High Availability Replication Strategy for Deduplication Storage System

Transcription

1 Zhengda Zhou, Jingli Zhou College of Compute Science and Technology, Huazhong Univesity of Science and Technology, *, Abstact As the amount of digital data gowing explosively, data deduplication becomes an attactive technique to conseve the stoage and netwok equiements fo the mass stoage systems. Howeve, data deduplication achieves data compession at the cost of eo esilience. A high availability data eplication stategy is poposed to impove data availability fo the deduplication stoage systems. Fistly, data availability optimization model is poposed to calculate the optimal eplication degees fo the individual data object. Then seveal acceleation techniques ae poposed to educe the computational cost of the model and make the poposed scheme feasible and effective. Lastly, the evaluation expements demonstated the poposed stategy has impoved the data availability significantly fo the deduplication stoage systems.. Intoduction Keywods: Deduplication, Data Availability, Stoage System As the volume of stoed digital data expanding explosively, stoage systems confont challenges to stoe and manage the massive digital data in an efficient way. By the yea of 00, the amount of digital infomation will be 44 times as lage as it was in 009. [] To meet the exponentially inceasing stoage equiement, the deduplication technology povides a solution to the stoage efficiency issues fo moden stoage systems. The stoed data in the stoage systems contains plenty of duplicates: identical files o sub-file egions, and obviously, eliminating the duplicates in stoed data can educe the stoage space ovehead and impove the stoage efficiency. The deduplication technology is a specialized data compession technique to impove stoage and netwok utilization. The stoage system divides the data into non-intesecting chunks o stoage objects and assigns the digital fingepnts by the cyptogaphic hash function, typically SHA o MD5, befoe stong the data on the physical media. Consequently, the chunks o stoage objects which thei contents ae identical ae supposed to shae the same digital fingepnt, and then the duplicated chunks o stoage objects can be detected and eliminated easily. Howeve, the deduplication stoage system achieves stoage space savings at the cost of data availability. The client data is divided into chunks dung the deduplication pocessing, and the duplicate data chunks just stoe one copy. Since a data chunk can be shae by many files o clients, the loss of a single data chunk will possibly bng much moe loss of client data than the case befoe the deduplication pocessing. Theefoe, the diffeent data chunks have diffeent influences on the availability and integality of the total data set. Specifically, the data chunks with a highe commonality tend to have moe influences on the total data set. In this pape, ou objective is to achieve an optimum level of data availability at minimum stoage space ovehead. We ague that it is an efficient method to impove the oveall data availability by exploiting the statistical chaactestic of the commonality of the data chunks in the deduplication stoage system. To achieve this, we build a data availability optimization model in the context of the deduplication stoage system. And we poposed some acceleation techniques to impove the feasibility and efficiency of the poposed scheme. The emainde of this pape is oganized as follows. The next section discusses the motivation fo ou eseach. Section 3 poposes a novel eplication scheme fo the deduplication stoage systems. Then, section 4 poposes some acceleation techniques to impove the feasibility and efficiency of the poposed scheme. The section 5 pesents the expemental methodology, datasets and discusses the expemental esults. Section 6 povides some elated woks. Section 7 concludes the pape. Advances in infomation Sciences and Sevice Sciences(AISS) Volume4, umbe8, May 0 doi: 0.456/AISS.vol4.issue8.5 5

2 . Motivation The deduplication technology helps the stoage system to incease stoage utilization by eliminating data edundancy. As the duplicates in data ae eliminated, the stoage systems have educed the actual stoage capacity demands, and the eduction of the TCO (Total cost of owneship) of stoage infastuctue becomes the immediate benefit. Howeve, the deduplication technology achieves high stoage space efficiency at the cost of data availability. The issue of data availability becomes moe inteesting when the taget datasets pefomed deduplication pocessing. The duplicate data chunks can be detected by thei fingepnts and only stoe one copy on the physical media. Thus the data chunks actually eoganized in a patially ovelap way. Obviously, the loss of a data chunk may bng dispopotionately lage client data o