Online Algorithms for Uploading Deferrable Big Data to The Cloud

Save this PDF as:

Size: px
Start display at page:

Transcription

4 through one ISP only. The user decdes to delver the workload to apper n te slot t. ssue t takes a unt te to transt data va denote the total data sze at apper n te slot t +. M t+ can be calculated as the suaton of all transtted workloads at te slot t: D M t+ = W t d x d,t d, M. ISPs. Let M t+ ssue the appers take te slot to process a receved workload. Therefore the appers wll transfer data to the reducer n te slot t+. Let T,r t+ be the traffc fro apper to reducer r s n te slot t + : V t+,r = βm t+ y t+,r, M, r R. (5) The axu traffc volue of the ISP (, r), V,r, satsfes: V,r V t+,r, t. (6) Notce that the MapReduce fraework parttons the output pars (key/value) of appers to reducers usng hash functons. ll values for the sae key are always reduced at the sae reducer no atter whch apper t coes fro. Furtherore, we assue that data generated n the data locatons are unforly xed, therefore we have: y t+,r = z r, M, r R. (7) Ths equaton also ples that the superscrpt of y,r t+ can be gnored. Now we can forulate the overall traffc cost nzaton proble for the cloud user, under the MX contract charge odel: nze f (V ) + f,r(v,r) (8),r subject to: V V t, t, (8a) V,r V,r t, t,, r (8b) D x d,t = n, t, (8c) n =, (8d) x d,t, n {, }, (8e). where V t and V t,r are defned n Eqn. (3) and Eqn. (5), respectvely. n s a bnary varable ndcatng whether ISP s eployed or not. For ease of reference, notatons are suarzed l n Tab. I. IV. THE SINGLE ISP CSE We frst nvestgate the basc case that ncludes one apper and one reducer only, co-located n the sae data center, wth no bandwdth cost between the pars. Gven a MX charge odel at the ISP, the algorth tres to explot the allowable delay by schedulng the traffc to the best te slot wthn the allowed te wndow, for reducng the charge volue. Ths can be llustrated through a toy exaple: n t =, a Sybol Defnton TBLE I NOTTION D the axu delay fro the te data s generated to the te the data locaton begns to transt t to the appers. M the set of appers. R the set of reducers. Soe apper and reducer ay be n the sae locaton,.e., M R. W t the workload released n user locaton at te slot t. x d,t the porton of the workload W t that s assgned to apper at te slot t + d. β the rato of the sze of output of a apper to the sze of ts nput. y,r t the porton of the output of apper that s transtted to reducer r at te slot t. z r the porton of the key space apped nto reducer r. V t the total traffc that goes through ISP at te slot t. f (y) the cost of ISP for the nput y. job (MB, ax delay = 9 te slots) s released; n the followng te slots, no jobs are released. If the algorth sooths the traffc across the te slots, the charge volue can be reduced to M B/5n, fro M B/5n f edate transsson s adopted.. The Pral & Dual Cost Mnzaton LPs We can drop the locaton ndex (, r) n ths basc scenaro of one apper and one reducer locatng n the sae data centre. Note that the chargng functon f s a non-decreasng functon of the axu traffc volue. Mnzng the axu traffc volue therefore ples nzng the bandwdth cost. Consequently, the cost nzaton proble n our basc sngle ISP scenaro can be forulated nto the followng (pral) lnear progra (LP): subject to: n{d,t } nze V (9) W t d x d,t d V, t T (9a) D x d,t =, t T D (9b) x d,t, V, d D, t T D, (9c) where T = [, T ], T D = [, T D], D = [, D] and x d,t =, t > T D, d D Introducng dual varable y and z to constrants (9a) and (9b) respectvely, we forulate the correspondng dual LP: subject to: axze T y t t= T D t= z t () (a) z t W ty t+d, t T D, d D (b) y t, t T (c) z t unconstranted, t T D (d)

5 The nput begns wth W and ends wth W T D, and W T D+ =,..., W T = s padded to the tal of the nput. We use P and D to denote feasble solutons to the pral and dual LPs, respectvely. The optzaton n (9) s a standard lnear progra. For an offlne optal soluton, one can sply solve (9) usng a standard LP soluton algorth such as the splex ethod or the nteror-pont ethod. B. Onlne algorths The splest onlne soluton n the basc one ISP scenaro s the edate transfer algorth (IT). Once a new job arrves, IT transfers t to appers edately wthout any delay. Next we analyze the copettve rato of IT, as copared to the offlne optu. Theore. IT s (D + )-copettve. Proof: Consder the followng nput: (W,,,,,...). IT wll process t edately wth bandwdth cost: W. However the offlne optal algorth wll dvde the workload nto sall peces: W/(D+), W/(D+),...W/(D+),,,,...), feasble wthn the deadlne D, wth axu traffc volue W/(D + ). W Copettve rato λ W/(D + ) = D + We hence obtan a lower bound on the copettve rato of IT, D +. Next we prove D + s also an upper-bound. Wthout explotng any delays, IT provdes a feasble soluton to the pral proble, whch s denoted as P IT. P IT = ax W t t Now we desgn a feasble soluton to the dual proble as follows (assue τ = arg ax t W t ): { /(D + ) f t = τ,..., τ + D y t = otherwse { /(D + )Wt f t = τ z t = otherwse D = D + Wτ So the copettve rato s: Copettve rato λ = PIT OP T PIT D = D + Rearks: f D =,.e. jobs are not deferrable, the offlne optal algorth degrades nto IT, agreeng wth the theore, whch clas IT s -copettve (D + = ). IT s apparently not deal, and ay lead to hgh peak traffc and hgh bandwdth cost as copared wth the offlne optu. Golubchk et al. [5] desgn a cost-aware algorth that strkes to spread out bandwdth deand by utlzng all possble delays, referred to as the Sple Soothng lgorth. Upon recevng a new workload, Sple Soothng evenly dvdes t nto D + parts, and processes the one by one n the current te slot and the followng D te slots, as shown n lgorth. lgorth The Sple Soothng lgorth [5] : for τ = to T D do : for d = to D do 3: x d,τ = /(D + ) 4: end for 5: end for Theore. [5] The copettve rato of Sple Soothng s D+. Theore can be proven through weak LP dualty,.e., usng a feasble dual as the lower bound of the offlne optal. Sple Soothng s very sple, but guarantees a worst case copettve rato saller than. Nonetheless, there s stll roo for further proveents, snce Sple Soothng gnores avalable nforaton such as the htherto axu traffc volue transtted, and the current pressure fro backlogged traffc and ther deadlnes. Such an observaton otvated our desgn of the ore sophstcated Heurstc Soothng algorth for the case D, as shown n lgorth. Here T s the charge perod, τ s the current te slot, and H d s the total volue of data that have been buffered for d te slots. lgorth The Heurstc Soothng lgorth : V ax = : W τ =, τ = T D +,..., T ; 3: H d =, d =,..., D; 4: for τ = to T do { 5: V τ = n } D d= H d D } W τ + D d= H d, ax{v ax, Wτ D+ + 6: f V ax < V τ then 7: V ax = V τ ; 8: end f 9: Transfer the traffc followng Earlest Deadlne Frst (EDF) strategy; : Update H d, d =,..., D; : end for Theore 3. The copettve rato of Heurstc Soothng s lower bounded by ( e ). Proof: Consder the followng nput: (W, W,...W,,..., ) whose frst D + te slots are W. The traffc deand V ncreases untl te slot D +. V D+ = W D + + W (D )W + D + (D + )D (D )D W (D + )D D = W D + ( + D( ( D )D )) We can fnd a feasble pral soluton whch yelds the charge volue D+ D+W. Ths pral soluton s an upper bound of the offlne optu. Therefore the lower bound D+ of the copettve rato λ V D+ (D+) = D+ (D+) ( + D( ( D )D )) ( e ) as D +. Notce that

