Effective Management of ReRAM-based Hybrid SSD for Multiple Node HDFS

Transcription

1 Iteratioal Joural of Networked ad Distributed Computig, Vol. 3, No. 3 (August 25), Effective Maagemet of ReRAM-based Hybrid SSD for Multiple HDFS Nayoug Park College of Iformatio ad Commuicatio Egieerig Sugkyukwa Uiversity Suwo, Korea parky42@skku.edu Byugju Lee College of Iformatio ad Commuicatio Egieerig Sugkyukwa Uiversity Suwo, Korea byugju@skku.edu Kyug Tae Kim College of Iformatio ad Commuicatio Egieerig Sugkyukwa Uiversity Suwo, Korea kyugtaekim76@gmail.com Hee Yog You College of Iformatio ad Commuicatio Egieerig Sugkyukwa Uiversity Suwo, Korea you747@skku.edu Abstract Recetly, the research of Hybrid ReRAM/MLC NAND SSD is rapidly expadig ito the storage areas. Most existig researches of Hybrid SSD are based o a sigle storage, while the maagemet of multiple odes like HDFS is still immature. I this paper a ew efficiet cold data evictio scheme is proposed which is based o ode cogestio probability. Computer simulatio reveals that the proposed scheme sigificatly reduces replicatio ad recovery time i compariso to the existig replicatio schemes. Keywords: HDFS, hybrid SSD, ode cogestio probability, replicatio, cold data evictio. Itroductio The demad o efficiet storage for cloud computig has bee growig drastically year after year. Rather tha relyig o traditioal cetralized storage arrays, the storage system for cloud computig cosolidates a large umber of distributed commodity computers ito a sigle storage pool. It provides large capacity ad high performace storage service eve i ureliable ad dyamic etworkig eviromet at low cost. I buildig the cloud storage system, icreasig umber of idustries ad research istitutios rely o the Hadoop Distributed File System (HDFS) []. HDFS provides reliable storage ad high throughput access to the data. It is suitable for the applicatios maipulatig large data sets, typically the oes employig a replicatio maagemet scheme for data-itesive computig. HDFS has bee widely used as a commo storage appliace for cloud computig. Oe of the cospicuous treds i recet storage system is the emergecy of SSD. SSD-based storage is becomig a promisig techology for ext-geeratio storage due to a umber of reasos icludig low access 67

2 latecy, low power cosumptio, higher resistace to shocks, light weight, ad icreasig focus o edurace. Due to the iheret merits of solid state device, SSD ca provide better I/O performace compared with the traditioal HDD. Recetly, a large umber of Iteret service providers have started to replace HDD i their data ceters with SSD [2]. I additio, the fallig cost of NAND flash-based SSD is drivig its expasio ito the market previously reserved for HDD. Fig. shows the architecture of SSD. HostIterface DRAM SSD Cotroller Flash Traslatio Layer Wear Levelig Garbage Collectio Nad Flash Nad Flash Nad Flash Nad Flash Fig.. The block diagram of a traditioal SSD architecture. With the mometum leveraged by both persoal computig ad eterprise system, SSD has bee recogized as a viable choice to build high-performace storage. I order to satisfy the strict requiremets o the future storage system of higher speed, reliability ad eergy-efficiecy, SSD is preferred. Various methods cotributig to the advacemet of the SSD techology have thus bee proposed. Recetly, a hybrid ReRAM/MLC NAND SSD employig the cold data evictio (CDE) algorithm was proposed [3]. It dyamically evicts cold pages from ReRAM to MLC NAND to store hot data. Whe the free space of ReRAM drops below a threshold (for istace, 2% of the ReRAM capacity), evictio is triggered. With this, the page-level migratio is dyamically hadled which is trasparet to the file system. Most existig schemes cosider the evictio based o oly the state of each ode separately. Note, however, that cold ad hot data are dispersed i the odes of Hadoop cluster system. As a result, the performace of the storage system caot be maximized with this approach. Furthermore, it causes uecessary waitig time to the users. If several odes i the HDFS trigger the evictio process simultaeously while may jobs arrive, the users eed to wait for the completio of the evictio process. I