng algorthm, the real-tme schedulng mechansm based on large data replcaton was proposed.however, ths algorthm does not consder the case of large data mutex access to shared resource lbrary (Alersteald, 25; Gang and Ravchandran, 25); The packet moderaton algorthm selects the earlest avalable processor for data predcton when the large data does not access the resource, but the algorthm requres a large computatonal overhead (Adegbege and Heath, 25; Kavous-Fard, Khosrav, and Nahavand, 27). Based on the multvarable large data model n the knowledge sharng resource lbrary, ths paper proposes a knowledge sharng resource lbrary platform algorthm whch ntegrates the mult-varable large data predcton compensaton, whch dvdes the smlar large data n the knowledge sharng resource lbrary nto multple data Subset, moves the relevant data subset to the same resource lbrary to avod large data block between the resource lbrary platform; When the relevant data subset cannot be allocated to the same resource lbrary, the relevant data subsets are splt accordng to the large data relevance evaluaton method to reduce the nfluence of the large data blockage between the resource lbrary platform on the system utlzaton.fnally, The expermental evaluaton verfes the superorty of the algorthm. 2. KNOWLEDGE SHARING RESOURCE LIBRARY MODEL Multvarable cycle data (Task) s expressed as three tuples τ=(t,c,π), T for large data cycle, C for 683

2 bg data worst-case executon tme, π for bg data prorty. τs ready at the begnnng of each cycle, no release jtter, an executon of τ s called a large data nstance (Instance or Job), expressed as J. The relatve cutoff deadlne of large data s equal to the large data perod (Implct Deadlne). Defnton. The CPU utlzaton rato of τ s the rato of the worst executon tme of τ to the large data perod,.e. u=c/t. The perod large dataset Γ={τ,τ2,,τn} s executed on homogeneous mult-resource lbrary platform processor P={p,p2,,pm} whch s conssted of m resource lbrary platform. p(τ)denotes the resource lbrary platform where τ s located, τ(pk) denotes a large dataset dstrbuted to resource lbrary platform pk. The system has a set of Q shared lbrares Φ={ρ,ρ2,,ρq},Θ Φreprents the shared resource pool that requred τ to access, whch must be τ exclusve access to ρs Θ. fρs can only be assgned to the same bg data repostory platform access, then t can be called local resources, otherwse known as global resources. The collecton of a local resource n Ф s denoted as ФL, the global resource collecton s expressed as ΦG. when τ Γ and all other bg data do not access the same shared resource lbrary, called τ as ndependent data, data subset of all ndependent data consstng n theγs denoted as '. 3. ANALYSIS OF LARGE DATA BLOCKING IN KNOWLEDGE RESOURCE LIBRARY PLATFORM The large data wll be blocked when a large data request access has been locked global resources by other repostory platforms. Accordng to the MSRP protocol, large data spns to wat for global resources. When multple large data wats for the same global resource, n order to avod large data starved to death, the FIFO spn mechansm s used n MSRP. Lemma. If τj accesses the global resource ρsfor the longest tme of ξj,s, then any τ pk requests the maxmum tme to wat for ρs W max () k,s j,s j p p p r r Defnton 2. The spn loss (Sk) of the repostory platform pk s the rato of the longest tme to spn to tlcm for all large data on the repostory platform durng the tlcm tme, where tlcm s the least common multple the cycle of all large data on the resource lbrary platform. In order to meet the schedulablty of real-tme systems, t s often necessary to reserve a certan amount of processor resources to ensure that the system can stll meet the deadlne requrements of real-tme large data n the worst case. Therefore, reducng the spn loss can reduce system resource waste, and mprove the utlzaton rate. Theorem. The system scalablty loss caused by the schedulng τ pk s equal to the self-spn loss Sk () caused by τ to the resource lbrary pk k S k n,s Wk,s s G T (2) 4. DATA PREDICTIVE COMPENSATION ALGORITHM FOR SHARED RESOURCE PLATFORM 4.. Data Predcton Strategy