Auditing Cloud Service Level Agreement on VM CPU Speed

Size: px
Start display at page:

Download "Auditing Cloud Service Level Agreement on VM CPU Speed"

Transcription

1 Audtng Cloud Servce Level Agreement on VM CPU Speed Ryan Houlhan, aojang Du, Chu C. Tan, Je Wu Department of Computer and Informaton Scences Temple Unversty Phladelpha, PA 19122, USA Emal: {ryan.houlhan, dux, cctan, Mohsen Guzan Qatar Unversty Doha, Qatar Abstract In ths paper, we present a novel scheme for audtng Servce Level Agreement (SLA) n a sem-trusted or untrusted cloud. A SLA s a contract formed between a cloud servce provder (CSP)and a user whch specfes, n measurable terms, what resources a the CSP wll provde the user. CSP s beng proft based companes have ncentve to cheat on the SLA. By provdng a user wth less resources than specfed n the SLA the CSP can support more users on the same hardware and ncrease ther profts. As the montorng and verfcaton of the SLA s typcally performed on the cloud system tself t s straghtforward for the CSP to le on reports and hde ther ntentonal breach of the SLA. To prevent such cheatng we ntroduce a framework whch makes use of a thrd party audtor (TPA). In ths paper we are nterested n CPU cheatng only. To detect CPU cheatng, we develop an algorthm whch makes use of a commonly used CPU ntensve calculaton, transpose matrx multplcaton, to randomly detect cheatng by a CSP. Usng real experments we show that our algorthm can detect CPU cheatng qute effectvely even f the extent of the cheatng s farly small. Keywords Cloud computng; Servce Level Agreement; audtng; CPU I. INTRODUCTION Over the years, cloud computng has steadly ganed popularty n both ndustral and academc settngs. Cloud computng s a model whch allows for ubqutous and on demand network access to a shared pool of confgurable computng resources whch are capable of beng rapdly provsoned and released [1]. The cloud maxmzes ts effcency usng shared resources and rapd elastcty to acheve both coherence and economes of scale. Usng cloud servces, users can fnd many economc benefts by avodng upfront nfrastructure costs, mantenance costs and operatonal expendtures. Cloud computng systems also sgnfcantly reduce unnecessary overhead by both provdng enterprses wth faster deployment and mproved manageablty by a reducton n mantenance demands. Recently, a number of large cloud provders have begun pay-as-you-go servces. Some of these are Amazon [2], IBM [2], Google [3] and Mega [4]. Wth the emergence of such large and effectve cloud provders, an ever ncreasng number of enterprses and ndvdual users have been mgratng ther data and computatonal tasks to cloud systems. By defnton, offered cloud servces belong to one of three models. These models nclude nfrastructure as a servce (IaaS), platform as a servce (PaaS) and software as a servce (SaaS). IaaS s any cloud system that provdes provson processng, storage, networks and other fundamental computng resource. PaaS s any cloud system that deploys consumercreated or acqured applcatons. SaaS s any cloud systems that provdes applcatons [1]. Cloud servce provders (CSPs) offer ther servces to clents n a pay-as-you-go fashon. The actual servces the CSPs are requred to provde are defned n the Servce Level Agreement (SLA) whch s a contract between the clent and the CSP. For ths work, we are most concerned wth provders of IaaS servces such as Amazons Elastc Compute Cloud (EC2) [2] that provdes basc components such as memory, dsk drves, and CPUs. For IaaS based servces, the SLA metrcs nclude CPU speed, storage sze, network bandwdth, etc. In cloud computng, a SLA serves as the bass for the expected level of servce the CSP s requred to provde. Beng that a CSP s a proft drven enterprse, there s a great ncentve for the CSP to cheat on the SLA. Due to ths ncentve to cheat, a CSP can not guarantee to audt the SLA and to verfy for sure that t s beng met. To handle ths problem Amazon EC2, for example, has now moved the burden of audtng the SLA to the user. Unfortunately, the overhead for ndvdual users to audt the cloud by themselves s hgh, snce any audtng process wll consume resources whch the user has pad for. Thus, the only reasonable choce s to put the burden of audtng the SLA onto a thrd party whose purpose s to verfy that the SLA s beng met. Ths, however, s also problematc beng that the CSP has ncentves to defeat the SLA montorng and verfcaton technques performed by the thrd party by nterferng wth the montorng and measurement process. In ths paper, we present an algorthm for audtng CPU allocaton and verfy the correspondng SLA s beng met va a SLA verfcaton framework whch makes use of a thrd party audtor (TPA). The TPA framework, frst ntroduced by [5], [6], s hghly benefcal for three fundamental reasons. Frst, t s hghly flexble and scalable and can easly be extended to cover a varety of metrcs from memory allocaton to CPU usage. Secondly, t supports testng for multple users whch ncreases the accuracy of the cloud testng. Thrd, t removes the audtng and verfcaton burden from the user and nstead puts t on the TPA. Usng the TPA, we can ether prove that the CSP satsfes the SLA or detect and report an SLA volaton. In ths paper, our contrbuton can be summarzed as follows: Develop a novel algorthm for audtng CPU allocaton usng a TPA framework to verfy the SLA s met. Use real experments to demonstrate the effectveness