files unavailability. In othe wod, the moe files ae efeed to a specific data chunk; the moe loss bngs to the total dataset if the data chunk is unavailable. The figue illustates the poblem. Chunks Chunks Refeences A 3 File A B C B File D A B C A E F D File 3 E F Deduplication Figue. Illustation of elationship between files and data chunks in deduplication stoage systems As shown in the Figue, File, File and File 3 ae divided into chunks which ae ecoded with capital lettes A to F. Afte the deduplication pocess, the data chunks ae stoed on the physical stoage media. If the data chunk A is lost, all the thee files ae unavailable. Similaly if the data chunk C is lost, only the File is unavailable. Thus, the data chunks ae not equally valuable in the tems of data availability. Theefoe, we conside finding a way to impove the oveall availability of the datasets stoed in a deduplication stoage system by exploiting the commonality of data chunks. 3. A novel data eplication scheme As the failue is inevitable, data eplication is the common solution to impove availability is to keep the data edundant. The poposed scheme aims at maximizing data availability at minimal stoage space ovehead by calculating the optimal eplicated degee fo evey individual data object in deduplication stoage system. The poposed scheme is suitable fo both file-level deduplication and chunk-level deduplication. The basic deduplication units, files fo file-level mode and chunks fo chunk-level mode, ae efeed to as stoage object in the pape. The poposed scheme assumes that stoage node failues ae independent and identical distbuted. The eplicas ae stoed in a distbuted stoage systems andomly, and no two same eplicas ae stoed in the same stoage node. All the stoage nodes ae supposed to shae the same availability distbution, which implies that the expected availability of a stoage node is a constant in the model. Moeove, the failue event occus on a stoage node independently and andomly, hence the binomial distbution can be employed to detemine the pobability of obtaining exactly i available stoage nodes in all the stoage nodes which a specific data object ae stoed on. This model only consides the conventional eplication case; hence the data object is available if at least one eplica can be accessed. The availability function fo an individual data object can be given as follows: A k C i 0 k i k i ( ) i () whee μ is the availability of a stoage node and k is the numbe of eplicas. The availability function can be used to calculate the availability of an individual data object in the distbuted stoage system by giving the eplication degee k. 6

3 In a deduplication stoage system, a data object is pobably shaed by a numbe of files o diffeent client due to deduplication pocessing. Accoding to the analysis above, the data objects ae not equally valuable in tem of the availability of data set. A lost data object with a highe commonality causes moe data lost than the one with a lowe commonality. In ode to evaluate the value of a data object in deduplication stoage system, we define a cost function as follows: () P (k ) ( A ) whee the total numbe of files and the numbe of efeences ae denoted by and espectively; the facto (-A) is the pobability the data object with k eplicas ae unavailable, and the facto / is the weight based on the data object commonality in the data set. Theefoe, the cost function fo the data set can be given as follows: (3) P ( k, k k ) i ( A i ) The individual data object size is given and denoted by s, hence the total stoage space ovehead S can be computed as follows: S k i si (4) Then, the poblem is how to find the value of k fo each data object to minimize the cost function by given a toleant stoage space ovehead. Fo this pupose, we employ the Lagangian multiplie method to deve the solution of the poblem. Fistly, the maximum value of S is given and denoted by Smax. Then, one Lagange multiplie λ is intoduced, and the Lagange function is constucted to incopoate the cost function and the constaint condition togethe as follows: (5) ( k, k k, ) i ( Ai ) ( k i si S max ) i ( ) ki ( k i si S max ) The ctical point of Λ is obtained when its gadient is equal to 0. + equations ae obtained as follows: k k ( ) ln( ) s 0 k k ( ) ln( ) s 0 k i si S max 0 As above, fo the vey k in (6), the following equations can be satisfied si ( ) k i ln ( ) k i lo g Let L ( ) lo g ln ( ) (6) (7) si lo g ln ( ) (8) Applying (8) to the constaint condition in (6), we have the following equation: s S m ax s i k i s i ( L ( ) lo g i ) The oginal total stoage space ovehead is denoted by Sog. Sog can be computed as follows: S og s i (9) (0) Combining (9) to (0), we can obtain the following equation to calculate L ( ) : 7

