ࡱ>  npm{GrVE@ sbjbj ok7 jjj$,,,P,-T%l.0"000355Pkkkkkkk$mRp|kjv@2@3v@v@k 00k.`E`E`Ev@ l0j0k`Ev@k`Ez`EEc >,jf0- 0r& ,>Ce<jk<%lfp&D/qxf f/qj g69`E:ta<5hg66kk DE Reperasure: Replication Protocol using Erasure-code in Peer-to-Peer Storage Network Zheng Zhang, Microsoft Research Asia, zzhang@microsoft.com Qiao Lian, Computer Science Department, Tsinghua University, lq97@mails.tsinghua.edu.cn 6/1/2001 Technical Report MSR-TR-2002-59 Microsoft Research Microsoft Corporation One Microsoft Way Redmond, WA 98052 Reperasure: Replication Protocol using Erasure-code in Peer-to-Peer Storage Network Zheng Zhang Microsoft Research Asia zzhang@microsoft.comQiao Lian Computer Science Department Tsinghua University lq97@mails.tsinghua.edu.cn Abstract Peer-to-peer overlay networks offer a convenient way to host an infrastructure that can scale to the size of the Internet and yet stay manageable. These overlays, which are essentially self-organizing distributed hash tables (DHT), however, face challenges of data reliability because of the dynamic nature of the system. Furthermore, in order to see wider adoption, it is time to design support for generic replication mechanisms capable of handling arbitrary update requests most of the existing proposals are deep archival systems in nature. Utilizing the fact that DHT can function as a super-reliable and high performance disk when data stored inside are erasure coded, we believe practical and simple protocols can be designed. In this paper, we introduce the reperasure protocol, a layer on top of the basic DHT, which efficiently supports strong consistency semantic with high availability guarantee. By relieving the DHT layer out of replication duty inside, a cleaner overall architecture can be derived because of the clear division of responsibility. Introduction Recently many interesting P2P systems, such as OceanStore  ADDIN ENRfu [10] PAST  ADDIN ENRfu [3], Chord  ADDIN ENRfu [15] and CAN  ADDIN ENRfu [13] have been proposed. In general, these systems are self-organizing, distributed hash tables (DHT), and capable of scale to Internet size and yet stay manageable. The dynamic nature of the system poses challenges to data reliability. Many of these systems thus internally employ replication resolutions one way or the other. However, as a design philosophy, we believe a clear division of responsibility will result a better overall design. In other words, the baseline DHT should focus on efficient self-organizing and routing protocols and routing, while a layer on top handle the availability and consistency of data. Furthermore, majority of the current proposals are archival systems in nature. It appears that those do deal with update requests have designs unduly complex and could be simplified. In order to see wider adoption of the P2P technology in the system space, we believe it is useful to investigate more practical and simpler alternatives. These two reasons are the fundamental motivations of our work. We borrow insights from early works  REF _Ref10629205 \r \h [5] REF _Ref10628977 \r \h [6] REF _Ref10628279 \r \h [10] that extremely high availability can be achieved if DHT store erasure coded data. We extend this vision to conclude that DHT can thus be considered, logically, as one super-reliable and high-performance disk. With this, efficient protocols, which we call reperasure (replication with erasure code), can be designed. This set of simple protocols operates outside the DHT, and is efficient and robust, and yet is capable of retaining strong consistency semantic. The latter is guaranteed by executing transactions among concurrent clients, instead of replicas. This position paper explains the reperasure protocols. After a brief introduction of background and system assumption in section 2, we give the availability analysis of reperasure in section 3. The architecture and protocols of reperasure are presented in section 4. We offer a comparison of reperasure against other consistency protocols in Section 5. Related work is covered in Section 6 and we conclude in Section 7 along with a description of our planned future work. Background Peer-to-peer network The peer-to-peer systems we are interested in are those that will guarantee the retrieval of an existing object, as opposed to systems such as Freenet  ADDIN ENRfu [1]. Important flavors of such systems include CAN  ADDIN ENRfu [13], Pastry  ADDIN ENRfu [14], Tapestry [17] and Chord  ADDIN ENRfu [15]. All of these systems can be regarded as a distributed hash table (DHT): lookup an object is equivalent to searching with the key associated with the object. Similarly, the concept of hashing bucket is mapped to a node in the system. Consequently, object query, insertion and deletion become routing in the overlay network composed by the participating nodes (buckets in the hash). Theses systems all guarantee an upper bound of total number of hops in the overlay network, mostly with O(logN) (even CAN can be improved to O(logN) with simple optimizations  REF _Ref10627699 \r \h [16]). Erasure code Erasure code is commonly used in error-resilient communication systems. A few research projects have also used it to construct storage systems mostly for archival purpose  REF _Ref10628977 \r \h [6]. In that class of scheme, a data unit is divided into m blocks (fragments), and then they are recoded into a total of n blocks. We call the original blocks data block and the others check blocks. The ratio of n/m is often termed as the stretch factor. The key property of erasure code is that any m blocks are sufficient to reconstruct the original data unit. Popular erasure coding schemes include Reed-Solomon  REF _Ref10628994 \r \h [8] and Tornado code  REF _Ref10629010 \r \h [11] (which require slightly more than m blocks). System assumptions We are interested in building a P2P storage system with commodity PCs, possibly distributed over wide area. These PCs self-organized into a P2P system, using any of the aforementioned proposals, as long as there is hard guarantee to retrieving an existing data. This system belongs to a well-defined administration domain. As opposed to other systems such as Gnutella  REF _Ref10629028 \r \h [12] and Freenet  REF _Ref10629042 \r \h [1] where nodes are volunteered from arbitrary owners, the system dynamism is not as dramatic. We assume nodes are fail-stop; and they leave either by loss of connectivity, parts failure, or scheduled downtime. Replication with erasure code and statistically strong consistency A replication system is primarily to ensure data availability, and secondarily to speed up the access of data from many clients at physically diverse locations. Traditionally, this is done by generating multiple full replicas and distributing them over failure-independent and geographically dispersed nodes. In a P2P replication system using erasure code (reperasure), logically there is only one single copy. This copy, however, is broken down into large number of blocks (both data and check blocks) that are distributed widely across nodes inside the DHT. Because nodes in DHT are distributed, this arrangement leads to extremely high data availability guarantee. The total storage space needed to host the blocks is much smaller than if we were to create the same number of full replicas. Furthermore, parallel access to sufficient number of blocks yields adequate performance and more efficient use of network and storage bandwidth. Logically then, the DHT can be considered as a super-reliable disk with very high I/O bandwidth. This abstraction leads us to believe that in a reperasure P2P system, we can enforce both consistency and availability statistically, by issuing writes to a large number of blocks in parallel. As will be detailed in Section 4, we wait only till all the updated blocks are released successfully into the underlying DHT and then return right away. This means we rely on a sufficient number of blocks to be updated to the point that any inconsistency can be detected and repaired in the subsequent accesses to the data. The stretch factor (total number of blocks over that of original data blocks) is a tunable parameter and can differ for different data, and it should be such that the level of consistency and availability reached is statistically equal or higher than that of strong consistency model using full replications. We proceed to derive this as follows; our analysis is similar to the model in  REF _Ref10629205 \r \h [5]. We assume a fixed availability EMBED Equation.3  for a single node. And compare the availability of reperasure system versus full-replication system.  