ࡱ> Kj7 {bjbjUU !7|7|,Sl:0, &, 8 L PPP8$ L g 4'(('('8'+u396dugwgwgwgwgwgwg$i kg7++77gG\\8'8'ggGGG7h\l8'8'ugG7ugGGePaV,e8' X|ʏ!L P@8eNeg0gceNl=EVleGL L \\\\Fcast Multicast File Distribution Jim Gemmell Microsoft Research 301 Howard St., #830 San Francisco, CA 94105 USA  HYPERLINK mailto:Jgemmell@microsoft.com Jgemmell@microsoft.comEve Schooler Computer Science, 256-80 California Institute of Technology Pasadena, CA 91125 USA  HYPERLINK mailto:Schooler@cs.caltech.edu schooler@cs.caltech.edu Jim Gray Microsoft Research 301 Howard St., #830 San Francisco, CA 94105 USA  HYPERLINK mailto:Jgemmell@microsoft.com gray@microsoft.comAbstract Reliable data multicast is problematic. ACK/NACK schemes do not scale to large audiences, and simple data replication wastes network bandwidth. Fcast, file multicasting, combines multicast with Forward Error Correction (FEC) to address both these problems. Like classic multicast, Fcast scales to large audiences, and like other FEC schemes, it uses bandwidth very efficiently. Some of the benefits of this combination were known previously, but Fcast contributes new caching methods that improve disk throughput, and new optimizations for small file transfers. This paper describes Fcast's design, implementation, and API. Introduction Frenzied downloading that raises Internet traffic by an order of magnitude has been dubbed the Midnight Madness problem because the mad dash for files often takes place late at night or in the early morning when files are first made available. Spikes in activity have been due to a range of phenomena: popular product releases; important software updates; security bug fixes, the NASA Pathfinder vehicle landing on Mars, the Kasparov vs. Deep Blue chess match, and the Starr report. The danger of such traffic spikes lies not in the data type, but rather the distribution mechanism. For example, when Internet Explorer 3.0 (IE 3.0) was released, the number of people attempting to download the new product overloaded Microsoft web servers and saturated network links near Microsoft, as well as elsewhere. Not surprisingly, nearby University of Washington found that it was nearly impossible to get any traffic through the Internet due to congestion generated by IE 3.0 downloads. Unexpectedly, whole countries also found their Internet access taxed by individuals trying to obtain the software. These problems are caused by the web's current unicast "pull" model. A TCP connection is established between a single sender and each receiver, then the sender transmits a copy of the data once over each connection. Each copy must traverse many of the same network links. Naturally, links closest to the sender can become heavily saturated. Nonetheless such a transmission can create bottlenecks anywhere in the network where over-subscription occurs, as evidenced by the IE anecdotes above. This network congestion and server overload could have been avoided by using the multicast file transfer technology (Fcast) described here. In fact, using Fcast, every modem user in the entire world could have been served by a single server machine connected to the Internet via a modem, rather than the 44 machines that serve microsoft.com via two 1.2 Gbps network connections. This paper describes how Fcast combines erasure correction with a data carousel to achieve reliable multicast transfer as scalable as IP multicast itself. Multicast file transmission has been proposed before [ seq bibitem RIZ97a \* MERGEFORMAT 1,  seq bibitem VIC97 \* MERGEFORMAT 2]. However, previous work focused on network efficiency. This paper extends previous work by describing how Fcast optimizes network bandwidth for small file transmissions, and how Fcast uses caching to optimize disk throughput at the receiver. Reliable Multicast of Files Using Erasure Correction IP multicast provides a powerful and efficient means to transmit data to multiple parties. However, IP multicast is problematic for file transfers. IP multicast only provides a datagram service -- best-effort packet delivery. It does not guarantee that packets sent will be received, nor does it ensure that packets will arrive in the order they were sent. Many reliable multicast protocols have been built on top of multicast, e.g., [ seq bibitem FLO95 \* MERGEFORMAT 3,  seq bibitem KER98 \* MERGEFORMAT 4,  seq bibitem PAU97 \* MERGEFORMAT 5]. Scalability was not a primary concern for some of these protocols, hence they are not useful for the midnight-madness problem. The primary barrier to scalability for reliable multicast protocols is feedback from the receivers to senders in the form of acknowledgements (ACKs) or negative acknowledgements (NACKs). If many receivers generate feedback, they may overload the source, or the links leading to it, with message implosion. Some protocols, while addressing scalability, are still not as scalable as IP multicast. Others, while fully scalable, require changes to routers or to other infrastructure, making their use unlikely in the near future. The data carousel [ seq bibitem AFZ95 \* MERGEFORMAT 6] approach is a simple protocol that avoids any feedback from the receivers. The sender repeatedly loops through the source data. The receiver is able to reconstruct missing components of a file by waiting for it to be transmitted again in the next loop without having to request retransmissions. However, it may be necessary to wait for the full loop to repeat to recover a lost packet. The forward error correction (FEC) [ seq bibitem BLA94 \* MERGEFORMAT 7] approach requires no feedback and reduces the retransmission wait time by including some error correction packets in the data stream. Most of the FEC literature deals with error correction, that is, the ability to detect and repair both erasures (losses) and bit-level corruption. However, in the case of IP