2 of our algorthm for detectng CSP cheatng on the SLA metrc of CPU speed. II. RELATED WORK Brandc et al. [7] proposes a layered cloud archtecture to model the bottom-up propagaton of falures and uses these to detect SLA volatons va mappng of resource metrcs to SLA parameters. There have also been a varety of approaches for SLA assessment whch focus on measurng or estmatng Qualty of Servce (QoS) parameters. Sommers et al. [8] proposes a passve traffc analyss method for onlne SLA assessments whch reduce the need for measurng QoS metrcs. Wang and Eugene [9] present a quanttatve study of end-toend network performance among Amazon EC2 and conclude that vrtulzaton causes sgnfcant unstable throughput and abnormal varatons n delay. L et al [10] compares the performance cost of four major cloud provders ncludng Amazon, Mcrosoft, Google and Rackspace. Goldburg et al. bult off of L s study by consderng the prevous work on an untrusted cloud whch can nterfere wth the measurement and montorng process whch s trggered by users. Zhang et al. [5] and Ye et al. [6] propose a flexble and scalable framework whch uses a TPA for SLA verfcaton. Ths framework supports varous types of SLA tests. In partcular, they also develop an effectve testng algorthm that can detect SLA volatons on physcal memory sze of a VM. III. ASSUMPTIONS AND ATHREAT MODEL A. Assumptons In our paper we make the followng assumptons: The TPA can be trusted by the user to properly carry out the audtng functons whle audtng the CSP and verfyng the SLA. The CSP must provde the hypervsor source code to the TPA to ensure that t does not exhbt malcous behavor. The TPA must be able to ensure the ntegrty of the hypervsor. Ths s provded by Trusted Platform Group (TCG) s Trusted Platform Module (TPM) and Core Root of Trust for Measurement (CRTM) [13]. The framework for ensurng hypervsor ntegrty s provded by Hypersentry [11]. Communcaton tme between the cloud system and the TPA s 200 ms or less. B. Threat Model Our threat model conssts fundamentally of the fact that the CSP has complete control over all ts own resources whch nclude physcal machnes, VMs, hypervsor, etc. The CSP s able to access any data held on the VM and know about anythng executed on the VM. The CSP can also modfy any data held on the VM or any output of any executon. For example f a test s run and outputs a varety of tmestamps, the CSP could stealthy change the tmestamp values. Thus, the output data saved on the cloud system s not to be trusted. Fnally, the CSP wll only perform cheatng f the beneft s greater than the cost. The cost may be too large for the CSP and thus the CSP would have no ncentve to cheat. Tme (s) Matrx Sze NxN Fg. 1. Tme t takes to compute a SHA-1 [15] of a NxN matrx of doubles. As s shown the tme to compute the SHA-1 hash [15] s relatvely small beng only s for a 1000x1000 matrx. Ths s 0.3% the tme t takes to compute a transpose matrx multplcaton of a 1000x1000 matrx. IV. IMPLEMENTATION AND EVALUATION A. Requrements A varety of requrements must be fulflled for our audtng test to be effectve n preventng SLA cheatng. Frst, our audtng test must run a farly generc computatonal task so as to not be easly detected as an audt whle also beng computatonally heavy and not wastng tme wth memory passng. To fll ths frst requrement, we choose to use a transpose matrx multplcaton. Ths was chosen over a standard matrx multplcaton as transpose multply mnmzes memory passng whle maxmzng CPU tme. Our audtng test must also be able to detect f the cloud system has modfed the nput or output of our audtng test. It must then report ths malcous behavor. We accomplsh ths through redundant tme recordng performed by the TPA whch wll be covered n depth further on. Next, our audtng test should be senstve enough to detect a wde varety of cheatng behavors, reportng as low as 2% cheatng to be unacceptable. Next, our audtng test should be developed n such a way that ts accuracy does not depend on any of the cloud systems tmng functons but nstead depends only on the accuracy of the tmng functons on the TPA s system. Ths prevents the CSP from reportng false tmes and hdng cheatng. The communcaton overhead of 200 ms must also be less than 1% the total executon tme of a sngle cycle of our executon. Fnally, we must be able to assure the computatonal task has actually been run, not just faked. We do ths by takng a SHA-1 hash [15] of the resultng matrx. The tme to compute the SHA-1 hash [15] of a NxN matrx s relatvely small compared to the computaton tme of a transpose matrx multplcaton. Ths s shown to be true n (Fg. III-A) where t s clear that the tme to run the SHA-1 hash on a NxN matrx of doubles does not ncrease sgnfcantly wth larger szes of N compared to the ncreased computatonal tme of the transpose matrx multplcaton of the larger matrces. As an example t takes s to

3 compute the SHA-1 hash [15] of a 1000x1000 matrx whle t takes on average s to compute the transpose matrx multplcaton of two 1000x1000 matrces on the same system. Ths means that the SHA-1 hash [15] only takes about 0.3 % of the tme t takes to compute the matrx tself. V. IMPLEMENTATION AND ALGORITHM Our mplementaton conssts of three dstnct parts. Intalzaton, algorthm executon, and verfcaton. A. Intlzaton The ntalzaton conssts of the followng parts: VM mrrorng NxN matrx creaton and upload The VM mrrorng s done by the TPA. The TPA must frst rent a VM on the target cloud system. The TPA must then, on ther audtng system, create a VM whch mrrors the specfcatons of the VM they have rented from the cloud provder. Ths VM s must also have the same or smlar background tasks. Next, the TPA must create two NxN matrces where N s a number such that when the two NxN matrces are multpled wth each other the tme, τ to perform the multplcaton s large enough that the communcaton overhead s less than 1% of τ. Fnally, once the NxN matrces are created they must be loaded onto the target cloud systems VM. B. Audtng Test Executon To ntroduce the next stage n our mplementaton, we must frst ntroduce the audtng test tself. Only three peces of nformaton are needed for the executon of our test. Two premade NxN matrces, A and B, and a number where s the number of transpose matrx multplcatons to be run. The executon of the audtng test on the cloud VM s as follows: Output sgnal to termnal that multplcaton wll begn and record the tme, t 2. Perform a matrx multplcaton where C = A B. Record the elapsed tme, e 2, and output to the termnal that the multplcaton has ended. Compute the SHA-1 hash of the resultng matrx C, represented as SHA-1[C]. Output the tme to compute the matrx multplcaton, e 2 and SHA-1[C] to the termnal. Shft each element of matrx A and B by one. Repeat the prevous steps 1 more tmes where s equal to the current transpose matrx multplcaton beng performed. The executon of the algorthm on the TPA VM s as follows: Record the tme, T. Intalze and execute the audtng test on the cloud VM. Watch the output from the cloud VM termnal. Compute the tme elapsed between the sgnal that the multplcaton has started and the sgnal that the multplcaton has ended, e1. Also record the hash value, SHA-1[C], and the executon tme, e 2 as reported by the cloud VM. Record the elapsed tme, E, for the entre executon of the test. Frst, the TPA begns the executon of the audtng test on the cloud VM and records the tme, T, the test begns. The audtng test then ntalzes tself and sgnals va the termnal that t wll compute a transpose matrx multplcaton. Ths sgnals the TPA to take a recordng of the tme t 1 where s ntalzed to one. The audtng test also takes ts own recordng of the tme t 2. Two matrces, A and B, are then multpled as C = A B. The tme s then agan recorded by the audtng test and the tme elapsed snce t 2 s recorded as e 2. The audtng test then sgnals the termnal that the multplcaton has ended. The TPA receves ths sgnal and records the tme elapsed snce t 1 as e 1. Next, the audtng test computes the SHA-1 hash [15] of the resultng matrx, C, assha-1[c]. The elapsed tme e 2 and SHA-1[C] are then both output to the termnal. The TPA then records these values. Next, all the values n A and B are then shfted to the rght by one place. The counter s ncreased by one. The multplcaton process as lsted above s then repeated untl = for a total of 1 tmes. Fnally, the executon of the audtng test ends and the TPA records the tme elapsed snce T as E. Whle ths s all gong on the TPA runs, on ts VM, the same algorthm records the tme for each transpose matrx multplcaton test as well as the hash of the result for valdaton purposes. C. Verfcaton Now the queston remans: Gven all ths nformaton we have recorded, how do we detect a breach n the SLA by the CSP? The frst step we take s to sum up the e 2 value, e 2 for all tests run on the cloud VM. We compare ths to the value of E. Snce the communcaton overhead, SHA- 1 hashng, and tme to shft the matrx values are very small compared to the executon tme of the algorthm we can be assured that e 2 should be close to but less than the value of E. If the CSP has blatantly cheated then e 2 wll be greater than, or sgnfcantly less than, the value of E and a volaton of the SLA can be detected. Next, we take e 1 and compare t to e 2. Agan snce the communcaton tme s small compared to the actual transpose matrx multplcaton, e 1 should be very close, << 1%, to e 2. If t s not, then a volaton of the SLA s once agan s detected.. Furthermore, the hash of the resultng matrx C, wrtten as SHA-1[C ], from the tests run on the TPA s VM should match the SHA-1[C ] values produced on the clouds. Ths prevents the cloud from avodng dong an actual computaton or delayng computatons to a later tme.. Snce t s mpossble to compute the correct SHA-1[C ] values wthout computng C tself, the CSP can not avod carryng out the computatons. Also snce the hash s output and recorded va the termnal by the TPA, each transpose matrx multplcaton n the cloud has no way of delayng the computaton and later modfyng