Figure 1: Availability Comparison With full-replication, a certain file is unavailable only when all k replicas are unavailable, so the availability is EMBED Equation.3 . In reperasure system, file is partitioned into s equal sized units; each unit is split into m data blocks, and then apply erasure code on the blocks generating totally n blocks (i.e. EMBED Equation.3  check blocks). Since any of the m blocks can reconstruct the original unit, the availability for a single unit  EMBED Equation.3 is EMBED Equation.3 . Furthermore, a file is only available when all the units are available, so the availability for a file is EMBED Equation.3 . Figure 1 shows the availability status for m=8, m=16 and full-replication system, when availability for a single node  EMBED Equation.3 =0.9, unit size of 4kB for a 4GB file (and thus s=1,000,000). The choice of p and s gives us a very conservative measure. According to the figure, stretch factor of 28/8 or 40/16 for n/m pair will give nine-9 availability. In contrast, full-replication reaches only four-9 availability even with four full replicas. These results are valid for Reed-Solomon code, if Tornado code is used instead, we need slightly more than m blocks to reconstruct the data. However, this does not change the general conclusion. Architecture and protocol The reperasure protocol encompasses a number of design elements and considers all the important operations of a storage system. From a logical point of view, the storage system is abstracted into a distributed hash table (DHT), where data items in the form of pairs are stored. The value attribute is a data or check block belonging to a file segment which we call unit. The DHT, which can be implemented in any of the popular p2p overlays, supports the following fundamental API: lookup(key), insert(key, value) and delete(key). As we will show later, the addition of other APIs such as atomic operation over the value can yield further performance improvement. Other APIs will be explained as we discuss the protocols. The details of the architecture and protocols will now be described. To permit read while an update is in progress, our design is a versioning system which naturally lends itself to the support of archival. Both versioning and the choice of erasure code granularity affect how data are organized and located internally (section 4.1). As we shall demonstrate, the read protocol is a simple extension of the DHT lookup API (section 4.2). Update protocol is explained in section 4.3. Garbage collection, when deep archival is no longer required for a given piece, is discussed in section 4.4. Finally, we discuss the process of reconstructing data upon failures in section 4.5. Data organization, versioning and location scheme The logic granularity of a data item stored in the DHT is a unit, whose size is Su. The unit is then further broken down to m blocks, each of them Su/m bytes. Internally, by the chosen algorithm of erasure code (Reed-Solomon, Tornado), a stretchy factor is chosen, which result in n total blocks (i.e., n-m check blocks). Our default values of these parameters are: Su=4KB, m=8 and n=28. We assume any document has a unique document id, doc-id. A given unit also has a unit-id which is essentially its offset in the document in Su quantum. Thus, reading a file at offset 4KB starts the access at a unit 1. All blocks belong to a unit, including both data and check blocks, are numbered from 0 to n-1. Blocks are the basic pieces get stored in the DHT. A given block is keyed by hashing over the concatenation of doc-id, unit-id and block-id. To retrieve a block, a lookup with the block id is issued into the DHT (along with the version number requested, as will be explained shortly). For a file access that spans across multiple units, then the application layer will issue multiple requests to the units involved.  EMBED Visio.Drawing.6  Figure 2: Version File and Block Allocation There maybe multiple versions of a data. Thus, a version file is associated with each unit, or optionally with multiple consecutive units (Figure 2 shows the later case). The format of the version file is illustrated in Figure 2. For the associated unit, the lowest row denotes the earliest version thats currently available, while the highest one corresponds to the most recent one. As shown, there is also a write-in-progress (wip) bit associated with each version. If that bit is turned on, that means a write is in progress to the associated version. Only rows with the wip bit off can be read. This arrangement permits a compact representation of versions, and also parallel reads to older