multicast, lower network layers will detect corrupted packets and discard them. Therefore, an IP multicast application need not be concerned with corruption; it can focus on erasure correction only. The erasure correction used here is called an (n,k) code. k source blocks are encoded into n>k blocks, such that any k of the encoded blocks can be used to reconstruct the original k blocks ( REF _Ref440356186 Figure 1). (Note: in this paper, we will refer to blocks of data from a file; a packet is a block with an attached header, which is sent over the network.) For example, parity can be used to implement (k+1, k) encoding. Many (n,k) codes based on Reed-Solomon codes are efficient enough to be used by personal computers. For example, Rizzo has implemented a code capable of coding/decoding data at 90 mbps on a 133 MHz Pentium processor [ seq bibitem RIZ97b \* MERGEFORMAT 8].  EMBED Word.Picture.8  Figure  SEQ Figure \* ARABIC 1. An example of (n,k) encoding and decoding: k original packets are reconstructed from any k of the n encoded packets. In practice, k and n must be limited for Reed-Solomon based codes as large values make encoding and decoding expensive. (k,n) = (64, 255) are typical limits [ seq bibitem RIZ97a \* MERGEFORMAT 1]. Tornado codes, based on bipartite graphs, are an attractive alternative to Reed-Solomon codes [ seq bibitem BYE98 \* MERGEFORMAT 9]. A Tornado code may require slightly more than k blocks to reconstruct the original k blocks. However, the value of k may be on the order of tens of thousands. This paper uses a Reed-Solomon based (n,k) code, but discusses the impact of switching to Tornado codes, or codes like them, where appropriate. As most transmissions (e.g., files) are longer than k blocks, they must be divided into groups of k blocks each, with erasure correction (EC) performed on a group-by-group basis. Each block in the session is assigned to an EC group of k blocks, which is then encoded into n blocks. Each block is identified by an index specifying which of the n encoded blocks it is, as well as a group identifier associating it with an EC group. A nice property of FEC encoding is that encoded blocks are approximately the same size as original blocks. The only overhead is introduced in the block header where the index, group and other transmission details is carried a few bytes. Systematic encoding schemes simplify matters by making the first k of the n encoded blocks be the original blocks. If no blocks are lost, a receiver does not incur any processing overhead decoding the k blocks of a systematic code. Fcast uses a systematic coding scheme. The order in which blocks are transmitted is important. Suppose, for example that all n blocks were sent from one group before sending any from the next. Receivers with few losses would be forced to receive more FEC than they actually needed -- indeed the scheme would be more like a data carousel -- on average, a receiver would have to wait for 1/2 the file to be retransmitted to correct a single error. To avoid this, the sender sends all blocks with index i before sending blocks with index i+1. As shown in Figure 2, when block n of the last group of the last file is sent, the transmission cycles.  EMBED Word.Picture.8  Figure  SEQ Figure \* ARABIC 2. Transmission order: Any k blocks must be received from each group to reconstruct the transmitted file. To minimize receive time, one block is sent from each group in turn. While sending a given index value, the group order may be randomly varied to avoid correlation of periodic losses. G, the number of groups is the ceiling of the file size divided by k. To complete the reception, k distinct blocks (i.e., with different index values) must be received from each group. For some groups, more than k blocks may be received, in which case the redundant blocks are discarded. These redundant blocks are a source of inefficiency, as they increase the overall reception time. Supposing that only one additional block is needed to complete the reception. It is possible that a receiver may have to wait an entire cycle of G blocks (receiving blocks from all other groups) before obtaining a block from the desired group. Thus, the inefficiency is related to the number of groups G, which is the file size divided by k. Fcast efficiency is discussed further in section  REF _Ref437389861 \r \h 5. One danger with this transmission order is that a pattern of periodic network losses may become synchronized with the transmission so as to always impact blocks from certain groups; in the worst case, a single group is always impacted. The impact of periodic losses may be eliminated by randomly permuting the order of groups sent for each index. Thus, periodic losses are randomly spread among groups. The transmission as described so far greatly reduces overall network congestion. However, it does not deal with congestion control in the sense of sharing link bandwidth (e.g., like TCP does). To provide congestion control, the Fcast transmission is split up among a number of different layers, i.e., multicast addresses. Receivers drop layers when they detect congestion, and add layers in its absence. We follow the scheme described in [ seq bibitem VIC97 \* MERGEFORMAT 2], performing linear increase and exponential back-off to be TCP-friendly. A detailed discussion of congestion control is beyond the scope of this paper. However, we must note that in a layered transmission it is very difficult to predict the order in which packets will be received this will impact buffering schemes, which we discuss later. Sending Models: TIDDO and Satellite There are two primary models for file multicast. In the first model, illustrated in  REF _Ref422796808 \h  \* MERGEFORMAT Figure 3, the sender has a single set of files, which are continuously sent for some period. Receivers subscribe to the multicast, obtain the necessary blocks to construct the files, and then drop out of the multicast. We refer to this as the tune in, download, and drop out model (with respects to Timothy Leary) or TIDDO. TIDDO is applicable to the midnight madness scenario, where a file set is known to be in high demand by many concurrent receivers. The second model has the receiver continuously tuned in to a multicast address. Over time, the sender pushes different file sets that it believes may be of interest to the receiver. The receiver discards any unwanted files. We refer to this as the Satellite model, as it is most applicable to satellite transmission, where the receiver continuously receives a satellite broadcast.  