this paper, thus, we propose a ew scheme of cold data evictio based o the ode cogestio probability for HDFS. The proposed scheme aalyzes the ode cogestio probability with which the system ca decide if the ode is available to execute the evictio process or ot. I additio, a ew page search approach is proposed, which is importat for high performace HDFS service. Through comprehesive computer simulatio, it is cofirmed that the proposed scheme sigificatly decreases the executio time ad recovery time of replicatio i the HDFS eviromet i compariso to the radom evictio approach, the scheme employed i HDFS, ad the CDE algorithm. The rest of the paper is orgaized as follows. I Sectio 2 brief explaatio of data replicatio with HDFS ad hybrid SSD are provided. Sectio 3 presets the proposed cold data evictio scheme, ad Sectio 4 verifies its performace by computer simulatio. Fially, Sectio 5 cocludes the paper ad suggests the future course of study. 2. Related Work I this sectio the previous work relevat to data replicatio with HDFS ad Hybrid SSD are discussed. 2.. Data Replicatio with HDFS I order to tolerate failure i cloud computig system, various data replicatio techiques have bee proposed. I the cloud computig eviromet, data resources are geographically scattered, ad thus etworkig delay has bee a major obstacle i rapid data access. Numerous studies have bee udertake to replicate data i several data storages that are physically distributed ad as a result reduce the amout of log-distace data trasmissios over the etwork. Fig. 2 describes the structure of data replicatio with HDFS. The data replicatio strategies ca be categorized by the types, uits, ad criteria of replicatio [4]. I terms of replicatio type, there are two types: static ad dyamic. The former is ieffective for large-scale cloud data service because it statically maages data replicatio. It is icapable of quickly respodig to various etwork coditios ad chages i the data access patter. For this reaso, the studies o dyamic data replicatio have bee actively coducted [5, 6]. [7] suggested replicatio strategies reducig the etwork badwidth ad access delay. They also compared the performace of data access patters 68

3 categorized by time ad spatial locality. By cosiderig the etwork capacity ad file access patter, [8] proposed a file replicatio algorithm improvig the performace of basic replicatio method. Similarly, [6] proposed the Latest Access Largest Weight (LALW) method, which uses data access history i dyamically determiig the replicatio policy by applyig greater weight to more recet access. [9] proposed a dyamic optimal replicatio strategy (DORS) which evaluates the value of a file based o the access history, size, ad the etwork coditio i decidig the target files of replicatio. Network Cliets Some researches proposed the strategies for data replicatio focusig o cost-savig. [] proposed a service that applies the market ecoomy model to miimize the replicatio ad data access cost o a data grid. [] suggested a data replicatio strategy based o a cost-estimate model that cosiders both the cost of data access ad the performace of replicatio. I order to esure efficiet resource maagemet i a upredictable data grid eviromet, [2] developed a model that dyamically selects a resource maagemet strategy that respods to a particular workload type based o the performace history. This model demostrated that a real-time workload type is a importat factor i resource maagemet ad the selectio of data replicatio strategy. Similarly, [3] proposed the File Reuio based o Data Replicatio Strategy for Data Grids (FIRE) scheme, which refers to the file access history of earby storage to determie data replicatio, aimig to supply high quality service i the cloud computig eviromet. A data replicatio strategy is dyamically selected to provide optimal data service, while the least Locality based Data Replicatio Strategy (LDRS) scheme tured out to outperform the LRU Meta Data Cotrol Data Blocks Data s Fig. 2. The structure of data replicatio with HDFS. Name scheme for sequetial access patter havig spatial locality. [4] suggested a ovel cost-effective dyamic data replicatio strategy amed CIR