7 We next defne functon Λ n(x) where x R n \ {}: Λ n(x) = M φ n n(ω )( x w) α / x α w w Ω w= Ω W Lea. Gven any uploadng schee π and a randozed workload assgnent W, we have a randozed uploadng schee π W, whch satsfes: E(Cost (π W ) + Cost (π W )) ax Λ M (x)(cost x (π) + Cost (π)) Proof: Snce the traffc pattern n ISP (, r), r s exactly the sae as ISP, we only consder one stage. Let us consder schee π frst. In the frst stage, the cost s: Cost (π) = ax,w (v,w) α ax M Σ M (v α,w) where v,w ndcates the transfer speed n ISP durng [t, t + ) for workload w. Σ M (vα,w ) s the su of the largest M values of v,w α when gven. The nequalty holds because there are at ost M non-zero speeds for any gven duraton [t, t + ). We next have the cost of the second stage: Cost (π) = ax,w (βzrv,w) α r = β α zr α ax,w (v,w) α r β α zr α ax Σ M (v,w) α r The cost of the frst stage n π W s: E(Cost (π W )) = φ n(ω W ) ax( M Ω W W = M ax )( φ n(ω W Ω W W w Ω W w Ω W v,w) α v,w) α The second equalty above holds because the assgnent s unforly rando. Slarly, The cost of the second stage n π W s: E(Cost (π W )) = M Ω W = M β α r φ n(ω W P z α r ax ) r ax(z r φ n(ω W Ω W W w Ω W )( w Ω W βv,w) α v,w) α gan because for any [t, t + ), there are at ost M values of v,w. We have M Ω W = M Ω W W φ n(ω W Σ M (vα,w ) = Λ n (v) = Λ M (v ) )( w Ω W v,w) α W φ n(ω W )( w Ω v W,w ) α n w= (vα,w ) where v s an M -densonal subvector of v R n \ {}, whch contans all non-zero transfer speeds n [t, t + ). Therefore, the rato for the frst stage s: E(Cost (π W )) Cost (π) M Ω W W φ(ωw ) ax ( w Ω W ax Σ M (vα,w ) M Ω W W φ(ωw )( w Ω W Σ M (vα,w ) ax Λ M (x) x v,w) α v,w)α where = arg ax ( w Ω v,w) α. Slarly, the rato W for the second stage s also bounded by ax x Λ M (x),.e., E(Cost (π W )) Cost (π) ax x Λ M (x). Ths proves Lea. Let S(α, j) be the j-th Strlng nuber for α eleents, defned as the nuber of parttons of a set of sze α nto j subsets [4]. Let B α be the α-th Bell nuber, defned as the nuber of parttons of a set of sze α [4]. The Bell nuber s relatvely sall when α s sall: B =, B =, B 3 = 5, B 4 = 5. The defntons also ply: α S(α, j) = B α j The followng lea s proven by Grener et al. [6]. Lea. [6] α N and α M, ax x Λ M (x) = M! S(α, j) α j= M j ( M j)!. Theore 6. Gven a λ-copettve algorth wth respect to cost for the sngle ISP case, then the randozed onlne algorth s λb α -copettve n expectaton. Proof: Let π be the optal uploadng schee, the correspondng randozed uploadng schee s πw. The algorth we use s π W. Snce the workloads n πw and π W are the sae, we have: E(Cost (π W )) λe(cost (π W )) () snce the algorth s λ-copettve. Slarly, E(Cost (π W )) λe(cost (π W )) () snce the traffc pattern n ISP (, r), r s exactly the sae as n ISP. Lea ples: E(Cost (π W ) + Cost (π W )) ax x Λ M (x)(cost (π ) + Cost (π )) (3) Snce Λ M (x) s a onotoncally ncreasng functon of α, we use α as an upper bound of α >, obtanng a correspondng upper bound of Λ M (x). Cobnng Eqn. () () and (3) as well as Lea, we have the followng expected cost of the randozed onlne algorth:

