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P118 gummadi


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P118 gummadi

  1. 1. Measurement, Modeling, and Analysis of a Peer-to-Peer File-Sharing Workload Krishna P. Gummadi, Richard J. Dunn, Stefan Saroiu, Steven D. Gribble, Henry M. Levy, and John Zahorjan Department of Computer Science and Engineering University of Washington Seattle, WA 98195 {gummadi,rdunn,tzoompy,gribble,levy,zahorjan}@cs.washington.eduABSTRACT Categories and Subject DescriptorsPeer-to-peer (P2P) file sharing accounts for an astonishing C.4 [Performance of Systems]: Modeling Techniques;volume of current Internet traffic. This paper probes deeply C.2.4 [Computer-Communication Networks]: Distrib-into modern P2P file sharing systems and the forces that uted Systems—Distributed applicationsdrive them. By doing so, we seek to increase our under-standing of P2P file sharing workloads and their implica- General Termstions for future multimedia workloads. Our research usesa three-tiered approach. First, we analyze a 200-day trace Measurement, performance, designof over 20 terabytes of Kazaa P2P traffic collected at theUniversity of Washington. Second, we develop a model of Keywordsmultimedia workloads that lets us isolate, vary, and explore Peer-to-peer, multimedia workloads, Zipf’s law, modeling,the impact of key system parameters. Our model, which we measurementparameterize with statistics from our trace, lets us confirmvarious hypotheses about file-sharing behavior observed inthe trace. Third, we explore the potential impact of locality- 1. INTRODUCTIONawareness in Kazaa. A decade after its birth, the Internet continues its rapid Our results reveal dramatic differences between P2P file and often surprising evolution. Recent studies have shownsharing and Web traffic. For example, we show how the a dramatic shift of Internet traffic away from HTML textimmutability of Kazaa’s multimedia objects leads clients pages and images and towards multimedia file sharing. Forto fetch objects at most once; in contrast, a World-Wide example, a March 2000 study at the University of WisconsinWeb client may fetch a popular page (e.g., CNN or Google) found that the bandwidth consumed by Napster had edgedthousands of times. Moreover, we demonstrate that: (1) ahead of HTTP bandwidth [28]. Only two years later, a Uni-this “fetch-at-most-once” behavior causes the Kazaa popu- versity of Washington study showed that peer-to-peer filelarity distribution to deviate substantially from Zipf curves sharing dominates the campus network, consuming 43% ofwe see for the Web, and (2) this deviation has significant all bandwidth compared to only 14% for WWW traffic [29].implications for the performance of multimedia file-sharing When comparing these bandwidth consumption statisticssystems. Unlike the Web, whose workload is driven by doc- to those in a 1999 study of a similar campus workload [37],ument change, we demonstrate that clients’ fetch-at-most- the portion of network bytes ascribed to audio and videoonce behavior, the creation of new objects, and the addition increased by 300% and 400%, respectively, over a 3-year pe-of new clients to the system are the primary forces that drive riod. Without question, multimedia file sharing has becomemultimedia workloads such as Kazaa. We also show that a dominant factor in today’s Internet. In all likelihood, itthere is substantial untapped locality in the Kazaa workload. will dominate the Internet of the future, barring a chillingFinally, we quantify the potential bandwidth savings that effect from legal assaults.locality-aware P2P file-sharing architectures would achieve. Two traits of today’s file-sharing systems distinguish them from Web-based content distribution. First, current file- sharing systems use a “peer-to-peer” design: peers volun- tarily provide resources as well as consume them. Because of this, the system must dynamically adapt to maintain ser-Permission to make digital or hard copies of all or part of this work for vice continuity as individual peers come and go. Second,personal or classroom use is granted without fee provided that copies are file-sharing is being used predominantly to distribute mul-not made or distributed for profit or commercial advantage and that copies timedia files, and as a result, file-sharing workloads differbear this notice and the full citation on the first page. To copy otherwise, to substantially from Web workloads [29]. Multimedia files arerepublish, to post on servers or to redistribute to lists, requires prior specific large (several megabytes or gigabytes, compared to severalpermission and/or a fee.SOSP’03, October 19–22, 2003, Bolton Landing, New York, USA. kilobytes for typical Web pages), they are immutable, andCopyright 2003 ACM 1-58113-757-5/03/0010 ...$5.00. there are currently far fewer distinct multimedia files than