EMBED Word.Picture.8  Figure  SEQ Figure \* ARABIC 3. TIDDO (Tune In, Download, and Drop Out) model. Sender continuously sends the same files. Receivers tune in when they like, get the bits they need, and then drop out of the multicast.  EMBED Word.Picture.8  Figure  SEQ Figure \* ARABIC 4. Satellite model. Receivers are continuously tuned in. The sender sends when it has material that may interest the receivers. The satellite model should be used with some caution. Using this model, it would be easy to write applications that send multiple files sequentially in a number of transmissions. If files are sent sequentially, then a receiver may not obtain enough blocks for a given file before the transmission ends, and may receive extra blocks for other files. In contrast, files sent in parallel (i.e., combined into a single batch file) do not suffer from this inefficiency. With Fcast, files may be sent in parallel by combining them into a single file, e.g., a tar, zip or cab file. Alternately, each file may be sent using Fcast on separate multicast addresses. Furthermore, if the Satellite model is widely used on conventional networks, it may not be possible for users to subscribe to all the channels of interest to them because the aggregate bandwidth may be too high. To avoid this problem, publishers will need to make their sending timetable known, so that receivers can tune in only for the files they need - but then we are back to the TIDDO model. Therefore, satellite mode is most applicable to actual satellite receivers. Fcast Implementation This section outlines Fcast's implementation, describing the architecture, the transfer of session and meta-file information, the Application Programming Interface (API), and the block header format. Sections  REF _Ref437389861 \r \h 5 and  REF _Ref437409379 \r \h 6, describe novel solutions to tune the k parameter and to enhance disk performance. General Architecture Fcast assumes that a single sender initiates the transfer of a single file to a multicast address. The sender loops continuously either ad infinitum, or until a certain amount of FEC redundancy has been achieved. Receivers tune in to the multicast address and cache received packets in a temporary file name until they receive enough blocks to recreate the file. Under the TIDDO model, receivers then drop out of the multicast. Under the satellite model, they continue to receive blocks in anticipation of the next file to be sent. In either case, the file is then decoded, and the file name and attributes set. See Section  REF _Ref437409379 \r 6 for more details on the reception algorithm. The Fcast sender and receiver are implemented as ActiveX controls. These controls may be embedded in web pages and controlled by scripting languages such as VB script, or embedded into applications (see  REF _Ref437408653 \h  \* MERGEFORMAT Figure 5). Each has an Init() call to initialize the Fcast with the multicast address and other transmission settings. A call to Start() begins the sending/receiving in another thread and returns control immediately. Progress of the transmission can be monitored by a call to GetStats(), and via connection points (ActiveX callback routines). An Fcast receiver spawns two threads. One thread simply receives packets and puts them in a queue; it ensures that packet losses in the network can be distinguished from packets dropped at the receiver when transmission proceeds faster than disk writes. The second thread takes packets from the queue, and writes them to a disk file with a temporary. When sufficient packets have been received to construct the file, the packets are decoded, and the file name and attributes are set. Figure  SEQ Figure \* ARABIC 5. The Fcast receiver embedded in a web page (left) and in an application (right). Session and Meta-file information The Fcast sender and receivers need to agree on certain session attributes to communicate. This session description includes the multicast address, and port number. We assume that there exists an outside mechanism to share session descriptions between the sender and receiver. The session description might be carried in a session announcement protocol such as SAP, located on a Web page with scheduling information, or conveyed via E-mail or other out-of-band methods. In addition to the actual file data, the receiver needs certain metadata: the file name, its creation date, etc. Metadata could be sent out of band. For example, it could be part of the session description or sent as a separate file in the transfer with a well-known file ID. For simplicity Fcast sends file metadata in-band. The metadata is appended to the end of the file as a trailer and sent as part of the transmission. The receiver places the metadata into a temporary file name. Once the data is decoded, the metadata is extracted (the real file name, length and other attributes). The metadata is appended rather than prepended to the file so that the correct file length may be achieved via simple truncation rather than requiring re-writing. A checksum is included