8 E(Cost (π W ) + Cost (π W )) λe(cost (π W ) + Cost (π W )) λ ax x Λ M (x)(cost (π ) + Cost (π )) α M! = λ S( α, j) M j ( M j)! (Cost(π ) + Cost (π )) j= α λ S( α, j)(cost (π ) + Cost (π )) j= λb α OP T Reark: For a sngle lnk, we can eploy Heurstc Soothng, whose copettve rato s saller than wth respect to axu traffc volue. Then the copettve rato of lgorth s α n cost. Thus lgorth 3 s α B α - copettve n expectaton. When α =, the copettve rato s 8, a constant regardless of the nuber of appers. VI. PERFORMNCE EVLUTION We have pleented Sple Soothng, Heurstc Soothng, as well as the randozed onlne algorth, for perforance evaluaton through sulaton studes. The default nput W t s generated unforly at rando, as shown n Fg., where all data are noralzed,.e., scaled down by ax t W t. We assue there are 5 appers at dfferent locatons, and 5 reducers at dfferent locatons. We choose α =, thus the charge functon f (x) = f,r (x) = x.. The Sngle ISP Case Frst we copare Heurstc Soothng wth Sple Soothng. The two algorths are executed under a delay requreent D = 5. Fg. 3 llustrates the traffc volue scheduled at each te slot. Copared wth Sple Soothng, Heurstc Soothng results n a axu traffc volue ths s about 8% saller. Heurstc Soothng tres to explot the avalable delay to average the traffc and s less senstve to the fluctuaton of traffc deand, as copared wth Sple Soothng. For exaple, at around t =, the traffc of Sple Soothng ncreases abruptly due to hgh traffc deand n the nput; around t = 4, t goes down due to low traffc deand. In coparson, Heurstc Soothng results n ore even traffc dstrbutons around t = and t = 4. Next we exane how the tolerable delay affects the perforance of the proposed onlne algorths. We execute Sple Soothng, Heurstc Soothng and IT aganst a varety of delays rangng fro D = to D = 4. We also copute the offlne optu as a benchark. The observed copettve ratos are shown n Fg. 4. The results suggest that both Sple Soothng and Heurstc Soothng perfor uch better than IT. Heurstc Soothng also beats Sple Soothng, by a saller argn. Heurstc Soothng approaches the offlne optu rather closely; the observed copettve ratos are always below.5 and usually around., uch better than the theoretcally proven upper bound n Theore 4. Heurstc Soothng s further evaluated under other rando nputs, ncludng Posson dstrbuton n Fg. 5, Gaussan dstrbuton n Fg. 6 and a specfcally desgned rando nput n Fg. 7. ll results verfy that Heurstc Soothng works best aong the three onlne cost nzaton algorths. B. The Cloud Scenaro We pleented the randozed algorth n lgorth 3 and the natve algorth n Sec. V-. They are evaluated under three types of nputs: unfor dstrbuton, Posson dstrbuton and Gaussan dstrbuton. We copare the costs of the two algorths usng these nputs, as shown n Fg. 8, Fg. 9 and Fg., respectvely. We observe that the randozed algorth acheves uch lower cost than the natve algorth, n partcular wth longer tolerable delays. For exaple, Fg. 8 shows that the randozed algorth saves approxately 45% cost as copared wth the natve algorth when D = 5, and t saves ore than 68% when D =. Ths suggests that longer tolerable delays provde the randozed algorth ore space of aneuver, leadng to ore evdent cost reduce. We further nvestgate the nfluence of β, the rato of orgnal data sze to the nteredate data sze. Results are shown n Fg.. When D s sall, a large β causes a rather hgh cost. However when a large D s used, e.g., D = 4, even a large β only produces a relatvely sall cost. Noralze Cost β Fg.. Relatonshp between traffc cost and paraeters D, β. VII. CONCLUSION ISPs now charge bg data applcatons wth a new, nterestng percentle based odel, leadng to new onlne algorth desgn probles for nzng the traffc cost pad for uploadng bg data to the cloud. We studed two scenaros for such onlne algorth desgn n ths work. Heurstc Soothng algorth s proposed n the sngle lnk case, wth proven better perforance than the best alternatve n the lterature, and a saller copettve rato below. randozed onlne algorth s desgned for the MapReduce fraework, achevng a constant copettve rato by eployng Heurstc Soothng as a buldng odule. We have focused on MX charge rules, and leave slar onlne algorth desgn for 95-percentle charge rules as future work. REFERENCES [] azon Elastc Copute Cloud, [] Lnode, [3] azon EC Case-studes,