  2. 2. Web pages. Video-on-demand (VoD) systems also distribute trace length 203 days, 5 hours, 6 minutesmultimedia files; we contrast our Kazaa measurements and # of requests 1,640,912 # of transactions 98,997,622analysis with related work on VoD systems in Section 3. # of unsuccessful transactions 65,505,165 (66.2%) This paper presents an in-depth analysis of a modern P2P 252KB (all transactions) average transaction sizemultimedia file-sharing workload, considering the “peer-to- 752KB (successful transactions only)peer” and the “multimedia” aspects of the workload inde- # of users 24,578pendently. Our goals are: # of unique objects 633,106 (totaling 8.85TB) bytes transferred 22.72TB 1. to understand the fundamental properties of multime- content demanded 43.87TB dia file-sharing systems, independent of the design of the delivery system, Table 1: Kazaa trace summary statistics, 5/28/02 to 12/17/02. A transaction refers to a single Kazaa HTTP 2. to explore the forces driving P2P file-sharing work- transfer; a request refers to the set of transactions a loads so we can anticipate the potential impacts of client issues while downloading an object, potentially future change, and in pieces from many servers. Clients are identified by Kazaa username. We only present statistics for down- 3. to demonstrate that significant opportunity exists to loads made by university-internal clients for data on optimize performance in current file-sharing systems university-external peers. by exploiting untapped locality in the workload. To meet these goals, we employ several approaches. First,we analyze a 200-day trace of Kazaa [19] traffic that we ies have described high-level characteristics of P2P work-collected at the University of Washington between May and loads [6, 29, 30, 32]. Our goals in this section are to: (1)December of 2002. We traced over 20 terabytes of traffic dig beneath these high-level studies to uncover the processesduring that period, from which we distill several key lessons that drive the workloads, and (2) demonstrate ways in whichabout the Kazaa workload. Second, we derive a model of these processes fundamentally differ from those of the Web.this multimedia traffic based on our analysis. The model 2.1 Trace Methodologyhelps to explain the root causes of many of the trends shownby Kazaa and to predict how the trends may change as the The data in this section are based on a 200-day traceworkload evolves. Third, we use trace-driven simulation to of Kazaa peer-to-peer file-sharing traffic collected at thequantify the significant potential that exists to improve the University of Washington between May 28th and Decemberperformance of multimedia file-sharing in our environment. 17th, 2002. The University of Washington (UW) is a large Our analysis reveals that the Kazaa workload is driven campus with over 60,000 faculty, students, and staff. Table 1by considerably different forces than the Web. Kazaa ob- describes the trace, which saw over 20 terabytes of incomingjects are immutable, and as a result, the vast majority of data resulting from 1.6 million requests. Our trace was longits objects are fetched at most once per client; in contrast, enough for us to observe seasonal traffic variations, includ-Web pages (e.g., Google or CNN) can be fetched thousands ing the end of spring quarter in June, the summer months,of times per client. Our measurements show that the pop- and the start and end of the fall quarter. We also observedularity distribution of Kazaa objects deviates substantially the impact of bandwidth rate-limiting instituted by the uni-from the Zipf curves we commonly see for the Web, and versity’s networking organization midway through the traceour model confirms that the “fetch-at-most-once” behavior in an attempt to control the cost of Kazaa traffic.1of Kazaa clients is the cause. Our model demonstrates an- We collected our trace using hardware and software in-other consequence of object immutability: unlike the Web, stalled at the network border between the university andwhose workload is driven by document change, the primary the Internet. Our hardware consists of a 2.0 GHz Pentium-forces in Kazaa are the creation of new objects and the addi- III workstation that monitored traffic flowing between thetion of new users. Without these forces, fetch-at-most-once university and the Internet. Our workstation had sufficientbehavior would drive the system to stagnation. CPU and network capacity to ensure that no packets were The structure of this paper follows the multi-tiered ap- dropped, even during peak load. An adjacent workstationproach cited above. Section 2 describes our trace method- acted as a one-terabyte file store for archiving trace data.ology and presents our trace-based analysis of Kazaa. In Our software used a kernel packet filter [24] to deliver TCPSection 3, we analyze the popularity distribution of Kazaa packets to a user-level process, which identified HTTP re-requests and the forces that shape it. Section 4 uses our quests within the TCP flows. Throughout our trace, theobservations and analysis to develop an analytical model; packet filter reported packet drop rates of less than 0.0001%.we then use the model to explore the processes that drive We made all sensitive information anonymous – includingKazaa’s behavior in greater depth. Section 5 considers the IP addresses, URLs, usernames, and object names – beforeperformance potential of bandwidth-saving techniques sug- compressing and storing the trace. Overall, our tracing andgested by our modeling and analysis. We describe research analysis software consists of over 30,000 lines of code.related to our study in Section 6, while Section 7 summarizes Our hardware monitored all incoming and outgoing traf-our results and presents conclusions. fic. However, the data presented in this paper (including Table 1) are for one direction only: requests made by uni- versity-internal peers to download data stored on university-2. THE MEASURED PROPERTIES OF P2P 1 FILE-SHARING WORKLOADS The imposed rate limits bounded upload traffic out of the university’s dormitory population and had little effect on This section uses our trace data to identify key properties download traffic (to the dorms or to the university as aof the Kazaa multimedia file-sharing system. Recent stud- whole), which is the focus of our research.