4 TABLE I. Average Tme 100% CPU: EECUTIONS OF THE AUDITING TEST. AVERAGE (s) STDEV (s) STDEV % DIFF TTL EECUTION % DIFF AV 100% CPU (Run 1): % 112 mn s 0.13% 100% CPU (Run 2): % 113 mn s 0.06% 100% CPU (Run 3): % 113 mn s 0.06% 100% CPU (Run 4): % 112 mn s 0.02% 90% CPU: % 125 mn s 11.38% 80% CPU: % 141 mn s 25.13% 70% CPU: % 161 mn s 43.12% 60% CPU: % 188 mn s 67.11% 85% CPU 15% TTL: % 115 mn s 2.78% 85% CPU 30% TTL: % 119 mn s 5.53% 70% CPU 15% TTL: % 119 mn s 6.52% 70% CPU 30% TTL: % 127 mn s 13.02% Average Tme % 10% 20% 30% 40% % Cheatng Fg. 2. The average tme to run a sngle transpose matrx multplcaton based on the percent cheatng (100%-CPU Cap %). As the % cheatng ncreases the average run tme ncreases lnerealy, as expected. Average Tme % 15-15% 15-30% 30-15% 30-30% % Cheatng - % Tme Fg. 3. The average tme to run a sngle transpose matrx multplcaton based on the percent cheatng (100%-CPU Cap %) and the % tme the cheatng lasts. As the % cheatng or the % tme of cheatng ncreases the average run tme ncreases as expected. the output. If the hashes do not match the TPA VM and the CSP VM, then we have once agan detected a volaton of the SLA. VI. TESTING A. Background To test our algorthms ablty to detect SLA volatons by the CSP, we ran a varety of dfferent tests. Intally, we ran four tests where no cheatng has occurred to fnd a base executon tme. We then ran one test each where the CSP lmts the CPU percentage to 90%, 80%, 70%, and 60% of the expected CPU, respectvely. We also ran tests where the CSP lmts the CPU to 85% and 70% of ts requred value for 15% of the algorthms executon and 30% of the algorthms executon.15% and 30% of the algorthms executon, respectvely. To perform ths testng, we used Ubuntu Server LST wth en DOM-0 Hypervsor 4.1 x64 [12]. For our SHA-1 hashng [15] algorthm we used PolarSSL s [14] lbrary. The tests were run on a system wth 4 Ggs of ram and a Intel Q6600 Quad Core processor. The VM used was gven one processor wth a clock of 1.0 Ghz as well as 1 Ggabyte of RAM. To create a CPU cap on our VM, we used en s schedcredt functon. The sched-credt functon allows us to specfy a CPU cap n percentage. Ths s done by the command xm sched-credt -d <doman> -c <cap> where <doman> s the name of the VM n queston and <cap> s the cap we would lke to apply to the CPU n terms of percentage. B. Results The results of our test are shown n Table I. For all these runs, we used a 1000x1000 matrx of doubles. In Table I the frst column lsts the average, e2 for each ndvdual audtng test. The second lsts the standard devaton, σ of each e 2. The thrd lsts the percent dfference between the average and the standard devaton. The fourth lsts the recorded tme to execute the entre audtng test. Fnally, the last column lsts the percent dfference of the e2 wth the average of e2 for each of the four runs where there was no cheatng. For the frst four runs we dd not set a cap on the CPU and used 100% of the allocated CPU. As we can clearly see that σ for all four of the runs usng 100% of the CPU s very small and the correspondng percent dfference s also small as expected. The total run tmes of each of the audtng test were also farly consstent. Lastly the percent dfference from the