Data predcton strategy: All the large data n the conflct that exsts n the Γ s dvded nto a subset of the relevant data and the data n the same dependent data subset s predcted to the same resource lbrary platform, where any two large dataτ Andτ2 access resourcesρ, andτ2 andτ3 access resourcesρ2, then the three large data wll be dvded nto the same correlaton data subset, as shown n Fgure, the relevant data subset. The data n ' wll be predcted to any resource lbrary platform but wll not cause large data blockage between the lbrary platform. Through the followng recursve algorthm can acheve data predcton. The basc dea of algorthm s as follows: () Assume that the ntal state of all large data s ndependent (.ndep TRUE) and no groupng (.lnk NONE). (2) f all the large data.lnk are not NONE, the algorthm ends; Otherwse, select an ungrouped large data (.lnksnone), modfy ts groupng status.lnk, and decde whether to share the same lbrary as other large data accesses. 684

3 (3) f access to the same shared lbrary wth a large data j, then the two large data state.ndep set to FALSE, the packet status j wll be set to.lnk, and j wll be dvded nto the same relevant data subset concentrated; and start from j then repeat the step from the begnnng to recursvely. Fgure. Data predcton strategy (4) to determne whether the same large data access to the same shared resource lbrary, f access to the same shared resource lbrary jumps to step (3); Otherwse skp to step (2). After the above steps, all the large data wth the.ndep TRUE are ndependent data, whch form a separate subset of the data ' and all the large data wth the same.lnk values are dvded nto the same relevant data subset, resultng n a seres of related data subsets Large Data Relevance Evaluaton Method The large data relevance evaluaton method s proposed to measure the correlaton between dfferent large data n the relevant data subset. Lemma 2. Arbtrary,τj τ(pk), the spn loss caused to pk s equal to the sum of the spn losses caused and j to pk S j S S j (3) k k k The maxmum tme for accessng all shared resource pools of x( x n) large data s represented by x q (q s the number of shared resource banks) order matrx Ux q. Among that, the arbtrary element u,s represents the -th number of x large data(assume t to be c ) the maxmum tme to access the shared resource lbrary s u,s=ξc,s( Lemma). If u,s=, there s no access to the shared resource lbrary. s s U xq u u u u u u u u u,, 2,q 2, 2, 2 2,q x, x, 2 x,q (4) Theorem 2. Set a, b,, c x n as the number of related data obtaned by algorthm, and use x q (q s the number of shared resource banks). The matrx Ux q represents the tme of to access the shared resource lbrary. For any ( set t as n the matrx Ux q the number c rows correspond to large data) are splt out and allocated to the repostory platform pr, and the rest of the data s predcted to be on the resource lbrary platform pk (r k), then the rest of the large data on the lbrary platform pk the spn loss caused s: 685

4 a c,d,s S k q s T a c,d d,xd c f d c,s c,s d,s f d,s u n,u u, uc,s ud,s (5) (6) among them, f (d) s the large data sequence number correspondng to the matrx Ux q number d row. The spn loss descrbed by Theorem 2 s caused by the large data assocated wth n accessng the same shared resource lbrary. Therefore, Sk depcts a correlaton quanttatvely descrbed from the perspectve of spn loss. Ths paper used k wth the other large data evaluaton ndcators. wth the rest of the large data n s S as the relevance of n 4.3. Data Compensaton Strategy Let the ntal data subset be allocated to the repostory platform pk, n the frst splt wll be assgned to pr (r k). The large data u that s splt then can be assgned to pr or some other repostory platform py (Y k, y r). Accordng to Lemma, when u and access any same shared resource lbrary s,f assgn u to pr, then the maxmum tme to wat for large data access s on the repostory platform pk s max(ξ,s,ξu,s) ; And f u t s allocated to py, the longest tme to wat for the large data access on the resource lbrary platform pk s ξ,s+ξu,s. Combnng Lemma and Theorem, we wll predct the data splt from to