  3. 3. external peers. This unidirectional trace captures all re- 1quests issued by a stable, complete user population over a 0.9 <10MB objectsperiod of time. Kazaa control traffic, which consists primar- 0.8 % of requests (CDF)ily of queries and their responses, is encrypted and was not 0.7captured as part of our trace. 0.6 Throughout this paper, the term “user” refers to a person 0.5 100MB+ objectsand “client” refers to the application instance running on be- 0.4 0.3half of a user. We assume there is largely a one-to-one corre- 0.2spondence between users and specific application instances 0.1in our environment (although this may not always be true); 0therefore, we draw conclusions about users based on observa- 5 -3 mins 1 hour 1 day 1 week 150 daystions of clients in our trace. Note, however, that client-side download latencycaches may absorb some requests from users, meaning thatthe client request rate, which we observe in our trace, may Figure 1: Users are patient. A CDF of object transferbe lower than the true user request rate, which we cannot times in Kazaa for small (<10 MB) and large (100 MB+)directly observe. objects. The X-axis is on a log-scale. Kazaa clients supply Kazaa-specific “usernames” as anHTTP header in each transaction. We use these usernames(rather than IP addresses) to distinguish between differ- filtering removed 0.4% of total transactions (0.3% of bytes)ent users in our trace. Unfortunately, an unofficial ver- from our trace.sion of Kazaa, called “KazaaLite,”2 became popular dur-ing our tracing period and is compiled with a predefined 2.2 User Characteristicsusername embedded in the application itself. We “special- Our first slice through the trace data focuses on the prop-case” requests from KazaaLite, resorting to distinguishing erties of Kazaa users. Previous studies [29, 2] have shownbetween KazaaLite users by their IP addresses. Although that peer-to-peer users in general are “greedy” (i.e., mostDHCP is used in portions of our campus, and identifying users consume data but provide little in return) and haveusers by IP address is known to have issues when DHCP is poor availability [30]. We confirm some of these character-present [6], only 5.7% of transactions in our trace were from istics, but we also explore others, such as user activity.KazaaLite clients. Furthermore, KazaaLite clients did notappear within the first 59 days of our 203 day trace. 2.2.1 Kazaa Users Are Patient Kazaa file-transfer traffic consists of unencrypted HTTP As any Web-based enterprise knows, users are very sensi-transfers; all transfers include Kazaa-specific HTTP head- tive to page-fetch latency. Beyond a certain small thresholders (e.g., “X-Kazaa-IP”). These headers make it simple to (measured in seconds), they will abandon a site and movedistinguish between Kazaa activity and other HTTP activ- to another, possibly competing, site. For this reason, manyity. They also provide enough information for us to identify online businesses engage services such as Keynote [20] to tellprecisely which object is being transferred in a given trans- them quickly if their servers are not sufficiently responsive.action. When a client attempts to download an object, that In the world of the Web, users expect instant gratificationobject may be downloaded in pieces (often called “chunks”) and are unforgiving if they do not receive it.from several sources over a long period of time. We define a In this context, the behavior of Kazaa users is surprising.“transaction” to be a single HTTP transfer between a client Figure 1 shows the distribution of transfer times in Kazaa;and a server, and a “request” to be the set of transactions a transfer time is defined as the difference between the startclient participates in to download an entire object. A failed time of the first transaction and the end time of the lasttransaction occurs when a client successfully contacts a re- transaction of a given user request. We filtered out partialmote peer, but that remote peer does not return any data, requests (i.e., we only counted transfers for which the userinstead returning an HTTP 500 error code. eventually obtained the entire object). To deal with edge A single request may span many minutes, hours, or even effects, we ignored requests for which at least one transac-days, since the Kazaa client software will continue to at- tion occurred during the first month of the trace; note thattempt to download the object long after the user has asked this will tend to result in an underestimate of user patience.for it. Occasionally, a client may download only a subset of We present our results in terms of requests for “small” ob-the entire object (either because the user gives up or because jects (less than 10MB, typically audio files) and requests forthe object becomes permanently inaccessible in the middle “large” objects (more than 100MB, typically video files). Asof a request). We call this a “partial request.” we will show in Section 2.3.1, this is a natural and represen- The Kazaa application has an auto-update feature, mean- tative way to decompose the overall that a running instance of Kazaa will periodically check The results show incredible patience on the part of Kazaafor updated versions of itself. If found, it downloads the new users. Even for small objects, 30% of the requests take overexecutable over the Kazaa network. We chose to filter out an hour, and 10% take nearly a day. For large requests, lessthese auto-update transactions from our logs, as they are than 10% complete in an hour, 50% take more than a day,not representative of multimedia requests from users. Such and nearly 20% of users are willing to wait a week for their2 downloads to complete! From this graph, it is clear that the Many more unofficial versions of Kazaa that use generic dynamics of multimedia downloads differ totally from Webusernames have appeared since our trace period finished;precisely distinguishing between peer-to-peer users will be- usage. The Web is an interactive system where users wantcome very difficult, given that neither IP addresses nor an immediate response. Kazaa is a batch-mode delivery sys-application-specific usernames are unique. tem, where downloads are made in the background and the