5 average of the four 100% CPU runs s also small. The largest percent dfference s only 0.13% where the smallest s 0.02%. Next, f we look at the 90% CPU run we can see that σ and the percent dfference are both also small as expected.. We notce though that the total run tme of the audtng test has ncreased sgnfcantly. We also notce that the percent dfference between the 90% CPU test and the average of the four 100% CPU runs has ncreased sgnfcantly to 11.38%. Thus n our analyss, t s very obvous that the CSP has volated the SLA by puttng a cap on the total CPU percentage we can use. For a CPU cap of 80%, 70% and 60%, we notce smlar results. The average, e2, for each run steadly ncreases. The correspondng percent dfference from the average of the four 100% runs also ncreases sgnfcantly. When the CSP has a CPU cap of 60% we see a percent dfference of 67.11% from what we would expect. Thus, from these results t s farly safe to say that f a CSP puts an unchangng cap on the CPU, the proposed audtng test wll easly detect ths cap and report t as a volaton of the SLA. We also performed an analyss on cheatng of a dfferent type. A cloud provder, rather than puttng a sngle unchangng cap on the CPU mght nstead cheat only a percentage of the tme. We replcated such an event by cappng the CPU at 85% and 70% for 15% and 30% of the executon tme. For the remanng 85% and 70% of the respectve executons, the cap was removed. Frst, lookng at the two 85% CPU cap runs we notce a sgnfcant ncrease n σ and the percent dfference. Ths nconsstency n run tme of each ndvdual transpose matrx multplcaton s the frst obvous sgn of a malcous actvty. For the run wth a cap only used for 15% of the total executon, a percent dfference of 9.33% was found. For the run wth a cap used for 30% of the total executon a percent dfference of 12.30% was found. Furthermore, the percent dfference from the average also shows clear cheatng by the CSP. Overall, for both 85% CPU cap runs volatons of the SLA by the CSP are very obvous. Smlarly for the two 75% CPU cap runs, there were also large nconsstences between the run tmes of each ndvdual transpose matrx multplcaton. For the frst run wth a cap for 15% of the executon tme, the percent dfference between σ and the average has ncreased notceably to 14.45%. For the second run wth a cap for 30% of the executon tme, ths percent dfference ncreases even further to %. Agan ths s a sgn of malcous actvty by the CSP. Fnally, the percent dfferences from the average expected tme are 6.52% and 13.02% whch are clear sgns of a SLA volaton by the CSP. VII. CONCLUSION Due to an ncreased nterest n cloud computng, provdng accountablty to clents has become a crtcal component of the value proposton offered by cloud provders. Servce Level Agreements (SLA) defne the agreement between cloud servce provders (CSPs) and ther users. Beng that CSP s are proft based companes, t s n the CSP s best nterest to cheat on the SLA. To allevate ths problem we make use of a Thrd Party Audtor (TPA) to audt the SLA and verfy t s beng met by the CSP. In ths paper, we develop a scheme whch makes use of a TPA to audt the SLA metrc of CPU speed and verfy t s beng met by the CSP. Overall, our audtng scheme shows promsng results and s able to detect even mnor cheatng by the CSP on the SLA regardless of the CSPs attempts to hde ts cheatng. ACKNOWLEDGMENT Ths work was supported n part by the US Natonal Scence Foundaton under grants CNS , CNS , and CNS REFERENCES [1] The NIST Defnton of Cloud Computng. Natonal Insttute of Standards and Technology. Retreved [2] Amazon EC2, [Onlne]. Avalable: [3] Google App Engne, [Onlne]. Avalable: rse/appengne [4] Mega, [Onlne]. Avalable: [5] H. Zhang, L. Ye, J. Sh,. Du. Verfng Cloud Servce-Level Agreement By a Thrd-Party Audtor, Securty and Communcaton Networks, [6] L. Ye, H. Zhang, J. Sh,. Du. Verfyng Cloud Servce Level Agreement, Proceedngs of IEEE Global Communcatons Conference (GLOBECOM), pp , [7] I. Brandc, V. C. Emeakaroha, M. Maurer, S. Dustdar, S. Acs, A. Kertes, and G. Kecskemet, Proceedngs of 34th Annual IEEE Computer Software and Applcatons Conference Workshops, pp , [8] J. Sommers, P. Barford, N. Duffeld, and A. Ron, IEEE/ACM Transactons on Networkng, vol. 18, ssue. 2, IEEE Press: NY, USA, pp , [9] G. Wang and N. T. Eugene, Proceedngs of the 29th IEEE Conference on Computer Communcatons, pp , [10] A. L,. Yang, S. Kandula, and M. Zang, Proceedngs of the 10th Internet Measurement Conference, ACM: New York, NY, USA, pp. 1-14, [11] A. M. Azab, P. Nng, Z. Wang,. Jang,. Zhang, N. C. Skalsky. HyperSentry: Enablng Stealthy In-context Measurement of Hypervsor Integrty. Proc. of the 17th ACM Conference on Computer and Communcatons Securty, pp , [12] P. Barham, B. Dargovc, K. Fraser, S. Hand, T. Harrs, A. Ho, R. Neugebauer, I. Pratt, A. Warfeld. en and the Art of Vrtualzaton. Proc. 19th ACM Symposum on Operatng Systems Prncples, SOSP 2003, Bolton Landng, USA, October [13] Trusted Computng Group. TPM specfcatons verson July [14] PolarSSL. Offspark, Avalable: code [15] Department of Commerce Natonal Insttute of Standards and Technology. Secure Hash Sgnature Standard (SHS) (FIPS PUB 180-2). February 2004

Fault tolerance in cloud technologies presented as a service

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

More information

A Replication-Based and Fault Tolerant Allocation Algorithm for Cloud Computing

A Replication-Based and Fault Tolerant Allocation Algorithm for Cloud Computing A Replcaton-Based and Fault Tolerant Allocaton Algorthm for Cloud Computng Tork Altameem Dept of Computer Scence, RCC, Kng Saud Unversty, PO Box: 28095 11437 Ryadh-Saud Araba Abstract The very large nfrastructure

More information

An Alternative Way to Measure Private Equity Performance

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

More information

Open Access A Load Balancing Strategy with Bandwidth Constraint in Cloud Computing. Jing Deng 1,*, Ping Guo 2, Qi Li 3, Haizhu Chen 1

Open Access A Load Balancing Strategy with Bandwidth Constraint in Cloud Computing. Jing Deng 1,*, Ping Guo 2, Qi Li 3, Haizhu Chen 1 Send Orders for Reprnts to reprnts@benthamscence.ae The Open Cybernetcs & Systemcs Journal, 2014, 8, 115-121 115 Open Access A Load Balancng Strategy wth Bandwdth Constrant n Cloud Computng Jng Deng 1,*,

More information

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

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.

More information

Survey on Virtual Machine Placement Techniques in Cloud Computing Environment

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

More information

What is Candidate Sampling

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

More information

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

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,

More information

A Secure Password-Authenticated Key Agreement Using Smart Cards

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,

More information

On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features

On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features On-Lne Fault Detecton n Wnd Turbne Transmsson System usng Adaptve Flter and Robust Statstcal Features Ruoyu L Remote Dagnostcs Center SKF USA Inc. 3443 N. Sam Houston Pkwy., Houston TX 77086 Emal: ruoyu.l@skf.com

More information

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

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..

More information

SEVERAL trends are opening up the era of Cloud

SEVERAL trends are opening up the era of Cloud 1 Towards Secure and Dependable Storage Servces n Cloud Computng Cong Wang, Student Member, IEEE, Qan Wang, Student Member, IEEE, Ku Ren, Member, IEEE, Nng Cao, Student Member, IEEE, and Wenjng Lou, Senor

More information

Can Auto Liability Insurance Purchases Signal Risk Attitude?

Can Auto Liability Insurance Purchases Signal Risk Attitude? Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang

More information

Implementation of Deutsch's Algorithm Using Mathcad

Implementation of Deutsch's Algorithm Using Mathcad Implementaton of Deutsch's Algorthm Usng Mathcad Frank Roux The followng s a Mathcad mplementaton of Davd Deutsch's quantum computer prototype as presented on pages - n "Machnes, Logc and Quantum Physcs"

More information

VRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) 2127472, Fax: (370-5) 276 1380, Email: info@teltonika.

VRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) 2127472, Fax: (370-5) 276 1380, Email: info@teltonika. VRT012 User s gude V0.1 Thank you for purchasng our product. We hope ths user-frendly devce wll be helpful n realsng your deas and brngng comfort to your lfe. Please take few mnutes to read ths manual

More information

SEVERAL trends are opening up the era of Cloud

SEVERAL trends are opening up the era of Cloud IEEE Transactons on Cloud Computng Date of Publcaton: Aprl-June 2012 Volume: 5, Issue: 2 1 Towards Secure and Dependable Storage Servces n Cloud Computng Cong Wang, Student Member, IEEE, Qan Wang, Student

More information

APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT

APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT Toshhko Oda (1), Kochro Iwaoka (2) (1), (2) Infrastructure Systems Busness Unt, Panasonc System Networks Co., Ltd. Saedo-cho

More information

Trivial lump sum R5.0

Trivial lump sum R5.0 Optons form Once you have flled n ths form, please return t wth your orgnal brth certfcate to: Premer PO Box 2067 Croydon CR90 9ND. Fll n ths form usng BLOCK CAPITALS and black nk. Mark all answers wth

More information

Small pots lump sum payment instruction

Small pots lump sum payment instruction For customers Small pots lump sum payment nstructon Please read these notes before completng ths nstructon About ths nstructon Use ths nstructon f you re an ndvdual wth Aegon Retrement Choces Self Invested