the same repostory platform can reduce the large data blockng tme between the repostory platform, thereby reducng unnecessary spn loss. When the splt data s predcted to be on the same repostory platform, these large data can be treated as a vrtual large data, and the large data currently to be splt s logcally merged nto the orgnal vrtual large data, the splt s equvalent to splttng out a vrtual large data from the ntal correlaton data subset. As shown n Fgure 2, when 3 t s splt from the relevant data subset (whch has been splt out at ths tme) 3 wll be logcally merged nto a vrtual large data, and then the vrtual large data wll be splt out from the orgnal correlaton data subset. Fgure 2. Example of vrtual large data 686

5 Because each row vector n matrx U descrbes a large data, t s possble to realze the logcal mergng of large data by lne vector transformaton. Set a, b,, c x n as the relevant data subset obtaned by algorthm, matrx Ux q Let the orgnal vrtual large data pont to the matrx Ux q the number v row.when s the c (c> ) splt large data, n turn, the matrx Ux q the number v row s column elements (uv,s) as follows: s,q,uv,s maxu v,s,u,s (7) Theorem 3. If the ntal s allocated to the repostory platform pk, all the large data splt from then are allocated to the repostory platform pr (r k), then the remanng large data watng for vrtual large data S. on the resource lbrary platform The maxmum spn loss caused by pk s k Accordng to Theorem 3, we can take k S the correlaton evaluaton ndex between the vrtual large data and the remanng large data. As the vrtual large data s merged one by one for a large number of large data, t wll make large data k S get mnmum value and splt out, you can ensure that after each splt n the remanng large data on ts repostory platform wll cause the mnmum spn loss. The data compensaton strategy s obtaned by frst dvdng the large data n whch get the mnmum value from k S and then allocatng t to the other repostory platform to determne the schedulablty of the system. If the system can be scheduled, the splt process ends, otherwse, large data constructon methods contnue to splt, one by one wll make the mnmum value of k S the data to predct the frst splt out of the large data on the repostory platform untl the system can be scheduled. If all the large data n after the splt s stll not Schedulng, t ndcates that the data compensaton polcy has faled Data Predcton Compensaton Algorthm Overall Descrpton Combned wth the above analyss, ths paper presents a mult-varable and large data predcton compensaton algorthm for shared resource lbrary platform. The algorthm manly ncludes the followng three steps. () Usng the data predcton strategy, the set of large data to be allocated s dvded nto a seres of related data subsets and a subset of ndependent data, the subset of related data s sorted by CPU utlzaton u, and the relevant data subsets are allocated by algorthm (The large data of the same relevant data subset s allocated to the same repostory platform, otherwse the relevant data subset s consdered not to be allocated). (2) Combnng the data compensaton strategy and the algorthm.frstly, the algorthm s used to allocate the relevant data subsets of the data to be splt nto the current resource lbrary platform to reduce the number of large data that needs to be splt out Then, accordng to the large data relevance evaluaton results, the large data n the relevant data subset s splt out one by one untl the system satsfes the schedulablty requrement, and f all the large data n s stll not dspatched after the large data s splt, (To restore the large data allocated to each repostory platform to the current round of data compensaton strategy before the mplementaton of the stuaton). (3) usng algorthm to allocate ndependent large data and second stage unallocated large data. 5. EXPERIMENTAL EVALUATION 5.. Performance In ths paper, the large data set acceptance rate of 8 large-scale resource platform s used as the performance ndex of the data predcton algorthm. Therefore, each experment randomly generates N = large data set, and the statstcal data predcton algorthm A can schedule large data sets M, the acceptance rate of the large data set of algorthm A s M / N. The larger the acceptance rate of large data sets ndcates the hgher effcency of the algorthm. In addton, the system average spn loss of each algorthm s compared, the system self-spn loss s defned as follows: p P, p k k N S k (8) 687