  4. 4. 8 6000 1.4 1 TB requested data requested 7 population size 1.2 probability of request 5000 0.8 data requested per makes >0 requests population size 6 probabilty a client live client (GB) 1 TB requested 4000 5 0.6 0.8 4 3000 0.6 0.4 3 2000 0.4 2 0.2 1000 0.2 1 0 0 0 0 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 week weekFigure 2: Bytes requested by the population and attrition Figure 3: Older clients have slower request rates. On av-as a function of age. “Older” clients request a smaller erage, clients use the system equally often as they agefraction of bytes than newer clients. There are fewer old (having approximately a 50% chance of using the systemclients than young clients, but attrition occurs at a more any given week), but they request less data per sessiongradual rate than the slowdown in bytes requested. as they age. Note that the point corresponding to new clients (week 1) is artificially high, since by definition every new client requests an object immediately.content is examined later. Users do not wait for their con-tent to arrive. They go about their business and eventuallyreturn to review the content after it has been received. request rates for two reasons: (1) they may use the system less often, or (2) they may ask for less when they use the2.2.2 Users Slow Down As They Age system. Figure 3 shows that clients are equally likely to use An interesting question we explored is how user interest the system regardless of age: on average, clients have aboutin Kazaa varies over time. Do users become hungrier for a 50% chance of making a request on any given week. Oldercontent as they gain experience with Kazaa? Are their re- clients slow down because they ask for less each time theyquest rates relatively constant? Do they lose interest over use the system, not because they use the system less often.time? The answer to such questions significantly affects the In summary, new clients generate most of the load ingrowth of the system. Kazaa, and older clients consume fewer bytes as they age. To understand user behavior over time, we first calcu- In part, this is because of attrition: clients leave the systemlated the average number of bytes consumed by clients as a permanently as they grow older. Also, older clients tend tofunction of age. The methodology for this measurement is interact with the system at a constant rate, but ask for lesscomplex: our trace has finite length, so we must avoid end during each interaction.effects that would overcount short-lived or undercount long-lived users. We compensate, first, by counting transferred 2.2.3 Client Activitybytes only from clients whose “births” we could observe. Be- Quantifying the availability of clients in a peer-to-peer sys-cause there are no detectable birth events in our trace, we tem is notoriously difficult [6]: no one metric can accuratelyused the heuristic of treating the first observed download capture availability in this environment, since any individ-from a client as a birth event if at least a full month had ual client might exist only for a fraction of the traced timeelapsed in our trace before seeing that first download. To period. Given our passive tracing methodology, we facedcompensate at the end of the trace, we counted bytes only an additional methodological problem: we can detect thatfrom clients born prior to the last 11 weeks of the trace. users are participating in the system only when their clientsBecause of this “end threshold,” we could draw definitive transfer data (either by downloading or uploading files). If aconclusions about clients’ behavior only during the first 11 client is on-line but not active, we could not observe them.weeks of their lifetimes. Because of this, we report statistics about client activity Figure 2 shows the total number of bytes requested by only, which is a lower bound on availability.3the population as a function of its age. From this graph, we We use two specific metrics to quantify the amount ofcan see that older clients consume fewer bytes than newer client transfer activity: (1) activity fraction, which measuresclients. There are two reasons for this effect: (1) attrition the fraction of time a client is transferring content over thereduces the number of older clients, since clients may “die” client’s lifetime or over the duration of the entire trace, and(i.e., leave the system forever) over time, and (2) some clients (2) average session length, in which a session is defined asmay continue to issue requests but do so at a slower rate as an unbroken period of time during which a client has one orthey age. We explore each of these in turn. more active transactions. Average session length measures Attrition. To understand attrition in the system, we an- the typical duration of the periods during which a client isalyzed the number of clients that remain alive as a function receiving or transmitting data. Our measurements indicateof age (also shown in Figure 2). Population size declines at that the distributions of average session length and activitya more gradual rate than bytes requested, at least over the fraction over the measured population are heavy-tailed.first 11 weeks of clients’ lifetimes. Attrition therefore only 3partially explains why older clients demand less in aggregate P2P software is often designed to make it difficult to close the program once it starts, “fooling” users into making theirfrom the system. To fully explain this phenomenon, clients clients more available than intended. Accordingly, we sug-must also slow down their request rates as they age. gest that client activity is a more universally comparable Slowing down over time. Older clients may have slower and stable indicator of “availability” than other metrics.