versions when a new write is in process. It is important to note that block id is not keyed with version number, and thus all versions of a given block are located in one node (as shown in Figure 2). The version number is attached as a separate parameter to the DHT APIs to identify the right version of blocks. Since the version file is small, we simply generate a very large number of full replicas of it. When update to the version file itself is to be done, we update all of its replicas in parallel. Likewise, when we need to read the version file, we retrieve sufficient number of its replicas in parallel, and inconsistency among replicas is detected by simple majority vote, and repaired if over a threshold. Read protocol  EMBED Visio.Drawing.6  Figure 3: Read Flow (plus Recovery) The read protocol is quite straightforward. First, we examine the version file and from there decide which version we will be reading. Only versions with associated wip bits turned off can be read. Based on the version number as well as the unit id, we generate sufficient number of block ids (including both data and check data) and sends parallel lookup requests into the DHT. In a geographically distributed deployment, we will choose blocks that are near the requesting node. Such algorithms remain as one of our future work. When a node inside DHT receives a request to a block supposedly in its custody, it checks to see if the block with the right version number exists and returns it if so. If it fails to find the block, it will respond with a block_missing message instead. When enough blocks are received to reconstruct the data, we proceed to do so right away and return the unit back to the upper layer application. If not, lookup requests are sent out to blocks not attempted earlier. This process continues until all blocks are exhausted. As shown in Section 3, this is a very small event. In parallel, any blocks reported missing will initiates a data repair procedure, which is explained in Section 4.5. With this on-demand reparation procedure, the more a unit is accessed, the more robust it becomes. Update protocol In our system, update control is the responsibility of the upper application layer. To guard against potentially conflict updates among concurrent writing clients, any protocol that ensure serializability as who will proceed can be used. Finding out the concurrent writing clients is done at runtime in the application layer and many past works have indicated that such clients are small in number. Once this is decided, the update will start in three steps. First, we read out the latest version from the version file, increment it and store back to the version file, with the wip bit on. Next, all necessary blocks that need to be modified, which had been prepared, are released into DHT by parallel insert calls; for every unmodified block an upgrade message is sent instead. Once the release is finished, without waiting for any acknowledgement, the version file is accessed again and the wip bit is turned off. When a node receives a new block, it generates a new version as described by the update message; if it receives an upgrade message instead, if there exists a block with the previous version, it clones a copy and marks it with the new version. In a DHT, even when we have guaranteed that no two concurrent writes are admitted, its still possible that blocks with different versions arrive at a node out of order. This is because updates may originate from any nodes and their propagation to different nodes may complete with different speed, depending on the routes taken. When out-of-order blocks are received, they are simply inserted to the local store of the node. Unlike the case of read, because of the potential out-of-order arrival, we do not trigger unit repair when an earlier version of a block is found missing. This is justified because the blocks will be ordered once they do arrive, and the most recent update propagation will succeed anyway with high probability, and finally that in most if not all the cases a write will be preceded by read which will access, with high probability, the same set of blocks that the write will. Thus, statistically speaking, by the time the write is released into the system, the chances are that the repair for inconsistency has been trigged already. As one can observe, manipulating the version file to mark the beginning of the update