9 Noralzed Data Traffc.6.4 Unfor Input Te Noralzed Scheduled Traffc.6.4 Sple Soothng Heurstc Soothng Te Copettve Rato IT Sple Soothng Heurstc Soothng Copettve Rato.5.5 Fg.. Unforly Rando Input. IT Sple Soothng Heurstc Soothng Fg. 5. Copettve rato over varous delay wndow szes under nput of Posson dstrbuton. Fg. 3. Sple Soothng vs. Heurstc Soothng, D = Copettve Rato IT Sple Soothng Heurstc Soothng Fg. 6. Copettve rato over varous delay wndow szes under nput of Gaussan dstrbuton. Fg. 4. Copettve rato over varous delay wndow szes under nput of unfor dstrbuton. Copettve Rato IT Sple Soothng Heurstc Soothng Fg. 7. Copettve rato over varous delay wndow szes under a specfcally desgned nput. Randozed lgorth Natve lgorth Randozed lgorth Natve lgorth Randozed lgorth Natve lgorth Noralze Cost.6.4 Noralze Cost.6.4 Noralze Cost Fg. 8. Coparson between the proposed randozed algorth and the natve algorth under nput of unfor dstrbuton and β =. Fg. 9. Coparson between the proposed randozed algorth and the natve algorth under nput of Posson dstrbuton and β =. Fg.. Coparson between the proposed randozed algorth and the natve algorth under nput of Gaussan dstrbuton and β =. [4] E. E. Schadt, M. D. Lnderan, J. Sorenson, L. Lee, and G. P. Nolan, Coputatonal Solutons to Large-scale Data Manageent and nalyss, Nat Rev Genet, vol., no. 9, pp , Sep.. [5] L. Golubchk, S. Khuller, K. Mukherjee, and Y. Yao, To Send or not to Send: Reducng the Cost of Data Transsson, n Proc. of IEEE INFOCOM, 3. [6] L. Zhang, C. Wu, Z. L, C. Guo, M. Chen, and F. Lau, Movng Bg Data to The Cloud: n Onlne Cost-Mnzng pproach, IEEE Journal on Selected reas n Councatons, vol. 3, no., pp. 7 7, 3. [7] H. Wang, H. Xe, L. Qu,. Slberschatz, and Y. Yang, Optal ISP Subscrpton for Internet Multhong: lgorth Desgn and Iplcaton nalyss, n Proc. of IEEE INFOCOM, 5. [8] S. Peak, Beyond Bandwdth: The Busness Case For Data cceleraton, Whte Paper, 3. [9] D. K. Goldenberg, L. Quy, H. Xe, Y. R. Yang, and Y. Zhang, Optzng Cost and Perforance for Multhong, n Proc. of CM SIGCOMM, 4. []. Grothey and X. Yang, Top-percentle Traffc Routng Proble by Dynac Prograng, Optzaton and Engneerng, vol., pp ,. [] F. Yao,. Deers, and S. Shenker, Schedulng Model for Reduced CPU Energy, n Proc. of IEEE FOCS, 995. [] N. Bansal, T. Kbrel, and K. Pruhs, Speed Scalng to Manage Energy and Teperature, J. CM, vol. 54, no., pp. 3: 3:39, Mar. 7. [3] S. lbers, F. Müller, and S. Schelzer, Speed Scalng on Parallel Processors, n Proc. of CM SP, 7. [4] B. Bngha and M. Greenstreet, Energy Optal Schedulng on Multprocessors wth Mgraton, n Proc. of IEEE ISP, 8. [5] E. ngel, E. Baps, F. Kace, and D. Letsos, Speed Scalng on Parallel Processors wth Mgraton, n Euro-Par Parallel Processng, ser. Lecture Notes n Coputer Scence, C. Kaklaans, T. Papatheodorou, and P. Spraks, Eds. Sprnger Berln Hedelberg,, vol. 7484, pp [6] G. Grener, T. Nonner, and. Souza, The Bell s Rngng n Speedscaled Multprocessor Schedulng, n Proc. of CM SP, 9. [7] M.. dnan, Y. Ma, R. Sughara, and R. Gupta, Dynac Deferral of Workload for Capacty Provsonng n Data Centers, [8] Y. Yao, L. Huang,. Shara, L. Golubchk, and M. Neely, Data Centers Power Reducton: Two Te Scale pproach for Delay Tolerant Workloads, n Proc. of IEEE INFOCOM,. [9] B. Cho and I. Gupta, New lgorths for Plannng Bulk Transfer va Internet and Shppng Networks, n Proc. of IEEE ICDCS,. [] M. dler, R. K. Staraan, and H. Venkataraan, lgorths for Optzng the Bandwdth Cost of Content Delvery, Coput. Netw., vol. 55, no. 8, pp. 47 4, Dec.. [] J. Dean and S. Gheawat, MapReduce: Splfed Data Processng on Large Clusters, Coun. CM, vol. 5, no., pp. 7 3, Jan. 8. [] S. Rao, R. Raakrshnan,. Slbersten, M. Ovsannkov, and D. Reeves, Salfsh: Fraework for Large Scale Data Processng, Yahoo!Labs, Tech. Rep.,. [3] B. Hentz,. Chandra, and R. K. Staraan, Optzng MapReduce for Hghly Dstrbuted Envronents, Departent of Coputer Scence and Engneerng, Unversty of Mnnesota, Tech. Rep.,. [4] H. Becker and J. Rordan, The rthetc of Bell and Strlng nubers, ercan journal of Matheatcs, vol. 7, no., pp , 948.

Stochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters

Stochastc Models of Load Balancng and Schedulng n Cloud Coputng Clusters Sva Theja Magulur and R. Srkant Departent of ECE and CSL Unversty of Illnos at Urbana-Chapagn sva.theja@gal.co; rsrkant@llnos.edu

Stochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters

Stochastc Models of Load Balancng and Schedulng n Cloud Coputng Clusters Sva Theja Magulur and R. Srkant Departent of ECE and CSL Unversty of Illnos at Urbana-Chapagn sva.theja@gal.co; rsrkant@llnos.edu

Stochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters

01 Proceedngs IEEE INFOCOM Stochastc Models of Load Balancng and Schedulng n Cloud Coputng Clusters Sva heja Magulur and R. Srkant Departent of ECE and CSL Unversty of Illnos at Urbana-Chapagn sva.theja@gal.co;

An Electricity Trade Model for Microgrid Communities in Smart Grid

An Electrcty Trade Model for Mcrogrd Countes n Sart Grd Tansong Cu, Yanzh Wang, Shahn Nazaran and Massoud Pedra Unversty of Southern Calforna Departent of Electrcal Engneerng Los Angeles, CA, USA {tcu,

Basic Queueing Theory M/M/* Queues. Introduction

Basc Queueng Theory M/M/* Queues These sldes are created by Dr. Yh Huang of George Mason Unversty. Students regstered n Dr. Huang's courses at GMU can ake a sngle achne-readable copy and prnt a sngle copy

Near Optimal Online Algorithms and Fast Approximation Algorithms for Resource Allocation Problems

Near Optal Onlne Algorths and Fast Approxaton Algorths for Resource Allocaton Probles Nkhl R Devanur Kaal Jan Balasubraanan Svan Chrstopher A Wlkens Abstract We present algorths for a class of resource

BANDWIDTH ALLOCATION AND PRICING PROBLEM FOR A DUOPOLY MARKET

Yugoslav Journal of Operatons Research (0), Nuber, 65-78 DOI: 0.98/YJOR0065Y BANDWIDTH ALLOCATION AND PRICING PROBLEM FOR A DUOPOLY MARKET Peng-Sheng YOU Graduate Insttute of Marketng and Logstcs/Transportaton,

Fault tolerance in cloud technologies presented as a service

Internatonal Scentfc Conference Computer Scence 2015 Pavel Dzhunev, PhD student Fault tolerance n cloud technologes presented as a servce INTRODUCTION Improvements n technques for vrtualzaton and performance