  5. 5. 1 1 0.9 requests 0.9 bytes transferred % of requests/bytes 0.8 0.8 0.7 0.7 % requests 0.6 0.6 (CDF) 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 % bytes consumed 0.1 0.1 0 0 0.1 1 10 100 1000 <= 10 10-100 >100 object size (MB) object size (MB) (a) (b)Figure 4: Bandwidth consumed vs. object size. (a) CDFs of the total bandwidth consumed and the number of requestsgenerated, as a function of object size, and (b) bandwidth consumed and requests generated as a function of objectsize, grouped into three regions. 2.3 Object Characteristics all clients with >1 clients transfer We now turn our attention to the characteristics of re- activity fraction median 66.08% 5.54% quested objects. Kazaa objects consist of a mixture of media over lifetime 90th percentile 100% 94.41% types, including audio, video, images, and executables [29]. activity fraction median 0.01% 0.20% Our trace captured 1,640,912 requests to 633,106 different over trace 90th percentile 2.29% 4.15% objects, resulting in 98,997,622 transactions. In the remain- total activity median 0.69 hours 10.09 hours over trace 90th percentile 111.93 hours 202.65 hours der of this section, we discuss the most prominent properties average median 2.40 mins 2.40 mins of the requested objects. session length 90th percentile 28.25 mins 28.33 mins 2.3.1 Kazaa Is Not One WorkloadTable 2: Client session lengths and activity fractions. While we tend to think of a system like Kazaa as generat-Summary statistics over two different subsets of our ing a single workload, Kazaa is better described as a blendclient population: all clients, and only those clients that of workloads that each have different properties. Figure 4(a)have transferred more than one file. We show activity shows the percentage of total bandwidth consumed in ourfraction summary statistics relative to both the clients’ trace as a function of object size. The graph shows threelifetimes and to the entire trace. prominent regions: “small” objects that are less than 10 MB in size, medium-sized objects from 10 to a few hundred MB in size, and large objects that are close to 1 GB in size. Relative to the Web, our “small” objects are three orders Table 2 summarizes these metrics. Average session lengths of magnitude larger than an average Web object, while ourare typically small: the median average session length is large objects are nearly five orders of magnitude larger. Thisonly 2.4 minutes, while the 90th percentile is 28.25 minutes. was shown in [29] as well.Most clients have high activity fractions relative to their Figure 4(a) also graphs the percentage of requests ob-lifetimes. However, this is a potentially misleading statistic, served in the trace as a function of object size, demonstrat-since many clients have very short lifetimes, leaving the sys- ing that most requests are for the “small” objects. Tak-tem permanently after requesting only one file. The activity ing a closer look, Figure 4(b) breaks down the number offraction for clients with greater than one request is a more requests and bytes transferred into three regions: 10MBrevealing statistic. The median client in this category was or less, 10MB to 100MB, and over 100MB. We chose theactive only 5.54% of its lifetime, or 0.20% of the entire trace. value 10MB as this corresponds to an obvious plateau inHighly active clients in this category are active for most of Figure 4(a) and it serves as an upper bound on the size oftheir lifetime, but even these clients are typically active for most digitized audio clips. We chose the value 100MB as itonly a short fraction of the trace: the 90th percentile client is a reasonable lower bound on the size of most digitized full-has an activity fraction of only 4.15% over the trace. length movies transferred. While the majority of requests At first glance, our user patience statistics may appear at (91%) are for objects smaller than 10MB (primarily audioodds with our average session length statistics. The median clips), the majority of bytes transferred (65%) are due to thesmall object takes about 17 minutes to download fully, but largest objects (video files). Thus, if our concern is band-the median average session length is only 2.41 minutes. This width consumption, we need to focus on the small numberapparent paradox is reconciled by several facts: (1) many of requests to those large objects. However, if our concerntransactions fail (Table 1), producing many very short ses- is to improve the overall user experience, then we must fo-sions, (2) periods of no activity may occur during a request cus on the majority of requests for small files, despite theirif the client cannot find an available server with the object, relatively small bandwidth demand.and (3) some successful transactions are short, either be- The stark differences between the large and small objectcause servers close connections prematurely or the “chunk” workloads lead us to analyze their behaviors independentlysize requested is small. for much of the evaluation that follows.