has significant overhead because it involves two operations, one read and one write. Instead of these two operations, we can replace them with a new API that is an atomic read&modify. This operation atomically reads out the current newest version, adds one to generate the new version number, and store to the version file in a new row with the wip bit on. It also returns this new version number, enabling the majority vote to detect inconsistency in version file replicas. This process is illustrated in Figure 4. Garbage collection When old versions are no longer needed, a background process is issued to reclaim the space. This is accomplished by issuing delete APIs to the blocks to be removed, and modifying the version files accordingly. Data recovery Data recovery is actually very simple (see the recovery panel in Figure 3). As described by the read protocol in Section 4.2, by the time the recovery process is triggered, the original unit has already been reconstructed. This is because the read will keep on trying until all blocks are attempted, if that still fails the read will be aborted and the recovery process wont be triggered at the first place. Since we now have the original unit, we compute the blocks again and re-insert them into the DHT. Discussion We now offer a comparison of the reperasure protocol with other consistency mechanisms. At one end of the spectrum, there is strong consistency  REF _Ref10629105 \r \h [4] which essentially engages distributed transaction over the replicas to enforce one-copy serializability. Distributed transaction protocols are mature technologies, seeing wide applications especially in financial institutes. However, its overhead increases quickly with number of replicas, posing a practical limit on number of replicas. The small number of replicas, in turn, creates an availability upper bound as well as data access performance for large numbers of clients. Weak consistency breaks this barrier by allowing all replicas to receive updates locally, and then employs epidemic propagation such as anti-entropy protocols  REF _Ref10629117 \r \h [9] to pull all replicas to a globally consistent state eventually. This is coupled with conflict resolution schemes, which resolve potential inconsistencies when and if they arise. While this class of protocols improves efficiency of both reads and writes locally and are practical for quite a number of applications, in general it suffers from the syndrome of replica dilution, as explained by  REF _Ref10629137 \r \h [7]. Reperasure protocol rests upon the notion that when large number of blocks with erasure code are stored inside, a DHT can be regarded as a super-reliable and high-performance disk. Our current design offers a much stronger availability than full replicas with strong consistency, while at the same time achieves better network and storage utilization via parallel access to the blocks. Therefore, performance wise, we predict it to be somewhere between weak and strong consistency. By ensuring exclusive update right via transaction control among concurrent writing clients, we obtain the advantage of strong consistency utilizing mature transaction protocols. A full-replica strong consistency with same availability guarantee will need a lot more replicas much more than typical number of concurrent clients, making it hard if not outright impractical to invoke distributed transactions over them. We note that if weak consistency is desired, we can support this as well. In fact, the wip bit is designed precisely for this purpose. Rows in the version files with wip bits on are concurrent writes, as long as they are writing to different version. In order to do this, we simply relax the semantic of version number and let part of it carry client ID. Our experience leads us to believe that clear division of responsibility will ultimately lead to a cleaner design, and propose one such division in the following table. Application: File system, archivalConsistency semantic: strong consistency using transaction control (among concurrent clients, not replicas); weak consistency using conflict resolutionConsistency and availability enforcement: using reperasure to give statistically strong guarantee of data availability and consistencyP2P infrastructure: Self-organizing, self-evolving and efficient routingIt is also important to point out the limitations of the reperasure protocol. First