6 5.2. Large Data Smulaton Large number of data n by the major data CPU utlzaton u dstrbuton and system utlzaton u SU together to determne (<SU ) m () u s randomly obtaned n [.,.3] accordng to the UUnfast-Dscard algorthm. (2) The natural logarthm of the large data perod T s unformly dstrbuted and s randomly generated n [, ], thus smulatng most real-tme large data. (3) large data executon tme C by the large data cycle and large data CPU utlzaton decson, and meet C = T v. (4) The large data prorty s consstent wth the Rate Monotonc (RM) [7] algorthm, that s, the longer the large data perod, the smaller the relatve prorty. (5) Each 6-shared resource lbrary s dvded nto a group, a group of shared resource lbrary can be 8 large data random access. (6) the major data contans to 6 crtcal areas, the crtcal area of the correspondng shared resource lbrary n the large data access to the shared resource lbrary group randomly selected. (7) the tme of large data access to each shared resource lbrary, that s, the length of the crtcal area n [,2] value Expermental Results () large data collecton acceptance rate. When the crtcal area length s 4, each large data contans two crtcal areas, the large data collecton rate and the system utlzaton SU relatonshp as shown n Fgure 3. The algorthm large dataset acceptance rate decreases wth the ncrease of SU.WWD and algorthm have large data sets when SU>.6 s not dspatchable, and the algorthm does not schedule large data sets after SU>.7, n whch algorthm the large data set acceptance rate s greater than the algorthm. bg data set acceptance rate/% system utlzaton rate Fgure 3. Large dataset acceptance rate ncreases wth the system utlzaton Fgure 4 shows the effect of the length of the crtcal regon on the performance of each algorthm, n whch each large data contans two crtcal regons, SU =.65 (Fgure 3 shows SU =.65 when the algorthm large dataset acceptance rate close.) As large data from (See Lemma ), the acceptance rates of the large data sets are reduced, and when the relevant data subset cannot be allocated to the same repostory platform, the SRaware algorthm Accordng to the large data relevance evaluaton method wll be splt out of the large data as much as possble to the same repostory platform, so large data usually only need to wat for a large resource on a resource platform, the spn loss sgnfcantly reduced (detaled See the ratonale, theorem ). The algorthm for random large-scale data combnaton of random splt on the large data allocated randomly, so the length of the crtcal area caused by a longer system utlzaton loss. The performance of the algorthm approaches the algorthm due to the randomness n the segmentaton of the relevant data subsets. 688

7 mult-varable large data set acceptance rate/% crtcal zone length Fgure 4. Large dataset acceptance rate ncreases wth the crtcal area length Fgure 5 shows the nfluence of the crtcal data of the large data on the acceptance rate of large data sets when SU =.65 and the crtcal regon length 4. Wth the ncrease of the crtcal area of large data, the large data spn wats for the resource resources caused by the shared resource lbrary The correspondng loss of the large data set of each algorthm s also decreased. Meanwhle, the relatonshp between the large data rooms through the access to the shared resource lbrary s more closely, and t s easy to form a large subset of related data. The algorthm and the algorthm need to carry on the frequent correlaton data subsets. The advantage of the algorthm n the data compensaton strategy makes the large data collecton acceptance rate hgher than the algorthm. mult-varable large data set acceptance rate/% the number of crtcal zone Fgure 5. Large dataset acceptance rate ncreases wth the ncrease n the number of crtcal data area (2) System spn loss. The expermental parameters of Fgure 6, Fgure 7 and Fgure 8 correspond to Fgure 3, Fgure 4, and Fgure 5. System spn losses are calculated from equaton (8), whch contans the spn loss of the large data n the algorthm not dspatchable the large data set, so the system spn loss s greater than. In the experment, the same large data set s allocated, so the result of the allocaton of the algorthm s (Includng non-dspatchable condtons) for statstcal comparson, more able to reflect the performance of dfferent algorthms. system average selfspn loss system utlzaton rate Fgure 6. System average spn loss ncreases wth system utlzaton 689

8 system average selfspn loss crtcal zone length Fgure 7. System spn loss ncreases wth ncreasng crtcal length system average selfspn loss the number of crtcal zone Fgure 8. System spn loss ncreases wth the ncrease n the number of crtcal areas of large data The expermental results show that the system spn loss vares wth the trend of the large data set and the change trend of the large data set acceptance rate, n whch the algorthm system has the smallest spn loss, the largest algorthm, the algorthm spn loss approxmaton and algorthm.it s shown that the spn loss as a measure of the correlaton between large data has a certan practcal sgnfcance, and wll mnmze the large data spn loss as a data predcton algorthm optmzaton target can mnmze system resources waste and mprove system utlzaton. 6. CONCLUSIONS Ths paper presents a knowledge sharng resource lbrary platform algorthm whch ntegrates multvarable large data predcton compensaton. The algorthm ncludes data predcton strategy and data compensaton strategy to avod or reduce the large data blockage between the resource lbrary platform, thus reducng the large data spn that wll cause the waste of system resource and mprove system utlzaton. Fnally, the performance of the proposed algorthm s compared wth that of the smlar algorthm. The expermental results show that the predcton rate of the new algorthm s hgher than that of the and algorthms. ACKNOWLEDGEMENTS Ths work was supported by New Technology Promoton Project of Hgher Vocatonal and Techncal Colleges n Chongqng "Development and Applcaton of Smart Campus New Technology Based on the Moble Internet and Bg Data Support" (No.: GZTG269); Scence and Technology Research Project of Chongqng Muncpal Educaton Commsson "Unversty Informaton Innovaton Teachng Integrated Platform Research and Practce Based on Cloud Technology" (No.: KJ732434); and Hgher Educaton Teachng Reform Research Project n Chongqng "Teachng Resource Lbrary Platform Constructon Research and Practce Based on Cloud Technology" (No. 526). REFERENCES Adegbege, A. A., Heath, W. P. (25) Drectonalty compensaton for lnear multvarable ant-wndup synthess, Internatonal Journal of Control, 88 (), pp

9 Alersteald, L. (25) Insttutonalzaton of knowledge management n the federal government: an exploraton of the mechansms, Polymer, 52 (7), pp Aras, M. B., Bae, S. (26) Electrc vehcle chargng demand forecastng model based on bg data technologes, Appled Energy, 83, pp Cheng, H., Ma, Z. (26) A lterature overvew of knowledge space between petr nets and ontologes, Knowledge Engneerng Revew, 3 (3), pp Gang, K.W., Ravchandran, T. (25) Explorng the determnants of knowledge exchange n vrtual communtes, IEEE Transactons on Engneerng Management, 62 (), pp Jn, K., Yu, S.Y., Sang, O.P. (25) Smart knowledge sharng system for cybernfrastructure, Journal of Supercomputng, 72 (), pp.-8. Kavous-Fard, A., Khosrav, A., Nahavand, S. (27) Reactve power compensaton n electrc arc furnaces usng databases, IEEE Transactons on Industral Electroncs, 64 (7), pp L, B., Zhu, X., L, R., Zhang, C. (25) Ratng knowledge sharng n cross-doman conduct flterng, IEEE Transactons on Cybernetcs, 45 (5), pp.54. Salovaara, A., Tuunanen, V. (25) Medated sharng as software developers' strategy to manage ephemeral knowledge, Quaternary Scence Revews, 2(2 3), pp Shen, H., L, Z., Lu, J., Grant, J.E. (25) Knowledge sharng n the onlne socal network of yahoo! answers and ts mplcatons, IEEE Transactons on Computers, 64 (6), pp