for cloud data ceters which applies a icremetal replicatio approach to miimize the umber of replicas while meetig the requiremet of reliability ad cost-effectiveess. Their approach ca substatially reduce the data storage cost, especially whe the data are stored oly for a short duratio or have a lower reliability requiremet. Noetheless, their approach is based o oly the reliability parameters ad pricig model of Amazo S3 which makes it usuitable for Google cluster of a much higher failure rate tha Amazo S3 storage uits. Moreover, they did ot cosider the issue of the tradeoffs betwee cost ad performace. I [5], a dyamic distributed cloud data replicatio algorithm (CDRM) was proposed to capture the relatioship betwee availability ad the umber of replica. It maitais the miimum replica for the give availability requiremet. The replica placemet is based o the capacity ad blockig probability of data odes. Some researches [6] preset six differet replicatio strategies for three differet access patters: No Replicatio or Cachig, Best Cliet, Cascadig Replicatio, Plai Cachig, Cachig plus Cascadig Replicatio, ad Fast Spread. The mai aim of these strategies is reductio i access latecy ad badwidth cosumptio. [7] proposed a cetralized data replicatio algorithm (CDRA) to reduce the total file access time with the cosideratio of limited storage space of Grid sites. Based o the cetralized algorithm, they also desiged a distributed cachig algorithm wherei the Grid sites react close to the Grid status ad make itelliget cachig decisios, which ca be easily adopted i a distributed eviromet such as Data Grids. Their approach ca reduce the aggregated access delay to data files by at least half of that reduced by the optimal replicatio solutio. The limitatio of the algorithm is the cosideratio of oly the access cost. [8] studied a replicatio algorithm based o a costestimatio model, drive by the estimatio of the data access gais ad the replica s creatio ad maiteace cost. It allows the grid odes to automatically replicate data whe eeded i their Data Grid simulator, GridNet. [9] proposed a dyamic hybrid protocol (DHP) which effectively combies the grid ad tree structure so that 69

4 the overall topology ca be flexibly adjusted usig three cofiguratio parameters; tree height, umber of descedats ad grid depth. It ca easily detect read/write coflict ad write/write collisio for cosistecy maiteace. Some of the existig strategies optimize the umber of replicas, while others optimize the placemet of replicas. Others optimize how ofte replicas should be updated [2]. The shortcomig of them is that they oly cosider a restricted set of parameters affectig the replicatio decisio. Furthermore, they oly focus o the improvemet of the system performace without addressig the eergy efficiecy issue i data ceters. [2] desiged a evolutioary way to decide the optimal replicatio strategy. I that work, they optimize storage latecy ad reliability without cosiderig the total eergy cost of data ceter ad the issue of load balacig Hybrid SSD The hybrid SCM/MLC NAND flash SSD (hybrid SSD) is a promisig solutio i boostig the performace of the SSD-based storage while maitaiig the cost. Fig. 3 describes the etire structure of the system implemeted with SSD. SCM operates as both cache ad storage, ot simply as a cache/buffer for the NAND flash memory or merely as storage because it is both fast ad o-volatile. O the host side, the applicatio layer exists above the operatig system, uder which the block device layer resides, which i tur is above the iterface of the host ad storage. The hybrid SSD icludes SSD cotroller, SCM chip array, ad NAND flash memory chip array. Here, MLC NAND flash is preferred to sigle-level cell (SLC) NAND flash to lower the overall cost of the hybrid SSD. As the brai of the storage system, SSD cotroller rus complicated algorithms hadlig the characteristics of erase-before-write ad limited edurace of NAND flash. Withi the cotroller, the data maagemet module determies whether the target data are stored i SCM or i NAND flash memory based o the operatio o the data ad the status of memory. The address traslatio module maages the logical-to-physical