of all it relies on an abstraction of a distributed, super-reliable and high-performance disk, and thus is not immediately applicable for areas where this infrastructure is missing. Secondly, its known that retrieving many small packets has lower bandwidth efficiency than one big packet. On this we hold our ultimate brief that bandwidth will continue to improve rapidly, while the same is unlikely to be true for latency. Related work Replication protocols have been extensively studied over the years and we can only point out a few  REF _Ref10629105 \r \h [4]. Our DHT can utilize any of the P2P DHT proposals  REF _Ref10629166 \r \h [3] REF _Ref10629173 \r \h [13] REF _Ref10629175 \r \h [15][16][17]. We emphasize on providing a reliability shell over them. Our work is inspired by  REF _Ref10629205 \r \h [5]. The closet work to ours is  REF _Ref10628977 \r \h [6]. However, in that work the updates are wholesome replacement of the files and no protocol details are given; whereas we aim at a generic replication protocol. Among the existing proposals, only Oceanstore [10] has decidedly chosen to deal with update problems up front. A data may exist in the system in three different ways: as archival items coded with erasure, as secondary replicas and finally as a primary tier of replicas. The primary replicas ensure strong consistency among themselves with Byzantine protocols. This multi-tiered solution, while covering a wide range of possible semantics, also captures with it all the complexities of these diverse set of techniques. It isnt clear that in a dynamic system such as peer-to-peer network, this approach will work well or even necessary. Our approach is purely version based and uses erasure code as the only elementary support. We believe that both strong and weak consistency can be efficiently supports. Conclusion and status In this paper we propose a novel technique to realize generic replication over P2P DHTs. Utilizing the fact that large number of blocks with erasure code stored in a P2P collectively makes a super-reliable disk, we derive the reperasure protocol which is simultaneously simple, efficient, robust with strong consistency. We plan to build a prototype of the system and report experiment numbers in detail. One problem we have yet to deal with is the silent evaporation phenomenon where nodes depart and, with it, the associated blocks not read for a long time. We need an efficient method to monitor such events and recover the blocks before the availability of a unit reaches too low a threshold. To improve the performance further, a read access should read the close-by blocks. We are developing such algorithms. Finally, we will investigate if weak consistency can offer substantial advantages and, if so, to support it efficiently. References Clarke, I., et al. Freenet: A distributed anonymous information storage and retrieval system. in Workshop on Design Issues in Anonymity and Unobservability. 2000. Berkeley, CA, USA. Dabek, F., et al. Wide-area cooperative storage with CFS. in Symposium on Operating Systems Principles (SOSP). 2001. Banff, Canada. Druschel, P. and A. Rowstron. PAST: a large-scale, persistent peer-to-peer storage utility. in HotOS-VIII Workshop. 2001. Schloss Elmau, Germany. E.G.Kotsakis and B.H.Pardoe Dynamic Quorum Adjustment: A Consistency Scheme for Replicated Objects Proceedings of the Third Communication Networks Symposium, July 1996. Hakim Weatherspoon and John Kubiatowicz, Erasure Coding vs. Replication: A Quantitative Comparison Proceedings of IPTPS 2002, March 2002 Hakim Weatherspoon, Matthew Delco, Shelley Zhuang, Typhoon: An Archival System for Tolerating High Degrees of File Server Failure, available through http://www.cs.berkeley.edu/~hweather/Typhoon/TyphoonReport.html, 1999 J. Gray, P.Helland, P. E. O'Neil, D. Shasha, The Dangers of Replication and a Solution SIGMOD Conf. 1996: pp.173-182 J. Plank, A Tutorial on Reed-Solomon Coding for Fault-Tolerance in RAID-like Systems, available through http://cs.utk.edu/~plank/, February 1999. Karin Petersen, et al. Flexible Update Propagation for Weakly Consistent Replication ,Proceedings of SOSP 16, 1997 Kubiatowicz, J., et al. OceanStore: An Architecture for Global-Scale Persistent Storage. in ASPLOS 2000. 