More information

RequIn, a tool for fast web traffic inference

RequIn, a tool for fast web traffic inference RequIn, a tool for fast web traffc nference Olver aul, Jean Etenne Kba GET/INT, LOR Department 9 rue Charles Fourer 90 Evry, France Olver.aul@nt-evry.fr, Jean-Etenne.Kba@nt-evry.fr Abstract As networked

More information

CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol

CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL

More information

Ensuring Data Storage Security in Cloud Computing

Ensuring Data Storage Security in Cloud Computing 1 Ensurng Data Storage Securty n Cloud Computng Cong Wang,Qan Wang, Ku Ren, and Wenjng Lou Dept of ECE, Illnos Insttute of Technology, Emal: {cwang, qwang, kren}@ecetedu Dept of ECE, Worcester Polytechnc

More information

An RFID Distance Bounding Protocol

An RFID Distance Bounding Protocol An RFID Dstance Boundng Protocol Gerhard P. Hancke and Markus G. Kuhn May 22, 2006 An RFID Dstance Boundng Protocol p. 1 Dstance boundng Verfer d Prover Places an upper bound on physcal dstance Does not

More information

Politecnico di Torino. Porto Institutional Repository

Politecnico di Torino. Porto Institutional Repository Poltecnco d Torno Porto Insttutonal Repostory [Artcle] A cost-effectve cloud computng framework for acceleratng multmeda communcaton smulatons Orgnal Ctaton: D. Angel, E. Masala (2012). A cost-effectve

More information

A Design Method of High-availability and Low-optical-loss Optical Aggregation Network Architecture

A Design Method of High-availability and Low-optical-loss Optical Aggregation Network Architecture A Desgn Method of Hgh-avalablty and Low-optcal-loss Optcal Aggregaton Network Archtecture Takehro Sato, Kuntaka Ashzawa, Kazumasa Tokuhash, Dasuke Ish, Satoru Okamoto and Naoak Yamanaka Dept. of Informaton

More information

HP Mission-Critical Services

HP Mission-Critical Services HP Msson-Crtcal Servces Delverng busness value to IT Jelena Bratc Zarko Subotc TS Support tm Mart 2012, Podgorca 2010 Hewlett-Packard Development Company, L.P. The nformaton contaned heren s subject to

More information

Vision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION

Vision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION Vson Mouse Saurabh Sarkar a* a Unversty of Cncnnat, Cncnnat, USA ABSTRACT The report dscusses a vson based approach towards trackng of eyes and fngers. The report descrbes the process of locatng the possble

More information

iavenue iavenue i i i iavenue iavenue iavenue

iavenue iavenue i i i iavenue iavenue iavenue Saratoga Systems' enterprse-wde Avenue CRM system s a comprehensve web-enabled software soluton. Ths next generaton system enables you to effectvely manage and enhance your customer relatonshps n both

More information

J. Parallel Distrib. Comput.

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

More information

Canon NTSC Help Desk Documentation

Canon NTSC Help Desk Documentation Canon NTSC Help Desk Documentaton READ THIS BEFORE PROCEEDING Before revewng ths documentaton, Canon Busness Solutons, Inc. ( CBS ) hereby refers you, the customer or customer s representatve or agent

More information

Proactive Secret Sharing Or: How to Cope With Perpetual Leakage

Proactive Secret Sharing Or: How to Cope With Perpetual Leakage Proactve Secret Sharng Or: How to Cope Wth Perpetual Leakage Paper by Amr Herzberg Stanslaw Jareck Hugo Krawczyk Mot Yung Presentaton by Davd Zage What s Secret Sharng Basc Idea ((2, 2)-threshold scheme):

More information

Luby s Alg. for Maximal Independent Sets using Pairwise Independence

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

More information

Ensuring Data Storage Security in Cloud Computing

Ensuring Data Storage Security in Cloud Computing Ensurng Data Storage Securty n Cloud Computng Cong Wang, Qan Wang, and Ku Ren Department of ECE Illnos Insttute of Technology Emal: {cwang, qwang, kren}@ece.t.edu Wenjng Lou Department of ECE Worcester

More information

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

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

More information

A Resource-trading Mechanism for Efficient Distribution of Large-volume Contents on Peer-to-Peer Networks

A Resource-trading Mechanism for Efficient Distribution of Large-volume Contents on Peer-to-Peer Networks A Resource-tradng Mechansm for Effcent Dstrbuton of Large-volume Contents on Peer-to-Peer Networks SmonG.M.Koo,C.S.GeorgeLee, Karthk Kannan School of Electrcal and Computer Engneerng Krannet School of

More information

Hollinger Canadian Publishing Holdings Co. ( HCPH ) proceeding under the Companies Creditors Arrangement Act ( CCAA )

Hollinger Canadian Publishing Holdings Co. ( HCPH ) proceeding under the Companies Creditors Arrangement Act ( CCAA ) February 17, 2011 Andrew J. Hatnay ahatnay@kmlaw.ca Dear Sr/Madam: Re: Re: Hollnger Canadan Publshng Holdngs Co. ( HCPH ) proceedng under the Companes Credtors Arrangement Act ( CCAA ) Update on CCAA Proceedngs

More information

Efficient Dynamic Integrity Verification for Big Data Supporting Users Revocability

Efficient Dynamic Integrity Verification for Big Data Supporting Users Revocability nformaton Artcle Effcent Dynamc Integrty Verfcaton for Bg Data Supportng Users Revocablty Xnpeng Zhang 1,2, *, Chunxang Xu 1, Xaojun Zhang 1, Tazong Gu 2, Zh Geng 2 and Guopng Lu 2 1 School of Computer

More information

Introduction CONTENT. - Whitepaper -

Introduction CONTENT. - Whitepaper - OneCl oud ForAl l YourCr t c al Bus nes sappl c at ons Bl uew r esol ut ons www. bl uew r e. c o. uk Introducton Bluewre Cloud s a fully customsable IaaS cloud platform desgned for organsatons who want

More information

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS 21 22 September 2007, BULGARIA 119 Proceedngs of the Internatonal Conference on Informaton Technologes (InfoTech-2007) 21 st 22 nd September 2007, Bulgara vol. 2 INVESTIGATION OF VEHICULAR USERS FAIRNESS

More information

DBA-VM: Dynamic Bandwidth Allocator for Virtual Machines

DBA-VM: Dynamic Bandwidth Allocator for Virtual Machines DBA-VM: Dynamc Bandwdth Allocator for Vrtual Machnes Ahmed Amamou, Manel Bourguba, Kamel Haddadou and Guy Pujolle LIP6, Perre & Mare Cure Unversty, 4 Place Jusseu 755 Pars, France Gand SAS, 65 Boulevard

More information

Optimization Model of Reliable Data Storage in Cloud Environment Using Genetic Algorithm