in the trailer to validate that the file was encoded and decoded correctly. Packet Headers The packet header structure is shown in  REF _Ref436467019 \h Figure 6. Each file sent is given a unique ID. Thus, each block in the transmission is identified by a unique tuple. Packets with indices 0 to k-1 are original file blocks, while the packets with indices k to n-1 are encoded blocks. The sequence number, dwSeqno, monotonically increases with each packet sent, allowing the receiver to track packet loss. The file length is included in the header so that the receiver can allocate memory structures for tracking reception once it has received its 1st packet and read the header. Similarly, the k value is included so that the group size is known (the number of groups is calculated by dividing the file length by k). Instead of including the file length and k in every packet, we might have sent these values out of band. However, by including them in-band, and having meta-information embedded in the trailer, we enable a receiver to tune in to a well-known multicast address and port and begin receiving with no additional knowledge. Including the sequence number, k, and the file size only increases the header size by 5; with a payload of 1KB the difference is negligible. Note that in a satellite transmission, the file length will change with each file, and k may also. Our implementation makes the assumption that all packets are the fixed size. This value is not included in the packet header the receiver simply observes the size of the first packet it receives. The n value of the (n,k) codec also need not be transmitted, as it is not needed by the receiver. typedef struct { DWORD dwSeqno; //sequence number for detecting packet loss DWORD dwFileId; //file identifier DWORD dwFileLen; //length of file DWORD dwGroupId; //FEC group identifier BYTE bIndex; //index into FEC group BYTE bK; //k of (n,k) encoding } tPacketHeader; Figure  SEQ Figure \* ARABIC 6. Packet header structure. Selecting a value for k In general, the larger the value of k, the more efficient the file transfer (i.e. the fewer the redundant packets that must be received). However, implementation details prevent construction of a codec with arbitrarily large k. Let the maximum possible value of k be kmax. This section explains how to select an optimal k, given that k can be at most kmax. First, it examines the impact of k on transfer efficiency. Then, it considers selecting k for small file transfers. As described above, the group order of packets is randomized. Hence, even when network loss is somehow correlated, the loss to any given group will appear random to the receiver. Suppose the probability a given packet is lost is r. Let pg(k+x) be the probability that, for a given group g, no more than x packets are lost out of k+x packets, i.e., the receiver has obtained at least k distinct packets and is able to decode the group. This is calculated by the cumulative binomial distribution:  EMBED Equation.3  If k+x packets have been sent for each group, and there are G groups, then the probability that at least k packets have been received for all the groups is P(k+x) =( pg(k+x))G Therefore, the probability of completing reception depends on the loss rate, and inversely on the number of groups. Increasing k reduces the number of groups, so a larger k will tend to produce a more efficient transmission. Assuming a constant sending rate, the number of packets sent corresponds to time. We plot time as a percentage of the time to send the file once against the probability of complete reception in Figures 7 through 10.  REF _Ref437058009 \h Figure 7 shows listening time versus the probability of complete reception for a number of values of k. The ideal transmission has one group and has k set to the number of blocks in the file. In this case, the receiver has completed reception once it has received exactly as many blocks as are in the file. Note that this is the best case for a unicast transmission. With k limited to at most kmax, the number of groups, G, must grow with the file size.  REF _Ref437058436 \h  \* MERGEFORMAT Figure 9 shows the expected transmission time for file sizes ranging from 1 MB to 1 GB. It illustrates how the efficiency decreases for larger files. The average overhead goes from 20% to about 30% for a loss rate of 10%.  Figure  SEQ Figure \* ARABIC 7. Expected time required (as percent of time to send the file once) for a receiver to complete reception with random loss of 10%. File size 1 MB, block size 1 KB, various values of k plotted. Ideal is k = number of blocks in the file (1024).  Figure  SEQ Figure \* ARABIC 8. Expected time required (as percent of time to send the file once) for a receiver to complete reception with random loss of 40%. File size 1 MB, block size 1KB, various k plotted.  Figure  SEQ Figure \* ARABIC 9. Expected time required (as percent of time to send the file once) for a receiver to complete reception with random loss of 10%. Block size 1KB, k=32, various file sizes plotted.  Figure  SEQ Figure \* ARABIC 10. Expected time required for a receiver to complete reception with a random loss of 40%. Block size 1KB, k=32, various file sizes plotted. From  REF _Ref437058009 \h  \* MERGEFORMAT Figure 7 and  REF _Ref437058436 \h  \* MERGEFORMAT Figure 9 we see that with a 10% loss rate, using k=32 takes only about 15% longer than the ideal. Higher loss rates exacerbate the impact of the k value.  