address mappig to provide a logical block iterface to the SSD. The wear levelig module guaratees eve wear amog the storage cells to maximize the system logevity. Additioally, the garbage collectio module reclaims free space i the NAND flash whe the NAND flash blocks are almost full. Through the cotroller fuctios, both the performace ad lifetime of SSD ca be ehaced. Fially, the error code correctio (ECC) module detects ad corrects the errors iside the SSD. The role of ECC is becomig more critical as the size scales up, causig degraded reliability. With icreased umber of memory chips, the overhead o the chip area of SSD grows due to the icreased bus area. It is assumed that SCM has much higher reliability tha NAND flash memory. Therefore, oly a pair of simple BCH ECC ecoder/decoder is required for SCM but dozes of low-desity parity-check (LDPC) ECC ecoder/decoders are required for NAND flash. As a result, the area overhead for SSD cotroller due to the icreasig umber of SCM chips is small. I additio, the area for cotrol logic of SCM is egligible. The proposed replicatio scheme for ReRAM-based storage is proposed ext. Hybrid SSD SSD cotroller SCM cotrol Address traslatio ECC NAND flash I/F SCM chips Wear levelig Applicatio layer Operatig system Block device layer SATA,SAS,PCI_e... Data maagemet Chael File system Address traslatio Garbage collectio NAND flash NAND cotrol ECC NAND flash I/F Chael N Wear levelig NAND flash Fig. 3. The overall structure of the whole computer system with SSD. 7

5 3. The Proposed Scheme This sectio proposes a scheme evictig cold data for the HDFS based o the hybrid SSD. It cosists of three parts; evictio of cold data, estimatio of ode cogestio probability used i the cold data evictio, ad page search for fidig cold/hot pages. 3.. System Model Algorithm : Process of cold data evictio The proposed scheme targets the developmet of a replicatio maagemet approach for HDFS system. The HDFS has umerous similarities with the proprietary distributed file system, Google file system. It cosists of a sigle ame ode ad a set of data odes. The ame ode ad data odes are deployed withi a umber of racks as show i Fig. 4, each of which has a associated rack umber. Cotrary to the existig HDFS, i the proposed scheme the ame ode maily maages the amespace of the file system ad the locatio of data pages (the mappig of data pages to data odes). A file is split ito oe or more data pages which are dispersed i the data odes. I Hadoop, the applicatios are executed i data odes. Whe a applicatio eeds a data page, it acts as a HDFS cliet sedig a page read (write) request to the ame ode. The ame ode fids the requested data ode to process the request. Each data ode also periodically seds a heartbeat message to the ame ode to otify its soudess. I HDFS, the umber of replicas is set to three, which is also adopted i this paper. By usig NRU (Not Recetly Used) table, the proposed scheme decides if a data page is hot or cold. Rack Rack 2 Name The overall process of the proposed scheme is give i Fig. 5. The proposed replicatio scheme is triggered if the free space of ReRAM of etire odes drops below a threshold (for istace, 2% of the total ReRAM capacity), the free space of ReRAM of a sigle ode is less tha %, ad the ode cogestio probability (CP) is lower tha the threshold, Tcp. Rack 3 Data Fig. 4. The structure of HDFS. Rack 4 Rack 5 Switch Rack : 2: 3: 4: 5: 6: 7: 8: 9: : : 2: 3: 4: 5: New write data if Total free space i ReRAM < 2% Go to Lie 6 if Sigle ode free space i ReRAM < % if the ode cogestio probability > CPTHRESHOLD if Flag_NRU == (Hit NRU table?) if R<RRM Add page to cold data evictio list else Go to Lie 5 if NRM == RMEXECUTION Executio of cold data evictio else Go to Lie 5 else Search page i ReRAM Fig. 5. The overall process of the proposed scheme. To judge whether a page is hot or cold, the NRU table is referred. Because the write performace is critical to the NAND-based storage system, the proposed algorithm was desiged to optimize the operatio of SSD write request rather tha read request. Here the page utilizatio, U, is compared with the utilizatio threshold, Tu, to choose the least used page for