Optimization Model of Reliable Data Storage in Cloud Environment Using Genetic Algorithm Internatonal Journal of Grd Dstrbuton Computng, pp.175-190 http://dx.do.org/10.14257/gdc.2014.7.6.14 Optmzaton odel of Relable Data Storage n Cloud Envronment Usng Genetc Algorthm Feng Lu 1,2,3, Hatao

More information

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,

More information

Multiple-Period Attribution: Residuals and Compounding

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

More information

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

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

More information

Traffic-light a stress test for life insurance provisions

Traffic-light a stress test for life insurance provisions MEMORANDUM Date 006-09-7 Authors Bengt von Bahr, Göran Ronge Traffc-lght a stress test for lfe nsurance provsons Fnansnspetonen P.O. Box 6750 SE-113 85 Stocholm [Sveavägen 167] Tel +46 8 787 80 00 Fax

More information

Pricing Model of Cloud Computing Service with Partial Multihoming

Pricing Model of Cloud Computing Service with Partial Multihoming Prcng Model of Cloud Computng Servce wth Partal Multhomng Zhang Ru 1 Tang Bng-yong 1 1.Glorous Sun School of Busness and Managment Donghua Unversty Shangha 251 Chna E-mal:ru528369@mal.dhu.edu.cn Abstract

More information

v a 1 b 1 i, a 2 b 2 i,..., a n b n i.

v a 1 b 1 i, a 2 b 2 i,..., a n b n i. SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are

More information

IWFMS: An Internal Workflow Management System/Optimizer for Hadoop

IWFMS: An Internal Workflow Management System/Optimizer for Hadoop IWFMS: An Internal Workflow Management System/Optmzer for Hadoop Lan Lu, Yao Shen Department of Computer Scence and Engneerng Shangha JaoTong Unversty Shangha, Chna lustrve@gmal.com, yshen@cs.sjtu.edu.cn

More information

Vembu StoreGrid Windows Client Installation Guide

Vembu StoreGrid Windows Client Installation Guide Ser v cepr ov dered t on Cl enti nst al l at ongu de W ndows Vembu StoreGrd Wndows Clent Installaton Gude Download the Wndows nstaller, VembuStoreGrd_4_2_0_SP_Clent_Only.exe To nstall StoreGrd clent on

More information

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently.

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently. Corporate Polces & Procedures Human Resources - Document CPP216 Leave Management Frst Produced: Current Verson: Past Revsons: Revew Cycle: Apples From: 09/09/09 26/10/12 09/09/09 3 years Immedately Authorsaton:

More information

Network Aware Load-Balancing via Parallel VM Migration for Data Centers

Network Aware Load-Balancing via Parallel VM Migration for Data Centers Network Aware Load-Balancng va Parallel VM Mgraton for Data Centers Kun-Tng Chen 2, Chen Chen 12, Po-Hsang Wang 2 1 Informaton Technology Servce Center, 2 Department of Computer Scence Natonal Chao Tung

More information

Dynamic Fleet Management for Cybercars

Dynamic Fleet Management for Cybercars Proceedngs of the IEEE ITSC 2006 2006 IEEE Intellgent Transportaton Systems Conference Toronto, Canada, September 17-20, 2006 TC7.5 Dynamc Fleet Management for Cybercars Fenghu. Wang, Mng. Yang, Ruqng.

More information

Calculating the high frequency transmission line parameters of power cables

Calculating the high frequency transmission line parameters of power cables < ' Calculatng the hgh frequency transmsson lne parameters of power cables Authors: Dr. John Dcknson, Laboratory Servces Manager, N 0 RW E B Communcatons Mr. Peter J. Ncholson, Project Assgnment Manager,

More information

Modeling and Analysis of 2D Service Differentiation on e-commerce Servers

Modeling and Analysis of 2D Service Differentiation on e-commerce Servers Modelng and Analyss of D Servce Dfferentaton on e-commerce Servers Xaobo Zhou, Unversty of Colorado, Colorado Sprng, CO zbo@cs.uccs.edu Janbn We and Cheng-Zhong Xu Wayne State Unversty, Detrot, Mchgan

More information

Forecasting the Direction and Strength of Stock Market Movement

Forecasting the Direction and Strength of Stock Market Movement Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract - Stock market s one of the most complcated systems

More information

One Click.. Ȯne Location.. Ȯne Portal...

One Click.. Ȯne Location.. Ȯne Portal... New Addton to your NJ-HITEC Membershp! Member Portal Detals & Features Insde! One Clck.. Ȯne Locaton.. Ȯne Portal... Connect...Share...Smplfy Health IT Member Portal Benefts Trusted Advsor - NJ-HITEC s

More information

Fair Virtual Bandwidth Allocation Model in Virtual Data Centers

Fair Virtual Bandwidth Allocation Model in Virtual Data Centers Far Vrtual Bandwdth Allocaton Model n Vrtual Data Centers Yng Yuan, Cu-rong Wang, Cong Wang School of Informaton Scence and Engneerng ortheastern Unversty Shenyang, Chna School of Computer and Communcaton

More information

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

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

More information

Intra-year Cash Flow Patterns: A Simple Solution for an Unnecessary Appraisal Error

Intra-year Cash Flow Patterns: A Simple Solution for an Unnecessary Appraisal Error Intra-year Cash Flow Patterns: A Smple Soluton for an Unnecessary Apprasal Error By C. Donald Wggns (Professor of Accountng and Fnance, the Unversty of North Florda), B. Perry Woodsde (Assocate Professor

More information

Data Mining from the Information Systems: Performance Indicators at Masaryk University in Brno

Data Mining from the Information Systems: Performance Indicators at Masaryk University in Brno Data Mnng from the Informaton Systems: Performance Indcators at Masaryk Unversty n Brno Mkuláš Bek EUA Workshop Strasbourg, 1-2 December 2006 1 Locaton of Brno Brno EUA Workshop Strasbourg, 1-2 December

More information

Statistical Approach for Offline Handwritten Signature Verification

Statistical Approach for Offline Handwritten Signature Verification Journal of Computer Scence 4 (3): 181-185, 2008 ISSN 1549-3636 2008 Scence Publcatons Statstcal Approach for Offlne Handwrtten Sgnature Verfcaton 2 Debnath Bhattacharyya, 1 Samr Kumar Bandyopadhyay, 2

More information

Performance Evaluation of Infrastructure as Service Clouds with SLA Constraints

Performance Evaluation of Infrastructure as Service Clouds with SLA Constraints Performance Evaluaton of Infrastructure as Servce Clouds wth SLA Constrants Anuar Lezama Barquet, Andre Tchernykh, and Ramn Yahyapour 2 Computer Scence Department, CICESE Research Center, Ensenada, BC,

More information

PKIS: practical keyword index search on cloud datacenter

PKIS: practical keyword index search on cloud datacenter Park et al. EURASIP Journal on Wreless Communcatons and Networkng 20, 20:64 http://jwcn.euraspjournals.com/content/20//64 RESEARCH Open Access PKIS: practcal keyword ndex search on cloud datacenter Hyun-A