REF _Ref437846537 \h  \* MERGEFORMAT Figure 8 and  REF _Ref437846540 \h  \* MERGEFORMAT Figure 10 repeat  REF _Ref437058009 \h  \* MERGEFORMAT Figure 7 and  REF _Ref437058436 \h  \* MERGEFORMAT Figure 9, but with the loss increased to 40%. Using k=32 now takes about 40% longer than the ideal case. While significant, we still find this result acceptable taking 40% longer is worthwhile, considering the large number of receivers supported (not to mention that TCP achieving ideal performance under a high server load is extremely unlikely). While we want a large k value for transmission efficiency, our sender currently must read k blocks to generate each encoded block, so this creates an incentive to keep k low (see section  REF _Ref437409379 \r \h  \* MERGEFORMAT 6 for more details). We use k=32 as a default value that allows reasonable sender speeds combined with reasonably efficient transmission. We have shown how larger files are transmitted less efficiently. However, at the other end of the spectrum, small transfers also require a careful consideration of k value. For instance, transmitting a 17 block file with k = 32 would require 15 padding blocks to fill out the single group Recall, however, that larger k values only improve efficiency by reducing the number of groups. Therefore, using k=17, avoids the overhead of padded blocks, and has the same efficiency as k=32, since there is still be only one group. Therefore, any transmission of S ( kmax should use k=S. Transmissions of slightly larger values are also problematic. Assume for the moment that k must be fixed over all groups in a file. Consider a transfer of kmax + 1 blocks. Using k= kmax would give one full group of k blocks, and a second group containing only one data block with k-1 empty padding blocks. The overhead of the transfer would be close to 50% with k values that are larger than 10. For example, if kmax =8 and 9 blocks are to be transmitted, then 7 padding blocks would be required (see  REF _Ref437058894 \h  \* MERGEFORMAT Figure 11). Again, larger k values are not necessarily better. Rather than having 2 groups of 8 each, with 7 padding blocks, there should be 2 groups of 5 blocks (i.e., k=5), with only one padding block. This is just as efficient in terms of erasure correction (it still uses only 2 groups) but greatly reduces the number of padding blocks.  EMBED Word.Picture.8  Figure  SEQ Figure \* ARABIC 11. Avoiding padding overhead by selecting smaller k. In general, when transmitting S blocks, with kmax= nMaxK) return nMaxK; return ceiling(nBlocks/nGroups); } Figure  SEQ Figure \* ARABIC 12. Selection of optimal k value. Let the wasted transmission due to padding be w. Naively using k= kmax can yield w as high as kmax -1, regardless of the transmission size. Minimizing k, as above, means that w will be at most d. As a fraction of the file length, this is:  EMBED Equation.3  Therefore, the fraction of waste due to padding will always be less than 1/ kmax. For S ( kmax, the overhead is zero. For kmax < S < kmax2, the overhead is limited to 1/ kmax. For S > kmax2 the overhead is at most 1/ kmax, and tends to zero as the transmission size grows. With kmax = 32, file sizes from 32 to 1024 (k to k2) have an overhead of at most 3%, with an average of 1.32% and a median of 1.25%.  REF _Ref440268038 \* MERGEFORMAT Figure 13 shows the padding overhead for files from size 0 to 2500 blocks, with kmax=32. So far, we have assumed k must be the same for all groups in the file. However, as we carry k in each packet, we have the option to vary k for each group. Suppose that we have discovered the optimal k=k0, as above and that S = (d+1)k0 p, where p>k, so keeping FEC blocks in memory or on disk may consume much more space than the original file. Furthermore, in some cases the time penalty to pre-compute and write this file may not be acceptable. Fcast does not support this precomputation feature, but may support it as an option in a future release. The Fcast receiver has a more complicated task than the sender. Blocks may not arrive in the order sent, portions of the data stream may be missing, and redundant blocks must be ignored. Because the receiver is designed to reconstruct the file(s) regardless of the senders block transmission order, the receiver does not care to what extent the block receipt is out of order, or if there are gaps in the senders data stream. The receiver keeps track of how many blocks have been received for each group and what the block index values are. As each block is received, the receiver tests: Does the block belong to the Fcast session? Is it a new block (not a duplicate of one already received)? Is the block for a group that is still incomplete? (a group is complete when k distinct blocks are received) If a block fails any of these tests, it is ignored. Otherwise, it must be stored. In designing Fcast we considered five possible schemes to deal with received packets: In-Memory, Receive-Order, Group-Area, Crowd-Bucket, and Bucket-per-Crowd. The In-Memory scheme supposes that the receiver has enough RAM to hold the entire file (plus metadata). Blocks are received, sorted and decoded all in main memory. Finally, it is written to disk. Naturally, this approach cannot scale to large files. Receive-Order simply writes all blocks to disk in the order they are received. When reception is complete (i.e., k blocks have been received for each group) the file is sorted into groups prior to decoding. This scheme allows fast reception (sequential writing to disk is fast) but suffers delays in the sorting and decode phases. Such sorting delays can be significant for large transfers. Note also that in-place sorting typically requires disk space of twice the file size. All the other schemes presented here require only the disk space of the transmitted file (plus the metadata trailer). The Group-Area scheme writes blocks to the area of the file corresponding to their group (i.e., for group g, the k blocks beginning at block kg). Blocks are received into one of two single-block buckets in RAM. While one bucket is receiving, the other is written to disk. The block is stored in the next empty block position within the