Virtual machine resource allocation algorithm in cloud environment

COMPUTE MOELLIN & NEW TECHNOLOIES 2014 1(11) 279-24 Le Zheng Vrtual achne resource allocaton algorth n cloud envronent 1, 2 Le Zheng 1 School of Inforaton Engneerng, Shandong Youth Unversty of Poltcal

How Much to Bet on Video Poker

How Much to Bet on Vdeo Poker Trstan Barnett A queston that arses whenever a gae s favorable to the player s how uch to wager on each event? Whle conservatve play (or nu bet nzes large fluctuatons, t lacks

Revenue Maximization Using Adaptive Resource Provisioning in Cloud Computing Environments

202 ACM/EEE 3th nternatonal Conference on Grd Coputng evenue Maxzaton sng Adaptve esource Provsonng n Cloud Coputng Envronents Guofu Feng School of nforaton Scence, Nanng Audt nversty, Nanng, Chna nufgf@gal.co

An Analytical Model of Web Server Load Distribution by Applying a Minimum Entropy Strategy

Internatonal Journal of Coputer and Councaton Engneerng, Vol. 2, No. 4, July 203 An Analytcal odel of Web Server Load Dstrbuton by Applyng a nu Entropy Strategy Teeranan Nandhakwang, Settapong alsuwan,

Capacity Planning for Virtualized Servers

Capacty Plannng for Vrtualzed Servers Martn Bchler, Thoas Setzer, Benjan Spetkap Departent of Inforatcs, TU München 85748 Garchng/Munch, Gerany (bchler setzer benjan.spetkap)@n.tu.de Abstract Today's data

A Statistical Model for Detecting Abnormality in Static-Priority Scheduling Networks with Differentiated Services

A Statstcal odel for Detectng Abnoralty n Statc-Prorty Schedulng Networks wth Dfferentated Servces ng L 1 and We Zhao 1 School of Inforaton Scence & Technology, East Chna Noral Unversty, Shangha 0006,

An Alternative Way to Measure Private Equity Performance

An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate

On Monitoring of End-to-End Packet Reordering over the Internet

On Montorng of End-to-End Packet Reorderng over the Internet Bn Ye 1 Anura P. Jayasuana 1 Nschal M. Pratla 2 1Coputer Networkng Research laboratory, Colorado State Unversty, Fort Collns, CO 8523, USA 2

A R T I C L E S DYNAMIC VEHICLE DISPATCHING: OPTIMAL HEAVY TRAFFIC PERFORMANCE AND PRACTICAL INSIGHTS

A R T I C L E S DYAMIC VEHICLE DISPATCHIG: OPTIMAL HEAVY TRAFFIC PERFORMACE AD PRACTICAL ISIGHTS OAH GAS OPIM Departent, The Wharton School, Unversty of Pennsylvana, Phladelpha, Pennsylvana 19104-6366

Inventory Control in a Multi-Supplier System

3th Intl Workng Senar on Producton Econocs (WSPE), Igls, Autrche, pp.5-6 Inventory Control n a Mult-Suppler Syste Yasen Arda and Jean-Claude Hennet LAAS-CRS, 7 Avenue du Colonel Roche, 3077 Toulouse Cedex

CONSTRUCTION OF A COLLABORATIVE VALUE CHAIN IN CLOUD COMPUTING ENVIRONMENT

CONSTRUCTION OF A COLLAORATIVE VALUE CHAIN IN CLOUD COMPUTING ENVIRONMENT Png Wang, School of Econoy and Manageent, Jangsu Unversty of Scence and Technology, Zhenjang Jangsu Chna, sdwangp1975@163.co Zhyng

The Packing Server for Real-Time Scheduling of MapReduce Workflows

The Packng Server for Real-Te Schedulng of MapReduce Workflows Shen L, Shaohan Hu, Tarek Abdelzaher Unversty of Illnos at Urbana-Chapagn {shenl3, shu7, zaher}@llnos.edu Abstract Ths paper develops new

A Fuzzy Optimization Framework for COTS Products Selection of Modular Software Systems

Internatonal Journal of Fuy Systes, Vol. 5, No., June 0 9 A Fuy Optaton Fraework for COTS Products Selecton of Modular Software Systes Pankaj Gupta, Hoang Pha, Mukesh Kuar Mehlawat, and Shlp Vera Abstract

Dynamic Resource Allocation in Clouds: Smart Placement with Live Migration

Dynac Resource Allocaton n Clouds: Sart Placeent wth Lve Mgraton Mahlouf Had Ingéneur de Recherche ahlouf.had@rt-systex.fr Avec : Daal Zeghlache (TSP) daal.zeghlache@teleco-sudpars.eu FONDATION DE COOPERATION

DEFINING %COMPLETE IN MICROSOFT PROJECT

CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,

Scan Detection in High-Speed Networks Based on Optimal Dynamic Bit Sharing

Scan Detecton n Hgh-Speed Networks Based on Optal Dynac Bt Sharng Tao L Shgang Chen Wen Luo Mng Zhang Departent of Coputer & Inforaton Scence & Engneerng, Unversty of Florda Abstract Scan detecton s one

Research Article Load Balancing for Future Internet: An Approach Based on Game Theory

Appled Matheatcs, Artcle ID 959782, 11 pages http://dx.do.org/10.1155/2014/959782 Research Artcle Load Balancng for Future Internet: An Approach Based on Gae Theory Shaoy Song, Tngje Lv, and Xa Chen School

Naglaa Raga Said Assistant Professor of Operations. Egypt.

Volue, Issue, Deceer ISSN: 77 8X Internatonal Journal of Adanced Research n Coputer Scence and Software Engneerng Research Paper Aalale onlne at: www.jarcsse.co Optal Control Theory Approach to Sole Constraned

Schedulability Bound of Weighted Round Robin Schedulers for Hard Real-Time Systems

Schedulablty Bound of Weghted Round Robn Schedulers for Hard Real-Tme Systems Janja Wu, Jyh-Charn Lu, and We Zhao Department of Computer Scence, Texas A&M Unversty {janjaw, lu, zhao}@cs.tamu.edu Abstract

Luby s Alg. for Maximal Independent Sets using Pairwise Independence

Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..