4 L ( ) S max S og s i log si () S og Applying () to (7), we obtain the solution of k, k i log si S max S og s log i si () S og Accoding to the equation (), the solution of k consists of thee pats. The fist pat is log-usi/, which depends on the popeties of the data object i. (the value ofμis egaded as a constant in the model as mentioned above) The second pat Smax/Sog is the edundancy degee which indicates the stoage space ovehead fo the eplication elative to the oginal stoage space ovehead. The value of this pat is egaded as a constant when the constaint condition in (6) is detemined. The thid pat depends on the popeties of the tageted dataset. The influence of the popeties of an individual data object to the value of the thid pat is consideably small, thus, it can be egaded as a constant when calculating k fo an individual data object. Both the cost function (3) and the constaint condition in (5) ae stctly convex. Theefoe, the solution is globally minimal since a local ctical point of a stctly convex function is also its global minimal point. 4. Acceleation techniques The optimization model can obtain the set of optimal eplication degees fo each data object theoetically, howeve, this model meet some challenges to be employed by actual systems fo some easons. Due to the lage scale of the dataset in an actual stoage system, the model can impose a heavy buden to the system computational equiement. If the computational complexity is not impoved, the negative effects on system pefomance can make the model impactical in many applications. In ode to elieve computational buden fo implementation of the model, we popose two techniques below. 4.. Sample-based leaning algothm The pupose of the sample-based leaning algothm is to educe computational buden by exploiting statistical chaactestics of the data objects. Accoding to the equation (), the value of the thid pat depends on the statistical chaactestics of the tageted data set. In othe wod, the value can be egad as a constant, if the distbution of object size and efeence is stationay. Based on the obsevation, ou idea is to sample a sufficient subset fom the tageted dataset to apply the model instead of the whole dataset. The othe data objects can estimate thei optimal eplication degees by empical data. Because only the sample dataset is applied to the model, the poposed algothm can obtain optimal eplication degees fo each data object at much lowe computational cost. Choosing a pope subset fom the taget dataset is impotant to the poposed algothm. The pope subset must be a epesentative subset which shaes the same statistical distbution with the entie data set. Specifically, the empical distbutions of the object size and the efeence ae obtained fom the subset must appoximate the tue distbutions of entie data set adequately, o else the excessive sampling eos can lead to the inaccuate esults eventually. To educe the sampling eos, the sample method employed by the poposed algothm must satisfy two equiements. Fistly, the samples must be chosen andomly and independently, ensued that the empical distbution conveges unifomly to the tue distbution. Secondly, the sample size must be sufficient in ode to educe the sample eos. In the deduplication stoage system, the data is divided into data objects which ae assigned the IDs by hash functions. Because the employed hash functions ae pseudo andom numbe geneatos, the object IDs ae mapped to addess ange unifomly. Theefoe, the andom and independent sample can be implemented easily by pefix-based ID sampling method. The object ID which is a hash key geneated by hash function can be viewed as fixed-size stngs of bits in the space coveng all possible combinations of such stngs. A pefix can be used to select a sample zone. If the object ID is a -bit key, then a given M-bit pefix ( M<) 8

5 p p pm (whee p denotes a specific bit )is used to detemine the sample zone whee all the object ID stating with this pefix, i.e. of the fom p p pm bm bm b whee b denotes a vaable bit. The pefix-based ID sampling method has two advantages. Fistly, since the object ID follows the unifom distbution, the method is efficient and simple. Secondly, most distbuted stoage systems also employed the pefix-based method to distbute the data objects, hence this method make the poposed algothm moe feasible and scalable. To detemine a pope sample size, we poposed an eo contol method. Accoding to the equation (), only the thid pat depends on the statistical distbution of the data set. Hence, we define an obseved paamete T as follows: n s s i log i (3) T ( n ) n si whee the sample size is denoted by n. As the n gows lage, the value of T conveges to the T(), whee is the total numbe of data objects in the data set. Ou goal is to obtain appoximate value of T() unde a toleate eo. We poposed a heustic algothm to find the pope appoximate value of T() by an optimal