group, which may not be its final location within the file (see  REF _Ref437753060 \h Figure 14). Once reception is complete, each group is read into memory, decoded, and then written back in-place. This scheme avoids sorting delays in the decode phase, but must perform a random disk seek to write each block (in a perfect reception it will be seeking from one group to the next to write each block). The round-robin transmission order challenges file caching mechanisms. In fact, disk caching may slow down writing. For example, if a disk cache uses 4KB pages, then a 1 KB write operation may involve a 4KB read (to get the page) followed by a 4KB write. To prevent such problems, blocks are written in unbuffered mode during reception. However, even in unbuffered mode, 1 KB writes are so small as to allow disk latencies to dominate total writing time, making writing inefficient.  EMBED Word.Picture.8  Figure  SEQ Figure \* ARABIC 14. Group-Area reception method. The Crowd-Bucket scheme assigns multiple groups of blocks to crowds ( REF _Ref437753034 \h Figure 15). Blocks are written as in the Group-Area scheme, but rather than writing to the next available position in the group, they are written to the next available position in the crowd. In order to write several blocks as a single sequential write, blocks are buffered in several buckets, each of size b, before writing. The crowd size is set to be b groups. As long as a bucket is not full, and the incoming block is from the same crowd, no writes are performed. When the bucket is full, or the crowd has changed, then the bucket are written out to the next available free space in the appropriate crowd position in the file. When the Crowd-Bucket scheme has completed reception, each crowd is then read into memory and sorted into its constituent groups. The groups are decoded and then written back to the file. This requires no more disk I/O than is minimally required by decoding (one read/write pass). As sorting time should be small compared to decoding time, performance will be good. The cost is in memory: kb blocks of memory are required to read the crowd into memory to perform the sort. Note that Group-Area is a special case of Crowd-Bucket, with b=1. If groups are always sent in order starting with the first group, then Crowd-Bucket only requires two buckets while receiving: one to receive into while the other is written. However, as mentioned previously, group send order should be randomized in order to prevent periodic loss leading to correlated group loss. The sender could randomly permute crowds rather than groups, but as crowds are a receiver concept and would have their size adjusted by receiver disk speed and memory availability, it is not suitable to have the sender select a crowd size. A better choice would be to have the sender randomly select the first group to send, and then proceed in order, modulo the number of groups. That is, it could randomly select x in the interval [1,G], and send groups x through G, followed by groups 1 through x-1. The position of a group in a round is random, so correlated loss of the same group due to periodic losses is prevented. If the sender only randomizes the selection of the first group in a round, then the Crowd-bucket scheme can operate with 3 buckets. If the first block sent in a round is from the first group of a crowd, then only two of the buckets need be used, and writing can operate as described above (receive into one while the other writes). If the first block is not from the first group of its crowd, then the crowd will be split up so some of its blocks are sent at the start of the round, and some at the end. In this case, one bucket is dedicated to receiving blocks from that crowd. The third bucket is only written when the round is complete. All other crowds are received and written as described above using the other two buckets. (Note: as kb blocks of memory are required to decode and sort, and typically k>2, using 3 buckets instead of 2 has no impact on overall memory requirements in fact, k buckets might as well be used to allow for variations in disk performance). However, when a receiver may subscribe to an arbitrary number of layers, it is difficult to say anything about the group order of received packets. Therefore, crowd-bucket is only applicable for a single layer.  EMBED Word.Picture.8  Figure  SEQ Figure \* ARABIC 15. Crowd-Bucket reception method. The Crowd-Pool scheme also arranges groups into crowds. Like the crowd-buckets scheme, it writes blocks to the next available position in the crowd, and performs a single pass sort and decode when all receiving is complete. However, rather than having 2 buckets, it maintains a pool of block-sized buffers. With the Crowd-Pool method there need not be b groups per crowd more or less are possible. Also, the Crowd-Pool scheme does not constrain the randomization of group order as the Crowd-Bucket scheme does in fact, it does not make any assumptions about the send order, making it suitable for layered transmissions. There are two ways to implement crowd-pool. One uses gather writing, i.e. the ability to gather a number of discontiguous blocks of memory into a single contiguous write. The other way, non-gather, does not assume this capability, so discontiguous memory blocks must be copied to a contiguous write buffer before writing. The gather implementation works as follows: Let the number of crowds be c, and the number of groups per crowd be g. Thus, g=S/ck. Let the buffer pool consist of bc + b c + 1 blocks. Whenever a crowd has more than b blocks buffered in the pool, it writes b blocks to disk. The write is performed as a single contiguous write in the next available space in the crowd. Whenever all crowds have less than b blocks buffered, then any crowd may be chosen arbitrarily, and its blocks are written to disk.  