Ganesh Subramaniam. American Solutions Inc., 100 Commerce Dr Suite # 103, Newark, DE 19713, USA

238 Int. J. Sulaton and Process Modellng, Vol. 3, No. 4, 2007 Sulaton-based optsaton for ateral dspatchng n Vendor-Managed Inventory systes Ganesh Subraana Aercan Solutons Inc., 100 Coerce Dr Sute # 103,

Project Networks With Mixed-Time Constraints

Project Networs Wth Mxed-Tme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa

Near Optimal Online Algorithms and Fast Approximation Algorithms for Resource Allocation Problems

Near Optal Onlne Algorths and Fast Approxaton Algorths for Resource Allocaton Probles ABSTRACT Nhl R Devanur Mcrosoft Research Redond WA USA ndev@crosoftco Balasubraanan Svan Coputer Scences Dept Unv of

Yixin Jiang and Chuang Lin. Minghui Shi and Xuemin Sherman Shen*

198 Int J Securty Networks Vol 1 Nos 3/4 2006 A self-encrypton authentcaton protocol for teleconference servces Yxn Jang huang Ln Departent of oputer Scence Technology Tsnghua Unversty Beng hna E-al: yxang@csnet1cstsnghuaeducn

A Novel Dynamic Role-Based Access Control Scheme in User Hierarchy

Journal of Coputatonal Inforaton Systes 6:7(200) 2423-2430 Avalable at http://www.jofcs.co A Novel Dynac Role-Based Access Control Schee n User Herarchy Xuxa TIAN, Zhongqn BI, Janpng XU, Dang LIU School

The OC Curve of Attribute Acceptance Plans

The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4

1 Approximation Algorithms

CME 305: Dscrete Mathematcs and Algorthms 1 Approxmaton Algorthms In lght of the apparent ntractablty of the problems we beleve not to le n P, t makes sense to pursue deas other than complete solutons

Maximizing profit using recommender systems

Maxzng proft usng recoender systes Aparna Das Brown Unversty rovdence, RI aparna@cs.brown.edu Clare Matheu Brown Unversty rovdence, RI clare@cs.brown.edu Danel Rcketts Brown Unversty rovdence, RI danel.bore.rcketts@gal.co

INTRODUCTION TO MERGERS AND ACQUISITIONS: FIRM DIVERSIFICATION

XV. INTODUCTION TO MEGES AND ACQUISITIONS: FIM DIVESIFICATION In the ntroducton to Secton VII, t was noted that frs can acqure assets by ether undertakng nternally-generated new projects or by acqurng

Graph Theory and Cayley s Formula

Graph Theory and Cayley s Formula Chad Casarotto August 10, 2006 Contents 1 Introducton 1 2 Bascs and Defntons 1 Cayley s Formula 4 4 Prüfer Encodng A Forest of Trees 7 1 Introducton In ths paper, I wll

Two-Phase Traceback of DDoS Attacks with Overlay Network

4th Internatonal Conference on Sensors, Measureent and Intellgent Materals (ICSMIM 205) Two-Phase Traceback of DDoS Attacks wth Overlay Network Zahong Zhou, a, Jang Wang2, b and X Chen3, c -2 School of

Communication Networks II Contents

8 / 1 -- Communcaton Networs II (Görg) -- www.comnets.un-bremen.de Communcaton Networs II Contents 1 Fundamentals of probablty theory 2 Traffc n communcaton networs 3 Stochastc & Marovan Processes (SP

9.1 The Cumulative Sum Control Chart

Learnng Objectves 9.1 The Cumulatve Sum Control Chart 9.1.1 Basc Prncples: Cusum Control Chart for Montorng the Process Mean If s the target for the process mean, then the cumulatve sum control chart s

J. Parallel Distrib. Comput.

J. Parallel Dstrb. Comput. 71 (2011) 62 76 Contents lsts avalable at ScenceDrect J. Parallel Dstrb. Comput. journal homepage: www.elsever.com/locate/jpdc Optmzng server placement n dstrbuted systems n

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence

1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh

II. THE QUALITY AND REGULATION OF THE DISTRIBUTION COMPANIES I. INTRODUCTION

Fronter Methodology to fx Qualty goals n Electrcal Energy Dstrbuton Copanes R. Rarez 1, A. Sudrà 2, A. Super 3, J.Bergas 4, R.Vllafáfla 5 1-2 -3-4-5 - CITCEA - UPC UPC., Unversdad Poltécnca de Cataluña,

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

Web Service-based Business Process Automation Using Matching Algorithms

Web Servce-based Busness Process Autoaton Usng Matchng Algorths Yanggon K and Juhnyoung Lee 2 Coputer and Inforaton Scences, Towson Uversty, Towson, MD 2252, USA, yk@towson.edu 2 IBM T. J. Watson Research

Enabling P2P One-view Multi-party Video Conferencing

Enablng P2P One-vew Mult-party Vdeo Conferencng Yongxang Zhao, Yong Lu, Changja Chen, and JanYn Zhang Abstract Mult-Party Vdeo Conferencng (MPVC) facltates realtme group nteracton between users. Whle P2P

Lecture 3: Force of Interest, Real Interest Rate, Annuity

Lecture 3: Force of Interest, Real Interest Rate, Annuty Goals: Study contnuous compoundng and force of nterest Dscuss real nterest rate Learn annuty-mmedate, and ts present value Study annuty-due, and

PAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of Illinois-Urbana Champaign

PAS: A Packet Accountng System to Lmt the Effects of DoS & DDoS Debsh Fesehaye & Klara Naherstedt Unversty of Illnos-Urbana Champagn DoS and DDoS DDoS attacks are ncreasng threats to our dgtal world. Exstng

A Multi Due Date Batch Scheduling Model. on Dynamic Flow Shop to Minimize. Total Production Cost

Conteporary Enneern Scences, Vol. 9, 2016, no. 7, 315-324 HIKARI Ltd, www.-hkar.co http://dx.do.or/10.12988/ces.2016.617 A Mult Due Date Batch Scheduln Model on Dynac Flow Shop to Mnze Total Producton

Recurrence. 1 Definitions and main statements

Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.