replicatio. If it is impossible to select a page i ReRAM whe cold data evictio is triggered, RRM is reduced to icrease the umber of cadidate pages i ReRAM used i the ext evictio process. Fig. 6 compares the proposed scheme with the existig schemes. I the MLC NAND-oly SSD i Fig. 6(a), all pages are stored i SSD, regardless hot/cold, fragmeted/ot fragmeted. I the covetioal hybrid SSD (Fig. 6(b)) fragmeted pages are set to ReRAM [22]. Sice fragmeted cold data decreases free space of ReRAM, fragmeted hot data might be stored i MLC NAND. As a result, the SSD performace will be greatly degraded if this situatio gets worse or the ReRAM capacity is relatively small. I order to provide high performace uder itesive fragmeted cold data workloads, a ew cold data evictio scheme is proposed. Fig. 6(c) describes that the portios of cold data i ReRAM are evicted to MLC NAND flash. By 7

6 dyamically evictig the fragmeted cold data pages from ReRAM to MLC NAND, ReRAM ca effectively store hot data. Furthermore, fragmetatio i MLC NAND is miimized by the evictio of mostly fragmeted cold data page. MLC NAND oly Host SSD cotroller MLC NAND Covetioal Hybrid SSD Host SSD cotroller ReRAM MLC NAND Data Hot Cold Frag Not frag Proposed Hybrid SSD Host SSD cotroller ReRAM MLC NAND k CP[] i = ωi ACK[]. i ECN -Echo () k i= where ACK[i].ECN-Echo is the value of the CE bit k i ACK[i], ad w i are ormalized such that ωi =. i= The value of CP(t) is calculated usig k serial ACKs received util time t, deoted by ACK[i], ACK[i ],,ACK[i k+], respectively. For the sake of simplicity of presetatio, we use ACK[i] istead of ACK[i].ECN-Echo. The weights of the CE bits i differet ACK packets are assiged usig the expoetially weighted movig average method. The ACKs are divided ito several segmets, ad all ACKs i the same segmet are assiged a same weight. This is because a large umber of ACKs icrease the feedback delay, while a sigle ACK caot correctly reflect the cogestio state. To achieve the tradeoff betwee sesitivity ad stabilizatio, the k ACKs are divided ito segmets, ad the ACKs of segmet_x are assiged with a weight, w x. The value of CP(t) ca be calculated as k ( x ) CP[] i = ω ACK[] i (2) x x= k i= k ( x ) Writte to NAND for ReRAM space shortage 3.2. Cogestio Cold data evictio frees ReRAM space for hot data Fig. 6. The compariso of the proposed scheme with the existig oes. The key idea of cogestio probability based o ECN (Explicit Cogestio Notificatio) is to aalyze the distributio of Cogestio Experieced (CE) bits from the ECN feedbacks ad predict the etwork state accordig to the correlatio betwee multiple feedbacks. Oe ECN feedback reflects the etwork state oly at oe earlier time iterval, while a sequece of ECN feedback ca idicate the dyamic chage of the etwork state durig the past several cotiuous time itervals. Therefore, we propose to combie the iformatio cotaied i the CE bits of a sequece of ACK packets together to predict the etwork state. Assume that a seder receivers a ECN feedback sequece cotaiig k ECN feedbacks. We calculate CP(t), the cogestio probability at the curret time t, as We calculate CP(t) with movig weighted average value of samples. Note that the cogestio probability based o ECN tracks the chage of the CP(t) value to estimate the cogestio level. Geerally, the ewer the ACK packet, the more accurate it is i predictig the curret etwork state. Therefore, a larger weight eeds to be assiged to ewer ACK. The weights of ACKs i each segmet are assiged as ωx = * ωx, α (,) (3) α While all the weights satisfy ωx = (4) x= It is clear that the weight of x th segmet, ω, is larger x tha the weight of previous segmet, ωx, sice the value of α is less tha. I highly dyamic etwork, it is very difficult to get accurate traffic state with oly a smaller umber of 72