REF _Ref437752929 Figure 16 illustrates the gather implementation of Crowd-Pool. For a proof that bc+b-c+1 blocks of memory are sufficient for the gather implementation of Crowd-Pool, see [ seq bibitem GEM99 \* MERGEFORMAT 10].  EMBED Word.Picture.8  Figure  SEQ Figure \* ARABIC 16. Crowd-Pool reception method (gather implementation). Now suppose gather writing is not possible. Let a write buffer of size b blocks be used along with a pool of bc-c+1 blocks, for a total which is still bc+b-c+1 blocks of memory. Whenever any crowd has at least b blocks in the pool, b blocks are copied to the write buffer and a write is initiated. Whenever all crowds have less than b blocks buffered in the pool, then a crowd is chosen arbitrarily to have its blocks copied to the write buffer, and the write is initiated. Thus, it is possible to avoid requiring gather writing at the expense of extra memory-to-memory copies (which should not be significant).  REF _Ref454152643 Figure 17 illustrates the non-gather implementation of Crowd-Pool. For a proof that bc+b-c+1 is sufficient memory, see [ seq bibitem GEM99 \* MERGEFORMAT 10].  EMBED Word.Picture.8  Figure  SEQ Figure \* ARABIC 17. Crowd-Pool reception method (non-gather implementation). The memory required to implement Crowd-Pool is, as we have stated, bc+b-c+1. However, in order to sort and decode a crowd in one pass, there must also be sufficient memory to hold an entire crowd. A crowd consists of S/c blocks. Therefore, the memory required to cache, sort and decode is m( max(S/c, bc+b-c+1). As a function of c, S/c is decreasing, while bc+b-c+1 is increasing. Therefore, the minimum memory required is found at their intersection, which is at (b-1)c2 + (b+1)c S = 0 . This can be solved for c to obtain:  EMBED Equation.3  substituting into m=bc+b-c+1 yields  EMBED Equation.3  So we see that as S gets very large m can be approximated by (S(b-1))1/2, which grows with the square root of the file size. The above uses continuous functions, but, of course, all the variables must have integer values. In practice, g should calculated as  EMBED Equation.3  Now g must be an integer and be at least one. Let gc=max(1,(g() and gf max(1,(g(). Then calculate  EMBED Equation.3  and m=max(gck, bc-c+b+1, S/c). Perform the same calculations using gf in place of gc, and then select gf or gc according to which one requires less memory. MethodMemory required (blocks)Disk space required (blocks)Total Disk I/O requiredExperimental rate (Mbps)In-MemorySSS sequentialNetwork speedReceive-Order12SS random + 4S sequential29.1Group-Areas1SS random (size 1) + 2S sequential1.2Crowd-BucketsKbSS/b random (size b) + 2S sequential2.7Crowd-Pool (Sb) (approx)SS/b random (size b) + 2S sequential2.7Table  SEQ Table \* ARABIC 1. Comparison of receive methods.  REF _Ref437835852 \h  \* MERGEFORMAT Table 1 compares the memory, disk space and I/O requirements of the five methods. It is clear that both Crowd-Bucket and Crowd-Pool achieve low I/O costs, while maintaining low disk space and reasonable memory requirements. To compare their memory requirements, one must look at the parameter values.  REF _Ref437835388 \h  \* MERGEFORMAT Figure 18 illustrates this for k=64 and b set to 16 or 128. Note that when b=16, Crowd-Pool uses more memory for all but the smallest files. With b=128, Crowd-Pool uses less memory for files up to about 0.5 GB. Therefore, low bandwidth applications that need a relatively small b value can use the simpler Crowd-Bucket scheme, while higher bandwidth applications sending relatively small files would save memory by using Crowd-Pool. For modern PCs, b=8 should suffice to handle transfers on the order of several Megabits per second.  Figure  SEQ Figure \* ARABIC 18 - Memory requirements for crowd-buckets and crowd-pool schemes; k=64, block size 1KB. To support Crowd-Bucket, the sender must restrict randomization of send order to crowds, meaning the sender must select a crowd size. However, for Crowd-Pool, the receiver selects the crowd size, and the sender may send in any order. A receiver with memory m could determine the receiving strategy as follows: If m(S then use In-Memory. Otherwise, find the largest b that m can support using Crowd-Pools; call it bp. Let the maximum b that m can support (m/k) using Crowd-Bucket be bb. If bb> bp and bb>1 use Crowd-Buckets. Otherwise, if bp>1 use Crowd-Pools. Otherwise, use Group-Area. If the receive method selected above cannot keep up with the receive rate, switch to Receive-Order. Finally, we note that decoding a group could begin as soon as k packets have been received for the group, in parallel with reception of other packets. However, disk I/O is required to fetch the packets for decoding and then write the results; such I/O may interfere with the writes required for new packets received. In the current version of Fcast, decoding is deferred until all groups have completed. In the future we may add parallel decoding, but only as a lower priority I/O activity to writing newly received packets. In satellite mode, a new file may start being received while the previous one is still decoding. As no special priority is given to writing received data, care should be taken when using satellite transfer mode to target bandwidths such that the receiver can simultaneously decode and receive, or else space out transmissions such that simultaneous decode and receive will never be required. Conclusion and Future Work Fcast file transfer is as scalable as IP multicast. Fcast handles receivers with heterogeneous loss characteristics. It requires no feedback-channel communication, making it applicable to satellite transmissions. This paper described the Fcast design, API, and implementation. It discussed how Fcast optimally selects the k parameter to minimize