sample size. The poposed algothm estimates the value of T() by the moving aveage and vaance analysis in a successive appoximation appoach. Figue. The Flowchat of the poposed heustic method Figue shows the flowchat of the poposed method. The method is summazed as follows: Step : Initialize the paametes. The initial sample size and the step size ae given and denoted by n0 and m espectively; hence, the ith sample size is ni=n0+m*i. Step : The size of moving aveage window is denoted by w. T(ni) ae calculated by (3). Step 3: Calculating the simple moving aveage of the subset T(n0),T(n) T(nw-) by fomula as follows: w MA T (n i ) (4) w Step 4: Calculating vaance by fomula as follows and compaed with theshold K w [T ( n i ) MA ] (5) w If is less than K, the optimal sample size is set to nw- and the appoximate value of T() is set to T(nw-). Othewise n0 is incemented by n0 = n0+m and the new T(nw-) is calculated by (3), then go 9

6 to the step 3 and the MA and is calculated epeatedly. The poposed heustic method impoves the scalability and feasibility of the optimization model significantly. 4.. Fast decision table The fast decision table is anothe method to educe computational buden of the optimization model. Afte the statistical chaactestics of the tageted data set is obtained by sample-based leaning algothm, othe data objects in tageted dataset estimate thei optimal eplication degees by the equation (). Accoding to analysis above, only the fist pat in the equation () is an individual deviation pat, thus the fomula used to calculate optimal eplication degees can be wtten as follows: s K ( s, ) log C (6) whee C is a constant. Since the amount of data objects is vey lage, calculating the optimal eplication degees fo evey data objects is still a heavy computing task. To educe the computational complexity futhe, we poposed an optimized method which applies a fast decision table to lookup the optimal eplication degee instead of calculating diectly by the equation (). The poposed method has the advantage ove the calculating method. Due to the computational complexity of the logathm calculation fo the equation (), the fast decision table can achieve the same accuacy by lookup opeations at a lowe computational cost. In addition, the calculating method can bng in the ound-off eos and epesentation eos to the intemediate esults easily, which can accumulate in the ill-conditioned case and make the esult meaningless. To establish the fast decision table efficiently, the poposed method must satisfy two equiements. Fistly, the table ought to achieve adequate accuacy in value estimation. Secondly, the size of the table ought to be small enough in the context of lage-scale dataset, so that the stoage system can affod the stoage ovehead of the table and the computational cost of lookup opeations. Actually, the fast decision table is a feasible and efficient method. On the one hand, since only the conventional eplication is consideed fo data edundancy scheme, the feasible solutions of the optimal eplication degees ae intege. The efeed calculations in the model employ float point calculation and the ound-off opeation can achieve adequate accuacy easily. On the othe hand, the fomula (6) is monotonic fo both the individual data object size s and the numbe of efeences, which facilitates the ceation of the fast decision table. The poposed method does the following steps to establish the fast decision table: Step : Set the ange of the final solutions. The lowe bound of the solution is theoetically. And an uppe bound is given by U accoding to the system equiements. Obviously, the final solution is a intege in the ange of [,U]. Step : Calculate the coesponding value of s/ fo evey possible solution by the invese function of (6). The fomula can be wtten as follows: s (, ) ( ) K s C (7) Step 3: Choose a subset R fom value ange of by a binay logathmic scale. Hence the subset of is {,,4,8. }. The maximum of is in the ange of [ -, ]. Step 4: Calculate the coesponding value of s by given and s/; and establish the fast decision table. Step 5: Mege adjacent inteval of and s which shae the same eplication degee. The method to establish the table is pesented above. In addition, the method can be optimized futhe by exploiting the statistical distbution of the and s. Since the optimization depends on the intnsic popety of the data-set itself, we do not discuss this issue in detail hee. 5. Evaluation In ode to evaluate the feasibility and effectiveness of the poposed scheme, we built a simulato that allows us to expement with some impotant paametes. The simulato can pefom both file level and chunk level deduplication on taget data sets. The simulato applies the SHA- algothm to geneate the data object IDs and goup them in the pefix-based method. In the expements, the goups 0