International Journal of Industrial Engineering Computations

Internatonal Journal of Industral ngneerng Coputatons 3 (2012) 393 402 Contents lsts avalable at GrowngScence Internatonal Journal of Industral ngneerng Coputatons hoepage: www.growngscence.co/jec Suppler

PRIOR ROBUST OPTIMIZATION. Balasubramanian Sivan. A dissertation submitted in partial fulfillment of the requirements for the degree of

PRIOR ROBUST OPTIMIZATION By Balasubraanan Svan A dssertaton subtted n partal fulfllent of the requreents for the degree of Doctor of Phlosophy (Coputer Scences) at the UNIVERSITY OF WISCONSIN MADISON

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression

Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy S-curve Regresson Cheng-Wu Chen, Morrs H. L. Wang and Tng-Ya Hseh Department of Cvl Engneerng, Natonal Central Unversty,

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic

Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek

HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo

Multi-Source Video Multicast in Peer-to-Peer Networks

ult-source Vdeo ultcast n Peer-to-Peer Networks Francsco de Asís López-Fuentes*, Eckehard Stenbach Technsche Unverstät ünchen Insttute of Communcaton Networks, eda Technology Group 80333 ünchen, Germany

Analysis of Clock Synchronization Approaches for Residential Ethernet

Analyss of Clock Synchronzaton Approaches for Resdental Ethernet Geoffrey M. Garner (Consultant) Kees den Hollander SAIT, Sasung Electroncs ggarner@cocast.net, denhollander.c.@sasung.co Abstract Resdental

Secure Cloud Storage Service with An Efficient DOKS Protocol

Secure Cloud Storage Servce wth An Effcent DOKS Protocol ZhengTao Jang Councaton Unversty of Chna z.t.ang@163.co Abstract Storage servces based on publc clouds provde custoers wth elastc storage and on-deand

STATE HIGHWAY ADMINISTRATION RESEARCH REPORT ENHANCEMENT OF FREEWAY INCIDENT TRAFFIC MANAGEMENT AND RESULTING BENEFITS

MD-11- SP009B4Q STATE HIGHWAY ADMINISTRATION RESEARCH REPORT ENHANCEMENT OF FREEWAY INCIDENT TRAFFIC MANAGEMENT AND RESULTING BENEFITS WOON KIM AND MARK FRANZ GANG-LEN CHANG DEPARTMENT OF CIVIL AND ENVIRONMENTAL

Performance Analysis of Energy Consumption of Smartphone Running Mobile Hotspot Application

Internatonal Journal of mart Grd and lean Energy Performance Analyss of Energy onsumpton of martphone Runnng Moble Hotspot Applcaton Yun on hung a chool of Electronc Engneerng, oongsl Unversty, 511 angdo-dong,

Robust Design of Public Storage Warehouses. Yeming (Yale) Gong EMLYON Business School

Robust Desgn of Publc Storage Warehouses Yemng (Yale) Gong EMLYON Busness School Rene de Koster Rotterdam school of management, Erasmus Unversty Abstract We apply robust optmzaton and revenue management

Quality of Service Analysis and Control for Wireless Sensor Networks

Qualty of ervce Analyss and Control for Wreless ensor Networs Jaes Kay and Jeff Frol Unversty of Veront ay@uv.edu, frol@eba.uv.edu Abstract hs paper nvestgates wreless sensor networ spatal resoluton as

Efficient On-Demand Data Service Delivery to High-Speed Trains in Cellular/Infostation Integrated Networks

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. XX, NO. XX, MONTH 2XX 1 Effcent On-Demand Data Servce Delvery to Hgh-Speed Trans n Cellular/Infostaton Integrated Networks Hao Lang, Student Member,

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

An Enhanced K-Anonymity Model against Homogeneity Attack

JOURNAL OF SOFTWARE, VOL. 6, NO. 10, OCTOBER 011 1945 An Enhanced K-Anont Model aganst Hoogenet Attack Qan Wang College of Coputer Scence of Chongqng Unverst, Chongqng, Chna Eal: wangqan@cqu.edu.cn Zhwe

Calculation of Sampling Weights

Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample

Cloud-based Social Application Deployment using Local Processing and Global Distribution

Cloud-based Socal Applcaton Deployment usng Local Processng and Global Dstrbuton Zh Wang *, Baochun L, Lfeng Sun *, and Shqang Yang * * Bejng Key Laboratory of Networked Multmeda Department of Computer

CLoud computing technologies have enabled rapid

1 Cost-Mnmzng Dynamc Mgraton of Content Dstrbuton Servces nto Hybrd Clouds Xuana Qu, Hongxng L, Chuan Wu, Zongpeng L and Francs C.M. Lau Department of Computer Scence, The Unversty of Hong Kong, Hong Kong,

J. Parallel Distrib. Comput. Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers

J. Parallel Dstrb. Comput. 71 (2011) 732 749 Contents lsts avalable at ScenceDrect J. Parallel Dstrb. Comput. ournal homepage: www.elsever.com/locate/pdc Envronment-conscous schedulng of HPC applcatons

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12

14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed

A Hybrid Approach to Evaluate the Performance of Engineering Schools

A Hybrd Approach to Evaluate the Perforance of Engneerng Schools School of Engneerng Unversty of Brdgeport Brdgeport, CT 06604 ABSTRACT Scence and engneerng (S&E) are two dscplnes that are hghly receptve

I. SCOPE, APPLICABILITY AND PARAMETERS Scope

D Executve Board Annex 9 Page A/R ethodologcal Tool alculaton of the number of sample plots for measurements wthn A/R D project actvtes (Verson 0) I. SOPE, PIABIITY AD PARAETERS Scope. Ths tool s applcable

Technical Report, SFB 475: Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund, No. 1998,04

econstor www.econstor.eu Der Open-Access-Publkatonsserver der ZBW Lebnz-Inforatonszentru Wrtschaft The Open Access Publcaton Server of the ZBW Lebnz Inforaton Centre for Econocs Becka, Mchael Workng Paper

An MILP model for planning of batch plants operating in a campaign-mode

An MILP model for plannng of batch plants operatng n a campagn-mode Yanna Fumero Insttuto de Desarrollo y Dseño CONICET UTN yfumero@santafe-concet.gov.ar Gabrela Corsano Insttuto de Desarrollo y Dseño