transmission overhead while maintaining the best loss resilience. It also explained how to efficiently use the disk at the sender, and, more critically, at the receiver. We are considering several enhancements to Fcast. When data is compressed, it would improve performance to perform decompression in combination with decoding, so that there is only one step of read/decode/decompress/write, instead of read/decode/write + read/decompress/write. We are working on implementing layered transmission. In [ seq bibitem VIC97 \* MERGEFORMAT 2], each layers bandwidth is equal to the sum of bandwidth of all lower layers. We are investigating generalizing this scheme to allow alternative bandwidth allocations. Fcast has been used on the Microsoft campus to distribute nightly updates of Windows 2000 a 336 MB download. It has also been incorporated into a multicast PowerPoint add-in, to distribute master slide information in one-to-many telepresentations. By the time this paper appears, Fcast should be freely available on the World Wide Web to allow distributors of software and other popular content to avoid the catastrophes associated with the midnight madness download scenario. Acknowledgements The authors wish to acknowledge the helpful comments of Dave Thaler and Shreedhar Madhavapeddi. References [ SEQ bibitem \* MERGEFORMAT 1] Rizzo, L, and Vicisano, L., Reliable Multicast Data Distribution protocol based on software FEC techniques, Proceedings of the Fourth IEEES Workshop on the Architecture and Implementation of High Performance Communication Systems, HPCS97, Chalkidiki, Greece, June 1997. [ SEQ bibitem \* MERGEFORMAT 2] Vicisano, L., and Crowcroft, J., One to Many Reliable Bulk-Data Transfer in the Mbone, Proceedings of the Third International Workshop on High Performance Protocol Architectures, HIPPARCH 97, Uppsala, Sweden, June 1997. [ SEQ bibitem \* MERGEFORMAT 3] Floyd, S., Jacobson, V., Liu, C., McCanne, S., and Zhang, L., A Reliable Multicast Framework for Light-weight Sessions and Application Level Framing, Proceedings ACM SIGCOMM 95, Cambridge, MA, Aug 1995. [ SEQ bibitem \* MERGEFORMAT 4] Kermode, R., Scoped Hybrid Automatic Repeat ReQuest with Forward Error Correction (SHARQFEC) , Proceedings ACM SIGCOMM '98, Vancouver, Canada, Sept 1998. [ SEQ bibitem \* MERGEFORMAT 5] Paul, S., Sabnani, K.K., Lin, J. C.-H., and Bhattacharya, S., Reliable Multicast Transport Protocol (RMTP), , IEEE Journal on Selected Areas in Communications. Special Issue for Multipoint Communications, Vol. 15, No. 3, pp. 407 421, Apr 1997. [ SEQ bibitem \* MERGEFORMAT 6] Acharya, S., Franklin, M., and Zdonik, S., Dissemination-Based Data Delivery Using Broadcast Disks, IEEE Personal Communications, pp.50-60, Dec 1995. [ SEQ bibitem \* MERGEFORMAT 7] Blahut, R.E., Theory and Practice of Error Control Codes, Addison Wesley, MA 1984. [ SEQ bibitem \* MERGEFORMAT 8] Rizzo, L., and Vicisano, L., Effective Erasure Codes for Reliable Computer Communication Protocols, ACM SIGCOMM Computer Communication Review, Vol.27, No.2, pp.24-36, Apr 1997. [ SEQ bibitem \* MERGEFORMAT 9] Byers, J.W., Luby, M., Mitzenmacher, M., and Rege, A., A Digital Fountain Approach to Reliable Distribution of Bulk Data, Proceedings ACM SIGCOMM '98, Vancouver, Canada, Sept 1998. [ SEQ bibitem \* MERGEFORMAT 10] Gemmell, Jim, Schooler, Eve, and Gray, Jim, Fcast Scalable Multicast File Distribution: Caching and Parameters Optimizations, Microsoft Research Technical Report, MSR-TR-99-14, June 1999.  Naturally, to support higher-speed downloads, a higher speed connection would be required. It is likely that several simultaneous Fcasts would be performed at various speeds (e.g., 12, 24, 50, 100, 200, and 300 kbps). The receiver would select an appropriate transmission speed and tune in to the corresponding Fcast. Layered schemes are also possible (see the Conclusion).  At the very beginning of the transmission, the file may be sent in order. This avoids some disk performance problems mentioned later in the paper. However, this will only benefit receivers that tune in from the very beginning. As we want to support receivers tuning in any time that is convenient to them in the transmission, we will not consider this start-up phase in our analysis, although it is certainly worth implementing.  In the case of a batch file (.zip, .tar, .cab,) that contains many individual files in a standard format, Fcast manages only the meta-information of the batch file itself.  It is easy to prove from the derivation of k0 that k0 ( kmax/2. With kmax = 32, k0 ( 16. The difference between k=15 and k=16 will be very slight (at least until the loss rate approaches 100%).  Without pre-computation, k packets must be read to create one encoded packet. Thus, the reading throughput must be k times the sending rate. However, the k blocks are sequential, so the read is efficient. (Performing a sequential k block read is not k times slower than performing k ransom block reads as seek and rotation times, along with other overhead, mask the impact of the length of read.)  Unbuffered I/O requires writes that are multiples of the disk sector size often 512 bytes. We use a fixed block size of 1024 bytes in this first version of Fcast.  Using a Pentium 266 running Windows 98 with IDE hard drive. Crowd methods used a crowd size of 4. Receive order did sequential unbuffered 8 KB writes.  This assumes an -2pass sort which uses order sqrt(Sb) memory for the sort to generate the runs and them merge them.  The multicast PowerPoint add-in and Fcast will be made available at http://research.microsoft.com/barc/mbone/ PAGE 7 PAGE 24 PAGE \# "'Page: '#' '"  The old intro made it sound like we were bragging about IE. I rewrote it to make the first 2 paragraphs more engaging. Hope it worked. 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