7 packets. However, usig large umber of packets will icrease the feedback latecy. Like the way that TCP uses three duplicate ACKs to reflect packet loss, we choose four ACKs for oe segmet to track the cogestio state. After receivig three duplicate ACK packets, the seder eters the cogestio avoidace mode. Similar to this, we choose a period of four ACK packets to compose oe segmet reflectig the cogestio state. Thus, we have k = 4 (5) With Eq. (3), Eq. (4) ad Eq. (5), we ca rewrite Eq. (2) as ( ACK[4] ACK[] ) ωi CP = + ( ACK [8] ACK [4]) 4 α ( ACK[4 ] ACK[4 4]) α ACK[] + ACK[4] α ω i = + ACK[8] 2 4 α α ACK[4 ] α α (8) 4x CP[ i] = ω ACK[ i], i [, k] (6) t x= 4 i= 4( x ) Eq. (6) ca be expaded to 3 CP[] i = ω ACK[] i 4 i= 7 ω + ACK[] i α 4 i= 4 4 ω + + ACK[] i α 4 i= 4( ) (7) If CP(t+) > CP(t), the cogestio probability icreases; otherwise, the cogestio probability decreases. Therefore, the chage of CP ca properly reflect the chage of etwork state. Defiig ΔCP = CP(t+) CP(t) CP(t), we have From Eq. (8), it is clear that ΔCP is related to oly ACK[4i], that is to say, the last ACK of each segmet. We ca also see that the value of ΔCP lies i a discrete set cotaiig 2 + elemets. If the CE bit of the last ACK i the last segmet is marked with, the value of ΔCP will be i [, ]. I this case, it ca be cocluded that the cogestio probability is decreasig, which has the same effect of a sigle ECN feedback. O the cotrary, if the CE bit of the latest arrivig ACK is, the value of ΔCP will be i [, ]. I this case, it caot be determied whether the etwork is goig to be cogested or ot. However, ΔCP always idicates that the etwork cogestio may be relieved regardless of the value of the CE bit of the ewly arrivig ACK. The value of CP is calculated by the procedure show i Fig. 7 Algorithm 2 : Procedure of calculatig CP : CP(t +)=CP(t) 2: it ECN-Echo=hdr_flags::access(pkt) ECN-Echo() 3: float ω, ω 2 4: for i= to m do 5: ACK[i ]=ACK[i] 6: ed for 7: ACK[m ]= ECN-Echo 8: u = ACK[m ]+ +ACK[m/2] 9: u 2 = ACK[m/2 ]+ +ACK[] : CP(t)= ω* u+ ω2* u2 : retur CP(t) Fig. 7. The procedure for calculatig the CP Page Search I searchig cold pages, three approaches are available as show i Fig. 8, (a) From the first page, (b) From radom positio, ad (c) From the usearched page. The umbers i the figure deote the search sequece of ReRAM pages, ad the arrow shows the start poit of the search. For the approach of Fig.8 (c), the start poit is the cold page i ReRAM foud i the previous cold data evictio operatio. The page umber is stored i the memory, ad updated whe the cold data evictio process is triggered. I the proposed scheme the approach of Fig.8 (c) is adopted due to lower search overhead compared with the other approaches. With the adopted search approach, the usearched regio is scaed first. Statistically, the chace of the searched regio to cotai cold page after the executio of cold data evictio will be relatively low. 73

8 By checkig the usearched regio first, the overhead of searchig ca be miimized. We ext evaluate the performace of the proposed scheme. Table. MLC NAND/ReRAM Specificatio ad Used Table MLC NAND ReRAM Previously searched pages Usearched pages Read latecy (max.) 85µs/page s/sector Write latecy(typical) Lower page 4µs Upper page 28µs (Set/Reset) s/sector Erase latecy (typical) 85µs/block Access uit Page(6KiB) Sector(52B) NRU 2 etries (a) From the first ReRAM page (b) From a radom positio (c) From the usearched page Evictio list 5 etries Fig. 8. The approaches for the search of cold page. 4. Performace Evaluatio I the simulatio tree structure is assumed as the etwork topology of the HDFS. To simulate the tree etwork topology, the odes are distributed i the racks as follows: there are racks ad each rack is equipped with oe switch. The racks are radomly distributed over a uit square plae, ad a rack occupies a square plae. For ay two racks, there is o itersectio area betwee them. Amog the racks, oe is desigated as the root rack of all other racks i biary tree topology of the height of 7. After formig the racks i the tree topology, 3,5 odes are radomly deployed withi the racks. For ay two odes i the same rack, their locatios are withi the square plae occupied by the rack. Based o the geerated etwork topology, the simulatio is performed with the parameter settig summarized i Table I. I each ode the available replicatio space is represeted as the maximum umber of data block replicas allowed to be stored. It is set by radomly selectig a umber betwee ad 5 I a simulatio ru, some odes are radomly selected which frequetly eed to