More information

Daily Mood Assessment based on Mobile Phone Sensing

Daily Mood Assessment based on Mobile Phone Sensing 2012 Nnth Internatonal Conference on Wearable and Implantable Body Sensor Networks Daly Mood Assessment based on Moble Phone Sensng Yuanchao Ma Bn Xu Yn Ba Guodong Sun Department of Computer Scence and

More information

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):1884-1889 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel

More information

Conferencing protocols and Petri net analysis

Conferencing protocols and Petri net analysis Conferencng protocols and Petr net analyss E. ANTONIDAKIS Department of Electroncs, Technologcal Educatonal Insttute of Crete, GREECE ena@chana.tecrete.gr Abstract: Durng a computer conference, users desre

More information

Secure Network Coding Over the Integers

Secure Network Coding Over the Integers Secure Network Codng Over the Integers Rosaro Gennaro Jonathan Katz Hugo Krawczyk Tal Rabn Abstract Network codng has receved sgnfcant attenton n the networkng communty for ts potental to ncrease throughput

More information

A Dynamic Energy-Efficiency Mechanism for Data Center Networks

A Dynamic Energy-Efficiency Mechanism for Data Center Networks A Dynamc Energy-Effcency Mechansm for Data Center Networks Sun Lang, Zhang Jnfang, Huang Daochao, Yang Dong, Qn Yajuan A Dynamc Energy-Effcency Mechansm for Data Center Networks 1 Sun Lang, 1 Zhang Jnfang,

More information

Resource Scheduling in Desktop Grid by Grid-JQA

Resource Scheduling in Desktop Grid by Grid-JQA The 3rd Internatonal Conference on Grd and Pervasve Computng - Worshops esource Schedulng n Destop Grd by Grd-JQA L. Mohammad Khanl M. Analou Assstant professor Assstant professor C.S. Dept.Tabrz Unversty

More information

Using Elasticity to Improve Inline Data Deduplication Storage Systems

Using Elasticity to Improve Inline Data Deduplication Storage Systems Usng Elastcty to Improve Inlne Data Deduplcaton Storage Systems Yufeng Wang Temple Unversty Phladelpha, PA, USA Y.F.Wang@temple.edu Chu C Tan Temple Unversty Phladelpha, PA, USA cctan@temple.edu Nngfang

More information

LITERATURE REVIEW: VARIOUS PRIORITY BASED TASK SCHEDULING ALGORITHMS IN CLOUD COMPUTING

LITERATURE REVIEW: VARIOUS PRIORITY BASED TASK SCHEDULING ALGORITHMS IN CLOUD COMPUTING LITERATURE REVIEW: VARIOUS PRIORITY BASED TASK SCHEDULING ALGORITHMS IN CLOUD COMPUTING 1 MS. POOJA.P.VASANI, 2 MR. NISHANT.S. SANGHANI 1 M.Tech. [Software Systems] Student, Patel College of Scence and

More information

SEVERAL trends are opening up the era of cloud computing,

SEVERAL trends are opening up the era of cloud computing, 220 IEEE TRANSACTIONS ON SERVICES COMPUTING, VOL 5, NO 2, APRIL-JUNE 2012 Toward Secure and Dependable Storage Servces n Cloud Computng Cong Wang, Student Member, IEEE, Qan Wang, Student Member, IEEE,

More information

Watermark-based Provable Data Possession for Multimedia File in Cloud Storage

Watermark-based Provable Data Possession for Multimedia File in Cloud Storage Vol.48 (CIA 014), pp.103-107 http://dx.do.org/10.1457/astl.014.48.18 Watermar-based Provable Data Possesson for Multmeda Fle n Cloud Storage Yongjun Ren 1,, Jang Xu 1,, Jn Wang 1,, Lmng Fang 3, Jeong-U

More information

Calculation of Sampling Weights

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

More information

A New Quality of Service Metric for Hard/Soft Real-Time Applications

A New Quality of Service Metric for Hard/Soft Real-Time Applications A New Qualty of Servce Metrc for Hard/Soft Real-Tme Applcatons Shaoxong Hua and Gang Qu Electrcal and Computer Engneerng Department and Insttute of Advanced Computer Study Unversty of Maryland, College

More information

Feasibility of Using Discriminate Pricing Schemes for Energy Trading in Smart Grid

Feasibility of Using Discriminate Pricing Schemes for Energy Trading in Smart Grid Feasblty of Usng Dscrmnate Prcng Schemes for Energy Tradng n Smart Grd Wayes Tushar, Chau Yuen, Bo Cha, Davd B. Smth, and H. Vncent Poor Sngapore Unversty of Technology and Desgn, Sngapore 138682. Emal:

More information

How To Understand The Results Of The German Meris Cloud And Water Vapour Product

How To Understand The Results Of The German Meris Cloud And Water Vapour Product Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPP-ATBD-ClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller

More information

For example, you might want to capture security group membership changes. A quick web search may lead you to the 632 event.

For example, you might want to capture security group membership changes. A quick web search may lead you to the 632 event. Audtng Wndows & Actve Drectory Changes va Wndows Event Logs Ths document takes a lghtweght look at the steps and consderatons nvolved n settng up Wndows and/or Actve Drectory event log audtng. Settng up

More information

Genetic Algorithm Based Optimization Model for Reliable Data Storage in Cloud Environment

Genetic Algorithm Based Optimization Model for Reliable Data Storage in Cloud Environment Advanced Scence and Technology Letters, pp.74-79 http://dx.do.org/10.14257/astl.2014.50.12 Genetc Algorthm Based Optmzaton Model for Relable Data Storage n Cloud Envronment Feng Lu 1,2,3, Hatao Wu 1,3,

More information

How To Plan A Network Wide Load Balancing Route For A Network Wde Network (Network)

How To Plan A Network Wide Load Balancing Route For A Network Wde Network (Network) Network-Wde Load Balancng Routng Wth Performance Guarantees Kartk Gopalan Tz-cker Chueh Yow-Jan Ln Florda State Unversty Stony Brook Unversty Telcorda Research kartk@cs.fsu.edu chueh@cs.sunysb.edu yjln@research.telcorda.com

More information

Stress test for measuring insurance risks in non-life insurance

Stress test for measuring insurance risks in non-life insurance PROMEMORIA Datum June 01 Fnansnspektonen Författare Bengt von Bahr, Younes Elonq and Erk Elvers Stress test for measurng nsurance rsks n non-lfe nsurance Summary Ths memo descrbes stress testng of nsurance

More information

Sketching Sampled Data Streams

Sketching Sampled Data Streams Sketchng Sampled Data Streams Florn Rusu, Aln Dobra CISE Department Unversty of Florda Ganesvlle, FL, USA frusu@cse.ufl.edu adobra@cse.ufl.edu Abstract Samplng s used as a unversal method to reduce the