NON-CONSTANT SUM RED-AND-BLACK GAMES WITH BET-DEPENDENT WIN PROBABILITY FUNCTION LAURA PONTIGGIA, University of the Sciences in Philadelphia

To appear n Journal o Appled Probablty June 2007 O-COSTAT SUM RED-AD-BLACK GAMES WITH BET-DEPEDET WI PROBABILITY FUCTIO LAURA POTIGGIA, Unversty o the Scences n Phladelpha Abstract In ths paper we nvestgate

On the Interaction between Load Balancing and Speed Scaling

On the Interacton between Load Balancng and Speed Scalng Ljun Chen and Na L Abstract Speed scalng has been wdely adopted n computer and communcaton systems, n partcular, to reduce energy consumpton. An

Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts

Power-of-wo Polces for Sngle- Warehouse Mult-Retaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)

Extending Probabilistic Dynamic Epistemic Logic

Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σ-algebra: a set

A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION. Michael E. Kuhl Radhamés A. Tolentino-Peña

Proceedngs of the 2008 Wnter Smulaton Conference S. J. Mason, R. R. Hll, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION

A Secure Password-Authenticated Key Agreement Using Smart Cards

A Secure Password-Authentcated Key Agreement Usng Smart Cards Ka Chan 1, Wen-Chung Kuo 2 and Jn-Chou Cheng 3 1 Department of Computer and Informaton Scence, R.O.C. Mltary Academy, Kaohsung 83059, Tawan,

Nasdaq Iceland Bond Indices 01 April 2015

Nasdaq Iceland Bond Indces 01 Aprl 2015 -Fxed duraton Indces Introducton Nasdaq Iceland (the Exchange) began calculatng ts current bond ndces n the begnnng of 2005. They were a response to recent changes

Real-Time Process Scheduling

Real-Tme Process Schedulng ktw@cse.ntu.edu.tw (Real-Tme and Embedded Systems Laboratory) Independent Process Schedulng Processes share nothng but CPU Papers for dscussons: C.L. Lu and James. W. Layland,

Time Series Analysis in Studies of AGN Variability. Bradley M. Peterson The Ohio State University

Tme Seres Analyss n Studes of AGN Varablty Bradley M. Peterson The Oho State Unversty 1 Lnear Correlaton Degree to whch two parameters are lnearly correlated can be expressed n terms of the lnear correlaton

Lesson 2 Chapter Two Three Phase Uncontrolled Rectifier

Lesson 2 Chapter Two Three Phase Uncontrolled Rectfer. Operatng prncple of three phase half wave uncontrolled rectfer The half wave uncontrolled converter s the smplest of all three phase rectfer topologes.

Multiple-Period Attribution: Residuals and Compounding

Multple-Perod Attrbuton: Resduals and Compoundng Our revewer gave these authors full marks for dealng wth an ssue that performance measurers and vendors often regard as propretary nformaton. In 1994, Dens

Analysis of Energy-Conserving Access Protocols for Wireless Identification Networks

From the Proceedngs of Internatonal Conference on Telecommuncaton Systems (ITC-97), March 2-23, 1997. 1 Analyss of Energy-Conservng Access Protocols for Wreless Identfcaton etworks Imrch Chlamtac a, Chara

A Note on the Decomposition of a Random Sample Size

A Note on the Decomposton of a Random Sample Sze Klaus Th. Hess Insttut für Mathematsche Stochastk Technsche Unverstät Dresden Abstract Ths note addresses some results of Hess 2000) on the decomposton

Joint Scheduling of Processing and Shuffle Phases in MapReduce Systems

Jont Schedulng of Processng and Shuffle Phases n MapReduce Systems Fangfe Chen, Mural Kodalam, T. V. Lakshman Department of Computer Scence and Engneerng, The Penn State Unversty Bell Laboratores, Alcatel-Lucent

Chapter 7. Random-Variate Generation 7.1. Prof. Dr. Mesut Güneş Ch. 7 Random-Variate Generation

Chapter 7 Random-Varate Generaton 7. Contents Inverse-transform Technque Acceptance-Rejecton Technque Specal Propertes 7. Purpose & Overvew Develop understandng of generatng samples from a specfed dstrbuton

Survey on Virtual Machine Placement Techniques in Cloud Computing Environment

Survey on Vrtual Machne Placement Technques n Cloud Computng Envronment Rajeev Kumar Gupta and R. K. Paterya Department of Computer Scence & Engneerng, MANIT, Bhopal, Inda ABSTRACT In tradtonal data center

What is Candidate Sampling

What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble

Data Broadcast on a Multi-System Heterogeneous Overlayed Wireless Network *

JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 24, 819-840 (2008) Data Broadcast on a Mult-System Heterogeneous Overlayed Wreless Network * Department of Computer Scence Natonal Chao Tung Unversty Hsnchu,

Traffic Demand Forecasting for EGCS with Grey Theory Based Multi- Model Method

IJCSI Internatonal Journal of Coputer Scence Issues, Vol., Issue, No, January 3 ISSN (Prnt): 694-784 ISSN (Onlne): 694-84 www.ijcsi.org 6 Traffc Deand Forecastng for EGCS wth Grey Theory Based Mult- Model

Multivariate EWMA Control Chart

Multvarate EWMA Control Chart Summary The Multvarate EWMA Control Chart procedure creates control charts for two or more numerc varables. Examnng the varables n a multvarate sense s extremely mportant

Description of the Force Method Procedure. Indeterminate Analysis Force Method 1. Force Method con t. Force Method con t

Indeternate Analyss Force Method The force (flexblty) ethod expresses the relatonshps between dsplaceents and forces that exst n a structure. Prary objectve of the force ethod s to deterne the chosen set

State function: eigenfunctions of hermitian operators-> normalization, orthogonality completeness

Schroednger equaton Basc postulates of quantum mechancs. Operators: Hermtan operators, commutators State functon: egenfunctons of hermtan operators-> normalzaton, orthogonalty completeness egenvalues and