  6. 6. small objects large objects cently born objects. Given that there is significant turn- (primarily audio) (primarily video) over in popularity within Kazaa, we wanted to understand top 10 top 100 top 10 top 100 whether objects that become popular are old objects that overlap in the most popular objects have grown in popularity over time or recently born objects 0 of 10 5 of 100 1 of 10 44 of 100 between first and last that enjoy sudden popularity. Using the same month-long 30 days of trace segments as before, we calculated the fraction of objects that # of newly popular objects that are 6 of 10 73 of 95 2 of 9 47 of 56 were newly popular (i.e., in the top-10 or top-100 in the last recently born month of the trace but not the first month of the trace), but did not receive any requests at all during the first month ofTable 3: Object popularity dynamics in Kazaa. There is the trace (i.e., they were likely “born” after the first monthsignificant turnover in the set of most popular objects of the trace). Table 3 shows the results: newly popular ob-between the first 30 days and the last 30 days of the jects tend to be recently born in Kazaa, although this istrace. The newly popular objects (those in the set of more true for audio objects than video objects.most popular objects over the last 30 days but not inthe set over the first 30 days) tend to be recently born. Most requests are for old objects. The previous ex- periments confirmed that the most popular objects tend to decay in popularity, and that the newly popular objects that2.3.2 Kazaa Object Dynamics replace them tend to be newly born. A related, but different, question is whether most requests go to old or new objects. A simple but crucial difference between multimedia and We categorize an object as “old” if at least a month hasWeb workloads is that multimedia objects are immutable, passed since we observed the first request for that object.while Web pages are not. Though obvious, this fact and We categorize it as “new” if it has been less than a monthits implications have not been discussed in the research lit- since it was first requested. Note that we can be sure thaterature. A video clip of “Bambi Meets Godzilla” will be an object is old, but we can never be sure that an objectthe same clip tomorrow or the next day: it never changes. is new, since we may have missed requests for the objectOn the other hand, the Web page may change before our trace began. To deal with edge effects, we do notevery hour, or upon every access if the page is personal- include the first month of requests in our statistics, but weized for clients. Web workloads are thus strongly driven do use them to help distinguish between old and new objectsby dynamic content creation. It has been shown that the in subsequent months.rate of document change is a key factor in Internet behav- Using this methodology, 72% of requests for large objectsior and has enormous implications for caching, performance, go to old objects, while 28% go to new objects. For smalland content delivery in general [11, 37]. We now show how objects, 52% of requests go to old objects, and 48% go to newimmutability affects object dynamics. objects. This shows that a substantial fraction of requests Kazaa clients fetch objects at most once. Because are for the old objects. Large objects requested tend toobjects are immutable and take non-trivial time to down- be older than small objects, reinforcing our assertion thatload, we believe that users typically download a Kazaa ob- Kazaa is really a mixture of workloads: the pace of life isject only once. Our traces confirm that 94% of the time, slower for large objects than for small objects.a Kazaa client requests a given object at most once; 99% From the above discussion, it is clear that the forces driv-of the time, a Kazaa client requests a given object at most ing the Kazaa workload differ in many ways from those driv-twice. In comparison, based on a Web trace we gathered ing the Web. Web users may download the same pages manyduring the first nine days of our Kazaa trace period, a Web times; Kazaa users tend to download objects at most once.client requests a given object at most once only 57% of the The arrival of new objects plays an important role in P2Ptime. file-sharing systems, while changes to existing pages are a The popularity of Kazaa objects is often short- more important dynamic in the Web. We discuss implica-lived. Object immutability also has an impact on object tions of these differences in Sections 3 and 4.popularity dynamics. The set of most popular pages remainsrelatively stable for the Web and these pages account for 2.3.3 Kazaa Is Not Zipfa significant fraction of overall accesses [26]. In contrast, Much has been written about the Zipf-like qualities ofmany of the most popular audio/video objects are routinely the WWW [7]. In fact, researchers commonly quote thereplaced by newly released objects, often in only a few weeks. Zipf parameter of the popularity distributions seen in their To illustrate this change in popular Kazaa objects, we traces [14, 27], in part to demonstrate that their resultscompared the first 30 days of the trace to the last 30 days of are “correct.” This Zipf property of Web access patternsthe trace. For each of these 30-day (month-long) segments, is thought to be a basic fact of nature: a small number ofwe identified the top-10 most popular and the top-100 most objects are extremely popular, but there is a long tail ofpopular objects (Table 3). For small objects, there was no unpopular requests. Zipf’s law states that the popularity ofoverlap between the top-10 most popular objects: the most the ith-most popular object is proportional to i−α , where αpopular small objects had changed completely in the space is the “Zipf coefficient” or “Zipf parameter.” Zipf distribu-of only six months. For large objects, there was only one tions look linear when plotted on a log-log scale.object in common in the top-10 across these segments. The Figure 5 shows the Kazaa object popularity distributiontop-100 objects show only 5 small objects in common across on a log-log scale for large (>100MB) objects, along withthe segments, and 44 objects in common across the large the best-fit Zipf curve; a qualitatively similar curve existsobjects. Popularity is fleeting in Kazaa, and popular audio for small (<10MB) objects. This figure also shows the pop-files tend to lose popularity faster than popular video files. ularity distribution of Web objects, drawn from our Web The most popular Kazaa objects tend to be re- trace. Unlike the WWW, the Kazaa object request distribu-