7 of data objects ae supposed to be diffeent stoage nodes and the goup IDs ae set to the coesponding pefix. The simulato can eplicate the data object in successive goups, fo instance, if the data object t with eplication degee n is distbuted in goup G, the simulato eplicates it fom the goup G+ to goup G+n-. We apply the simulato to two ealistic data sets in the expements. The fist data set was collected all the document files in the pdf, doc, txt fomats and the media files in mp3, avi, mkv and mbv fomats fom the desktop PCs of 30 gaduate students in ou eseach team. Thee ae 00,83 files which amount to a total of.86tb data in the data set. The second data set was collected fom the backups of souce code ove the couse of two yeas in the lab. The backup policy is to do full backups if a new vesion is developed. Thee ae.7 million files which amount to a total of data in the data set. The fist data set was employed to the simulato in file-level depduplication mode and then pefomed the poposed scheme. Fo the compason, the unifom eplication method which all the data objects shaed the same eplication degee was also pefomed on the data set. Figue. The ate of available files compason in file depduplication mode Fo evaluating the effectiveness of the poposed scheme, we compae the ate of available files unde the same numbe of lost stoage nodes. The edundancy degee R is set to o 3 in the expements. In the figue, the vetical axis epesents the aveage client file lost ate and the hozontal axis epesents the numbe of unavailable stoage nodes. Fo a specific numbe of unavailable stoage nodes, thee can be seveal possible combinations. In the expement, the total numbe of the stoage nodes was set to 8 and all the possible combinations of lost nodes enumeated exhaustedly and the aveage client file lost ates ae summazed by athmetic means. (fo evey possible combination ae equally pobable.) As shown in the figue, the aveage client file lost ates fo the poposed scheme is lowe than the ate fo the unifom eplication in most case. Howeve since the eplication degees of some files fo the poposed scheme ae less than the edundancy degee R, the aveage client file lost ates fo the poposed scheme ae a bit highe than the lost ates fo the unifom eplication when the numbe of unavailable stoage nodes is less than R. In the expement, we measue the actual stoage ovehead fo both eplication schemes. Table compaes the actual stoage ovehead (afte deduplicaton pocess) fo the two eplication schemes unde diffeent edundancy degees. The data in the table shows the space ovehead of two schemes have toleated diffeence. Table. The actual stoage ovehead fo the two eplication schemes Redundancy degee The poposed scheme The unifom eplication Δ ΔRate 05.00% 05.8% 04.07% The second data set was employed to the simulato in chunk-level deduplication mode and then pefomed the poposed scheme. Similaly, fo the compason, the unifom eplication method was also pefomed on the data set fo the compason.

8 Figue 3. The ate of available files compason in chunk-level depduplication mode In the figue 3, the vetical axis epesents the aveage file lost ate and the hozontal axis epesents the numbe of unavailable stoage nodes. Since the file is split into many chunks fo deduplication pocessing, one chunk lost can cause the whole file useless. Theefoe, the file availability in chunk-level deduplication is moe vulneable. In the expement, the total numbe of the stoage nodes was set to 56, and in ode to educe the computation cost and make the expement feasible, we chose 00 possible combinations andomly fo evey lost node numbe and summazed the file lost ates by athmetic means. As shown in the figue 3, the file lost ates fo the poposed scheme is lowe than the ate fo the unifom eplication in most case. Table below compaes the actual stoage ovehead fo the two eplication schemes unde diffeent edundancy degees. The data in the table shows the space ovehead of two schemes have toleated diffeence. Table. The actual stoage ovehead fo the two eplication schemes Redundancy degee The poposed scheme The unifom eplication Δ ΔRate 03.