More information

An Interest-Oriented Network Evolution Mechanism for Online Communities

An Interest-Oriented Network Evolution Mechanism for Online Communities An Interest-Orented Network Evoluton Mechansm for Onlne Communtes Cahong Sun and Xaopng Yang School of Informaton, Renmn Unversty of Chna, Bejng 100872, P.R. Chna {chsun,yang}@ruc.edu.cn Abstract. Onlne

More information

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by 6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng

More information

A Performance Analysis of View Maintenance Techniques for Data Warehouses

A Performance Analysis of View Maintenance Techniques for Data Warehouses A Performance Analyss of Vew Mantenance Technques for Data Warehouses Xng Wang Dell Computer Corporaton Round Roc, Texas Le Gruenwald The nversty of Olahoma School of Computer Scence orman, OK 739 Guangtao

More information

Construction Rules for Morningstar Canada Target Dividend Index SM

Construction Rules for Morningstar Canada Target Dividend Index SM Constructon Rules for Mornngstar Canada Target Dvdend Index SM Mornngstar Methodology Paper October 2014 Verson 1.2 2014 Mornngstar, Inc. All rghts reserved. The nformaton n ths document s the property

More information

End-to-end measurements of GPRS-EDGE networks have

End-to-end measurements of GPRS-EDGE networks have End-to-end measurements over GPRS-EDGE networks Juan Andrés Negrera Facultad de Ingenería, Unversdad de la Repúblca Montevdeo, Uruguay Javer Perera Facultad de Ingenería, Unversdad de la Repúblca Montevdeo,

More information

A High-confidence Cyber-Physical Alarm System: Design and Implementation

A High-confidence Cyber-Physical Alarm System: Design and Implementation A Hgh-confdence Cyber-Physcal Alarm System: Desgn and Implementaton Longhua Ma 1,2, Tengka Yuan 1, Feng Xa 3, Mng Xu 1, Jun Yao 1, Meng Shao 4 1 Department of Control Scence and Engneerng, Zhejang Unversty,

More information

QOS DISTRIBUTION MONITORING FOR PERFORMANCE MANAGEMENT IN MULTIMEDIA NETWORKS

QOS DISTRIBUTION MONITORING FOR PERFORMANCE MANAGEMENT IN MULTIMEDIA NETWORKS QOS DISTRIBUTION MONITORING FOR PERFORMANCE MANAGEMENT IN MULTIMEDIA NETWORKS Yumng Jang, Chen-Khong Tham, Ch-Chung Ko Department Electrcal Engneerng Natonal Unversty Sngapore 119260 Sngapore Emal: {engp7450,

More information

DEFINING %COMPLETE IN MICROSOFT PROJECT

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,

More information

Robert Wilson for their comments on the a predecessor version of this paper.

Robert Wilson for their comments on the a predecessor version of this paper. Procurng Unversal Telephone ervce Paul Mlgrom 1 tanford Unversty, August, 1997 Reprnted from 1997 Industry Economcs Conference Proceedngs, AGP Canberra Introducton One of the hallmarks of modern socety

More information

VoIP Playout Buffer Adjustment using Adaptive Estimation of Network Delays

VoIP Playout Buffer Adjustment using Adaptive Estimation of Network Delays VoIP Playout Buffer Adjustment usng Adaptve Estmaton of Network Delays Mroslaw Narbutt and Lam Murphy* Department of Computer Scence Unversty College Dubln, Belfeld, Dubln, IRELAND Abstract The poor qualty

More information

Case Study: Load Balancing

Case Study: Load Balancing Case Study: Load Balancng Thursday, 01 June 2006 Bertol Marco A.A. 2005/2006 Dmensonamento degl mpant Informatc LoadBal - 1 Introducton Optmze the utlzaton of resources to reduce the user response tme

More information

CloudMedia: When Cloud on Demand Meets Video on Demand

CloudMedia: When Cloud on Demand Meets Video on Demand CloudMeda: When Cloud on Demand Meets Vdeo on Demand Yu Wu, Chuan Wu, Bo L, Xuanja Qu, Francs C.M. Lau Department of Computer Scence, The Unversty of Hong Kong, Emal: {ywu,cwu,xjqu,fcmlau}@cs.hku.hk Department

More information

Pre-allocation Strategies of Computational Resources in Cloud Computing using Adaptive Resonance Theory-2

Pre-allocation Strategies of Computational Resources in Cloud Computing using Adaptive Resonance Theory-2 Pre-allocaton Strateges of Computatonal Resources n Cloud Computng usng Adaptve Resonance Theory-2 Dr.T. R. Gopalakrshnan Nar 1, P Jayarekha 2 1 Drector, Research and Industry Incubaton Centre(RIIC), DSI,

More information

Multi-Resource Fair Allocation in Heterogeneous Cloud Computing Systems

Multi-Resource Fair Allocation in Heterogeneous Cloud Computing Systems 1 Mult-Resource Far Allocaton n Heterogeneous Cloud Computng Systems We Wang, Student Member, IEEE, Ben Lang, Senor Member, IEEE, Baochun L, Senor Member, IEEE Abstract We study the mult-resource allocaton

More information

A GENERIC HANDOVER DECISION MANAGEMENT FRAMEWORK FOR NEXT GENERATION NETWORKS

A GENERIC HANDOVER DECISION MANAGEMENT FRAMEWORK FOR NEXT GENERATION NETWORKS A GENERIC HANDOVER DECISION MANAGEMENT FRAMEWORK FOR NEXT GENERATION NETWORKS Shanthy Menezes 1 and S. Venkatesan 2 1 Department of Computer Scence, Unversty of Texas at Dallas, Rchardson, TX, USA 1 shanthy.menezes@student.utdallas.edu

More information

Frequency Selective IQ Phase and IQ Amplitude Imbalance Adjustments for OFDM Direct Conversion Transmitters

Frequency Selective IQ Phase and IQ Amplitude Imbalance Adjustments for OFDM Direct Conversion Transmitters Frequency Selectve IQ Phase and IQ Ampltude Imbalance Adjustments for OFDM Drect Converson ransmtters Edmund Coersmeer, Ernst Zelnsk Noka, Meesmannstrasse 103, 44807 Bochum, Germany edmund.coersmeer@noka.com,

More information

AN EFFICIENT GROUP AUTHENTICATION FOR GROUP COMMUNICATIONS

AN EFFICIENT GROUP AUTHENTICATION FOR GROUP COMMUNICATIONS Internatonal Journal of Network Securty & Its Applcatons (IJNSA), Vol.5, No.3, May 2013 AN EFFICIENT GROUP AUTHENTICATION FOR GROUP COMMUNICATIONS Len Harn 1 and Changlu Ln 2 1 Department of Computer Scence

More information

Mining Multiple Large Data Sources

Mining Multiple Large Data Sources The Internatonal Arab Journal of Informaton Technology, Vol. 7, No. 3, July 2 24 Mnng Multple Large Data Sources Anmesh Adhkar, Pralhad Ramachandrarao 2, Bhanu Prasad 3, and Jhml Adhkar 4 Department of

More information