2000. MA, USA: ACM. M. Luby, M. Mitzenmacher, M. A. Shokrollahi, D. Spielman, V. Stemann, Practical Loss-Resilient Codes, Proc. 29th Symp. on Theory of Computing, pp. 150-159, 1997. Qin Lv and Sylvia Ratnasamy, Can Heterogeneity Make Gnutella Scalable? Proceedings of IPTPS 2002 Ratnasamy, S., et al. A Scalable Content-Addressable Network. in ACM SIGCOMM. 2001. San Diego, CA, USA. Rowstron, A. and P. Druschel. Pastry: Scalable, distributed object location and routing for largescale peer-to-peer systems. in IFIP/ACM Middleware. 2001. Heidelberg, Germany. Stoica, I., et al. Chord: A scalable peer-to-peer lookup service for Internet applications. in ACM SIGCOMM. 2001. San Diego, CA, USA. Xu, Zhichen and Zhang, Zheng, Building Low-maintenance Expressways for P2P Systems, available through http://www.hpl.hp.com/techreports/2002/HPL-2002-41.html, March 2002 Zhao, B. Y,. Kubiatowicz, J.D., and Josep, A.D. Tapestry: An infrastructure for fault-tolerant wide-area location and routing. Tech. Rep. UCB/CSD-01-1141, UC Berkeley, EECS, 2001. PAGE  PAGE 1  EMBED Visio.Drawing.6  Figure 4: Update Flow     l m o p   ! 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FEMF+@ 5Visio (TM) Drawing obRl !fffMMM3338 TZ Arial@:NWingdzsh@uNuMonotype_ Sort mlSymbol5T?? Y@-1UJ:DT1EW-hTT<* /Ub bO0zGz?@8@H2!kWbʁk !k9 +k)kO,O, P/8&9$? CAk&,,'5/G%&X$&  E& y  0)^?Tp2    U1?%aUBEEEUEEE@ON7OIJ`2BEEEEEEE@$O9FAY; ,Q*VV,QgTY 1 W-oW?o\o ' `3kulb6`i#l l l ҕY??\ #TD5r|O,O,!O,N/BQ|USME6A_h8OOOOO@O$O _HOZOlI`7rZW?_ep؂fsU妖jȟxҏk)qĬ T %1Wdv mi| 9rSi'0UP]y??4zAy2Y#QU *~0{j:Ǯ/c֛!3vD//?Qpv?d4,Qk(YU1%1λ-(@kp 5 .$5-Bbi`w# *|‰)/й//߸??2 44ӶSPt߆27yՅ:]$NԵb}-?Qcuv")/"%)f'!U+%U2qa0?`?r6#!r6;<<7y/I[ײ|Q й|Qy ??-?%GZ4p%*4c bF!:/L/^/qZ?l3P?! "!@)"IAر1@//6a!,=:B@K` Rąh3qB,b/t"YE ItbQ?Y?}?3??=Nϖ=K/]/r I?032FX"kcT//((%1aq ߼6E9 */A8T0zGzsF,Yqq{q>P vq.}O?OOV_$WOHAeOKOBocjo|ooooooSpA/U_r/ -r..BqP_b_??__q COgOy7J_mIngO5_TfS{aLǯٯާ B .#c:L}şן1SO ft /HNxTq *O$%ӿL߈xĞjQ\pY(b0oTo //ӏvY0gfH(#!N&8Ͽ)Z!oEgFVjP/?@FZc[&~,,'&8J\1$,p4Ă9///HQ11i?;Di{?B Of' / mgM3W[SCPbbz@HqRbyRT/??/5!RN5]m__uopk$o6o 88:OqQ-O?O<NuOOOOO?OO/)_&/8/ cln//(,%%_oo??*?\oӏSoeowooooM_o  odX,LetterO(b4e~} DisplayUR) U- N U\k'}4UFDfP h-RTUUUA@ ?I? 3h eqYk 1Ho 2*w Q2vQ` Flowchart]` b TJ /?|wxpf6lbx{s'pwwpl } ww%Data stored on disk..e.UH PD  # Uh4JT@aaMAREMkUA@ ?u?Q NuQ`m uA >M@1IA 1^TbQ w cQM]L>2mTrY0YY]JN0!@H+`lr|t" p{rTb#w$u-@Np #"@> #!1&4+"`Vis_SAD.chm!#26353I"`?Copyright (c) 2001 Microsoft Corporation. All g2s reserved.7DC00'R)#-3 n"+ lJ0JUll5 \6 !(w *z# 0"?!$bS"|'.>Y   " Z(WM&QO*$F@,C^8 *%U/I& @ISrhANQ-D5 5I hOU|KS@oNo9ZiC|G&M rQ%MpiPLI% #@&$V $@$TꅰV8'C#b}" 1o#'BO[tfXg NRj }O8 iAU@#5GY~nz3O)ph `HMJ%| qR%]O og|k @KPX/n,}lq(f!ELOV]@&@&4QC %#&Iҿ%( Ɇ/%DB5]vψϬQm^liT9ߦ7+NTAA*j"bb, 2(sc!5!1! `Cost6\3,Enter the c associated withis process02`@ %`߷!##Du3m_0 dZ {ofstep05{'9(8` Resour_0:numb~people0quirtomte^task0nQJ7)1!1I0B` %Properti30?R?b  1K QiL I{pg0*NhTQDE"D2(Yf!1Y@3ODisk,st0ge,Dataded,on,Audit,Diagram,inspec1,accounts,bookkeeping,`65c$@Rmb?ؿ贁N8i6??54 _H9-gB !OyaGEsFL #, {B ? >SdeGao@5kbo|P+.4aUFDfP h-RTUUUAV? ?d WceqYk* Hu A  / B!+RdA AO 0Drag this shape to create a customizabl.]b?O贁Nk?t?@ ? 3 ?UH`D  # >  J h 8J T  GUN U@ ?PO[u`u`b&uQMQ2P hPM\J.U@T^}aAJ#0O贁N#k?Hp)`TBL`/CMD=1 /ROW=0 /COL=0y KNQ* JQ5OI!$+I`?Copyright (c) 2001 Microsoft Corporation. All "s reserved.`Vis_SF.chm!#32211|0LI/N-X#M2Uk2? R45  >(4&3LM5lJ U0JU||Uy5O 2r9O$J0PQ d#BiAfGyOO$Ay%OO_OWA5([?_Oc]T0N_J2rMe2SgD1|yo3 "Z2TrrA/[d11y"1ud2NNT3dy24 d2o!o! 6daC7detu DADA?!:d3;Ld<tE=t*>t9=AQ!_!_!23ST!d R0oq{y%5T%@&k3?TLr@ry5L@IaadG oq5cu \d"{SbBJ`NVoqb msb*b@c3_qraEß՟ rq)rDtAPb #$"57$rXMzr6rVhz1 bCrspd#ZTable,Drag,create,customizForms,business,office,f7,account,sheets,receipt,checklistsPr$@ ]r'!4`Resize lumnss5$,L9BrEA0y%aynyqEIUY a(?!#e'!! 0ڴ`In !ȱ to the &Lefprq"$"$"28$fϰ+`Ri&H 3\%@Row &Above[5y%V߰` DeletSo& w75_!a _3*TYgͷ(NƱ&mn34y5ߠ$&Belo26إ5a*|r5ҔQZﹳS)c!&3B|1W"&. z5btBorders.. d8WSr(#HeaOde&ru3z𻕿&Foote,1i۱4WUsa UU'OU;FaHOsua__ J TbTEa,_V݃IR52*9Q!;q////)a/su{_O_ _;?/__?*=!q????U&q?/?OvOAOSOeO2_!OOOOIaOO __x_CZ? 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All 2s reserved.J`Vis_Sba.chm!#26686ida?RlJ,>Ud6ZV$(s=?)ǹ<$$b# "yB:V/K;EA%;D]_2׳HX!:ͳ?OAM%2xO;zJrA RSq(@^!M%AM\Me2O-QVDH@}oz@Lc2oFTOhcWD`1@W@Sa?@/!jLYeb__hc%veN no_[jJ(?BkF8By!at憵 /T6DUmA@ ?ۿI?Y{O1@3EEbOeZ! Hu -^0B:NS?]bt WN>8Arrow with a 60-degree ahead.HD" # =hZ4>T(YY9 dA[UA? ?P-DT! @A-u `u bu  @"Y!T FWu`5 tue.A 8X>9@$U'P"_/q%@'i-/ 7$#5 L!  6 d*%:#/ h?Q(>uJ{6{P`"u?DF&`Qps-8R? WI@+ !Q6]u@B$#IAD5 I_`?Copyright (c) 2001 Microsoft Corporation. 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