% 03.5% 0.97% Expemental esults above show that the poposed scheme can impove the data availability at the simila stoage ovehead unde both the file-level deduplication mode and chunk-level deduplication mode. 6. Related woks As data have been gowing explosively, the data deduplication technology widely employed in the moden stoage systems. A numbe of solutions have been poposed and developed in both industy and academia. Venti[] is a content-addessable netwok stoage system. Venti povides inheent integty checking mechanism fo the data couption detection and employs RAID to impove the data eliability. HYDRAsto[3] is a moe ecent commecial implementation of a content addessable stoage deliveng global deduplication. HYDRAsto impoves the data eliability by employing the geneal eplication and easue coding methods. All the data in the two systems shae the same data edundancy scheme simply egadless of the popeties of data itself. Deep stoe[4] is also a block-level stoage system fo achival data, which employs a vitual content addessable stoage with multiple methods fo inte-file and inta-file compession. The pape [4] discussed side effects of data compession on data eliability which is vey simila to ou eseach, and poposed a eplication scheme based on chunk weight. In the pape [5], a high eliability povision mechanism fo lage-scale deduplication achival stoage systems is poposed. It packs vaable length data chunks into fixed sized objects, and exploits ECC codes to encode the objects and distbutes them among the stoage nodes in a edundancy goup. Howeve, without a theoetical model, the eplication scheme mentioned in the [4] and [5]

9 cannot obtain the optimal eplication degee fo evey individual data chunk. The pape [6] has outlined a few eliability analysis poblems that ase fom the deduplication of a easue-coded key-value stoe. Howeve, they have not discussed the solutions in detail. Besides, thee ae seveal othe studies have investigated on deduplication techniques in the academia. Some of them focus on fast duplicate data detection techniques[7], some of them focus on paallel deduplication pocessing[8][9], and some othes focus on content defined chunking techniques[0] []. But most of them ignoed the data availability issues in deduplication stoage and leave the poblem to the opeation systems o hadwae simply. Unlike the mentioned elated woks above, we focus on the data availability issues in deduplication stoage systems and popose the optimized schemes by exploiting the popeties of data itself. Analyzed both data compession and data edundancy synthetically, the poposed scheme aims to achieve high data availability at the least stoage ovehead. 7. Conclusion A data availability optimization model in the context of deduplication stoage system is pesented in this pape. The novel eplication scheme based on the model is poposed to the oveall data availability by exploiting the statistical chaactestic of the data object commonality fo the deduplication stoage system. In ode to make the scheme moe feasible, seveal fast algothms ae poposed to elieve computational buden of the poposed scheme. To vefy the feasibility and effectiveness of the poposed scheme, we pefomed the evaluation expements and the expemental esult shows that the data availability has significantly impoved by ou poposed stategy. 8. Refeences [] "The Digital Univese Decade Ae You Ready?" IDC white pape, May 00. [] Sean Quinlan, Sean Dowad. "Venti: a new appoach to achival stoage", In Poceedings of the st USEIX confeence on File and Stoage Technologies, pp.89-0, 00. [3] Cezay Dubnicki, Leszek Gyz, Lukasz Heldt, Michal Kaczmaczyk, Wojciech Kilian, Pzemyslaw Stzelczak, Jezy Szczepkowski, Cstian Ungueanu, Michal Welnicki, "HYDRAsto: a Scalable Seconday Stoage", in Poceedings of the 7th USEIX Confeence on File and Stoage Technologies, pp.97-0, 009. [4] Deepavali Bhagwat, Kstal Pollack, Daell D. E. Long, Thomas Schwaz, Ethan L. Mille, "Poviding High Reliability in a Minimum Redundancy Achival Stoage System", in Poceedings of the 4th IEEE Intenational Symposium on Modeling, Analysis, and Simulation of Compute and Telecommunication Systems, pp. 43-4, 006. [5] Chuanyi Liu, Yu Gu, Linchun Sun, Bin Yan, Dongsheng Wang, "R-ADMAD: High eliability povision fo lage-scale de-duplication achival stoage systems", In Poceedings of the 3d intenational confeence on Supecomputing, pp , 009. [6] Xiaozhou Li, Mak Lillibdge, "Reliability Analysis of Deduplicated and Easue-Coded Stoage", ACM SIGMETRICS Pefomance Evaluation Review, vol.38, no.3, pp.4-9, 0. [7] Benjamin Zhu, Kai Li, Hugo Patteson, "Avoiding the Disk Bottleneck in the Data Domain Deduplication File System", In Poceedings of the 6th USEIX Confeence on File and Stoage Technologies, pp. 69-8, 008. [8] Wei Dong, Fed Douglis, Kai Li, Hugo Patteson, Sazzala Reddy, Philip Shilane, "Tadeoffs in scalable data outing fo deduplication clustes", In Poceedings of the 9th USEIX Confeence on File and Stoage Technology, pp.5-9, 0. [9] Yujuan Tan, Dan Feng, Fangting Huang, Zhichao Yan, "SORT: A Similaty-Owneship Based Routing Scheme to Impove Data Read Pefomance fo Deduplication Clustes", IJACT, Vol. 3, o. 9, pp , 0 [0] Ek Kuus, Cstian Ungueanu, Cezay Dubnicki, "Bimodal content defined chunking fo backup steams", In Poceedings of the 8th Confeence on File and Stoage Technologies, pp. 8-3, 00. [] Jiansheng Wei, Ke Zhou, Lei Tian, Hua Wang, Dan Feng, "A Fast Dual-level Fingepnting Scheme fo Data Deduplication", JDCTA, Vol. 6, o., pp. 7-8, 0 3