  7. 7. 10,000,000 servers and the Web. Following this, we present a model of 1,000,000 multimedia workloads in Section 4, and we use this model to explore implications of non-Zipf behavior. 100,000 # of requests WWW objects 10,000 1,000 3. ZIPF’S LAW AND MULTIMEDIA WORK- 100 LOADS Previous studies of multimedia workloads examined ob- 10 100MB+ Kazaa objects ject popularity and found conflicting results, noting both 1 Zipf and non-Zipf behavior [4, 7, 8, 12, 33]. This section 1 10 100 1000 10000 100000 1E+06 1E+07 1E+08 examines previous work in the context of our own, with the object rank goal of explaining the similarities, differences, and causes of the behavior observed both by us and others. We begin byFigure 5: Kazaa is not Zipf. The popularity distribution presenting a hypothesis that explains the non-Zipf behav-for large objects is much flatter than Zipf would predict, ior in Kazaa. Next, we discuss previous studies that havewith the most popular object being requested 100x less observed or modeled non-Zipf workloads and contrast ourthan expected. Similarly shaped distributions exist for hypothesis with previous explanations. Finally, we attemptsmall objects and the aggregate Kazaa workload. For to show the generality of our claim by revealing non-Zipf be-comparison, we show the popularity distribution of Web havior in previously studied workloads. Section 4 then sup-requests during a subset of the Kazaa trace period: the ports our hypothesis through the use of a generative work-Web is well described by Zipf. load model whose output closely matches our observations. 3.1 Why Kazaa Is Not Zipftion as observed in our trace does not seem to follow a Zipf The previous section highlighted a crucial difference be-curve. The strongest difference from Zipf appears among tween the Web and the Kazaa P2P file-sharing system:the most popular objects, where the curves are noticeably Kazaa objects are immutable, while Web objects change,flatter than the Zipf lines. The most popular multimedia sometimes frequently. As we observed in Section 2.3.2, theobjects are significantly less popular than Zipf would pre- immutability of Kazaa objects causes two important effects,dict. For example, our trace shows that the most popular one affecting user behavior and the other object dynam-large object has only 272 requests, and only 47 objects have ics. First, Web clients often fetch the same Web page many100 requests or more. Popularity distributions of Web traffic times (“fetch-repeatedly”). In contrast, Kazaa clients rarelysometimes have slightly flattened “heads,” but the objects request the same object twice (“fetch-at-most-once”). Sec-in the flattened region account for only a small percentage of ond, the Web’s primary object dynamic is updates to exist-total requests and bytes. The flattened portion of the Kazaa ing pages. In contrast, the primary object dynamic in thepopularity curve consists of the top 1000 objects, and these Kazaa workload is the arrival of entirely new objects.objects account for more than 50% of bandwidth consumed. These object and user characteristics can be used as two axes for classifying systems: immutable objects vs. mu-2.4 Summary table objects, and fetch-repeatedly clients vs. fetch- Peer-to-peer file-sharing workloads are driven by consid- at-most-once clients. In systems with large client-sideerably different processes than the Web. Kazaa users are caches, these characteristics are intimately linked: fetch-much more patient than Web users, waiting hours, days, repeatedly behavior exists when there are object updates,and, in some cases, weeks for their objects to arrive. Even such as in the Web, and fetch-at-most-once user behaviorso, the median session length is only 2.4 minutes, because: exists when the only object dynamic is new arrivals, such as(1) many transactions fail, (2) many transactions are short, in the Kazaa system. We believe that these differences inand (3) clients occasionally have periods of no activity while object and user dynamics explain why we observed Zipf re-satisfying a request. As Kazaa clients age, they demand less quest distributions in our measured Web workload, but notfrom the system. Part of this is due to attrition (clients per- in Kazaa.manently leaving the system), but part is also due to older As an initial comparison of fetch-at-most-once and fetch-clients requesting fewer bytes per session. repeatedly behavior, we simulated the request distribution The Kazaa file-sharing workload consists of a mixture of generated by two hypothetical 1,000-user populations: onelarge, immutable audio and video objects, but the measured in which users fetch objects repeatedly, and one in whichpopularity distribution of these objects is not Zipf. The they fetch objects at most once. In both cases, we simulatedpopularity of the most requested objects is limited, since the same initial Zipf distribution (with Zipf parameter α =clients typically fetch objects at most once, unlike the Web. 1.0 over 40,000 objects). However, in the fetch-at-most-onceNonetheless, there is substantial non-uniformity in Kazaa case, we prevented users from making subsequent requestsobject popularity, which suggests that caching is a poten- for the same object. (Section 4 more fully explains the modeltially effective bandwidth savings tool. In Kazaa, object behind this simulation.)arrivals play an important role, while in the Web, updates Figure 6 shows the results when users have made an av-to existing pages are a more important dynamic. erage of 1000 requests each. While fetch-repeatedly behav- In the next section, we explore what we believe are the rea- ior recreates the underlying Zipf distribution, fetch-at-most-sons why Kazaa’s workload does not appear to follow Zipf’s once behavior shows markedly different results: the mostlaw. We also compare Kazaa’s non-Zipf behavior to non- popular objects are requested much less, while objects downZipf behavior in other systems, including video-on-demand the tail show elevated numbers of requests. This behav-