write data to their disks. Therefore, may odes ca cocurretly issue the replicatio requests. The umber of requestig odes are chaged from 5 to 2,5 to evaluate the effectiveess of the proposed scheme with differet load coditios. For the settigs of the read, write ad erase latecy, the NRU table ad evictio list are used i additio to the parameters show i Table I. I additio, dyamic workloads are also cosidered for the disk queue ad switch lik queue. Wheever oe or more odes cocurretly issue the replicatio requests, block read-write operatios are eeded betwee multiple ode pairs. The umber of cocurret block read-write operatios is radomly set betwee ad 5. Due to cocurret block read-write operatios, a umber of operatios eed to be hadled with the disk queue ad switch lik queue before issuig the replicatio requests. Total replicatio cost (secods) Radom HDFS CDE Proposed scheme Number of requestig odes Fig. 9. The compariso of total replicatio costs. Fig. 9 shows the total replicatio cost for differet umbers of requestig odes. As see from Fig. 9, the total replicatio cost of all the schemes icrease with the umber of requestig odes. Basically, the HDFS replicatio scheme adopts the radom scheme to place the replicas of a data block, but with additioal cosideratio o the possible rack failure. Therefore, the total replicatio cost of the HDFS replicatio scheme is 74

9 similar to that of the radom replicatio scheme. Notice that the proposed scheme cosistetly reduces the replicatio cost compared to the existig schemes. I case of the requestig odes of 5, the improvemet with the proposed scheme is ot sigificat. However, as the umber of requestig odes icreases, the reductio becomes quite substatial. Average excutio time (secods) Fig.. The compariso of average executio times. Fig. shows that the average executio time follows almost the same tred as the replicatio cost of Fig. 9. The proposed scheme outperforms the other schemes, especially with relatively large umber of requestig odes. Average recovery time (secods) Radom HDFS CDE Proposed scheme Radom CDE Number of requestig odes HDFS Proposed scheme Average Cogestio Probabilities (%) Fig.. Average recovery time with differet cogestio probabilities. The average recovery time is show i Fig. for differet cogestio probabilities ad, requestig odes. As ca be see from the figure that the proposed scheme requires the smallest average recovery time, which is about oe-fifth of that of the HDFS. Ad, as the cogestio probability grows, the improvemet gets larger. Worst recovery time (secods) Fig. 2. The compariso of worst recovery times. Fig. 2 shows the recovery times i the worst case. The worst recovery time deotes the largest access time to retrieve a failure-free data replica, which is measured by accumulatig the access time of all replicas of a data block. As show i the figure, the proposed scheme decreases about 7% of the worst recovery time of the HDFS. Compared to the CDE algorithm, it is about 2%. 5. Coclusio Radom HDFS CDE Proposed scheme Number of requestig odes I this paper we have proposed a ovel cold data evictio scheme which cospicuously cosiders the cogestio status of the odes. The proposed scheme aalyzes the ode cogestio probability by which the system ca decide if the odes are available to trigger the cold data evictio process or ot. Also, the proposed scheme classifies the data as hot or cold, ad decides the storage locatio of them usig NRU algorithm. Computer simulatio reveals that the proposed scheme sigificatly decreases the recovery ad executio time i the HDFS eviromet i compariso to the previous schemes. I particular, with relatively high cogestio probability, the proposed scheme substatia excels the existig schemes. As the future course of study, we pla to exted the proposed scheme to effectively decide the umber ad locatio of the replicatios of SSD blocks i the HDFS eviromet. Also, the desig parameters will be determied through comprehesive modelig of the proposed scheme o the target performace measures. There exist may storage odes i a cloud computig system, ad eergy efficiecy is a importat issue. The 75

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