  8. 8. 100000 show up many times (potentially as many times as there fetch-repeatedly are users) in a fetch-at-most-once workload such as Kazaa, 10000 even with individual client caches. Both result in non-Zipf # of requests 1000 distributions, but for different reasons. fetch-at-most-once 100 3.2.2 Kazaa vs. Other Multimedia Workloads 10 Tang et al. [33] show that streaming media server work- loads exhibit flattened popularity distributions similar to 1 those we observed. They attribute this flattened distribu- 1 10 100 1000 10000 100000 tion to the long duration over which they gathered their object rank trace. Their hypothesis is that with a longer trace, more files with similar popularity can be observed, in effect cre-Figure 6: Simulated object popularity for fetch-repeatedly ating “groups” of files with similar properties. Instead ofand fetch-at-most-once behavior. When users fetch an in- Zipf’s law being appropriate for describing the popularity ofdividual object at most once, the resulting request distri- individual files, they show that Zipf’s law better describesbution has a significantly flattened head, approximating the popularity of these groups, and they provide a mathe-the Kazaa popularity distribution. matical transformation inspired by these groups to convert observed non-Zipf distributions into a Zipf-like distribution. We believe that fetch-at-most-once behavior is the likelyior strikingly resembles the measurement results shown in cause of the non-Zipf popularity in their workload. A shortFigure 5, confirming our intuition that fetch-at-most-once trace can cause a non-Zipf workload to appear Zipf-like, asbehavior is a key contributor to non-Zipf popularity. not enough requests have been gathered within the trace for objects with similar popularity to be observed [8]. However,3.2 Non-Zipf Workloads in Other Systems the fact that an adequate sample size will reveal a non-Zipf Non-Zipf popularity distributions have been observed in workload does not explain why this true workload is non-many previous studies [1, 3, 8, 33]. We now compare our Zipf in the first place. The length of a trace by itself doesresults to two categories of previous studies: those that an- not provide a satisfactory explanation of the forces drivingalyzed Web workloads and those that analyzed video (or popularity in a system. Fetch-at-most-once behavior par-other multimedia) workloads. tially provides this explanation. Additionally, in a system that experiences object births (for example, new video ob-3.2.1 Kazaa vs. Web Workloads jects becoming available), a short trace may miss important While most Web studies conclude that Web popularity birth events. A longer trace may reveal objects spread outdistributions follow Zipf’s law, some studies have observed over time that have equivalent short-term popularity, similarnon-Zipf behavior, specifically in workloads that emerge as to the “group” effect Tang et al. proposed.miss traffic from a proxy cache [4, 7, 12]. Web proxy caches, In another study, Almeida et al. [3] observe a flattenedby design, absorb requests to popular documents; conse- Zipf-like popularity distribution in an educational mediaquently, the most popular documents in a workload will ap- server. They propose that this distribution is well describedpear only once (as cold misses) in cache miss traces. Mod- by the concatenation of two true Zipf distributions. Al-erately popular documents will occasionally be absorbed by though the data appears to be well modeled by two Zipfthe proxy cache, but they will also appear in the miss traffic distributions, their study does not provide an explanationas capacity misses. The least popular documents will always of what causes this effect.miss in the proxy cache, assuming the cache has finite size Another often studied multimedia workload is that of vid-and cannot hold the entire object population. As a result, a eo-on-demand (VoD) servers. VoD researchers frequentlyZipf workload, when fed through a shared proxy cache, will use Zipf distributions to model the popularity of video doc-result in a non-Zipf popularity distribution with a flattened uments in their systems [10, 18, 31]. Surprisingly, many ofhead [12], visually similar to the non-Zipf distributions we these researchers appear to base their Zipf assumption onobserved in Kazaa. Breslau et al. [7] derive equations that results published from a single data set: one week of rentalrelate the Zipf parameter of a workload fed to a proxy cache data from a video store [35].and the hit rate that the proxy cache will experience. Our results suggest that their workload (i.e., requests for Although proxy cache miss distributions may visually re- immutable video objects) should reveal similar non-Zipf be-semble our measured Kazaa popularity distributions, we be- havior stemming from fetch-at-most-once client behavior.lieve the two workloads diverge from Zipf for different rea- To understand the apparent discrepancy between their as-sons. In Kazaa, users’ requests do not flow through a shared sumptions and our results, we manually extracted the videoproxy cache: the workload we observed is the aggregate of a rental data set from Figure 2 of [10]. In Figure 7(a), we showlarge number of individual fetch-at-most-once streams, with this data as it appeared in that paper, plotted on a linearan absence of shared proxy caches. The only caches that scale with a Zipf curve fit. Figure 7(b) shows the samecurrently exist in Kazaa are client-side caches. Note that data plotted on a log-log scale with the same Zipf curvethere is no practical difference between true fetch-at-most- fit. While the linear scale plot seems to suggest the dataonce user behavior, and fetch-repeatedly user behavior fil- may be well described by Zipf’s law, the log-log plot revealstered through infinite client-side caches resulting in fetch- that even this data set appears to show the flattened headat-most-once client behavior. that is characteristic of fetch-at-most-once systems. We also The most popular Web object will show up only one time gathered recent box-office movie ticket sales data [34]. Fig-in a proxy cache miss trace. The most popular object will ures 7(c) and (d) show that this data, too, is consistent with
  9. 9. 100 1000 90 80 rental frequency rental frequency 70 100 60 50 40 30 10 20 10 0 1 0 50 100 150 200 250 1 10 100 1000 movie index movie index (a) (b) 500 1000000 450 100000 400 10000 box office sales box office sales 350 ($millions) ($millions) 1000 300 100 250 200 10 150 1 100 0.1 50 0.01 0 0.001 0 50 100 150 200 250 1 10 100 1000 movie index movie index (c) (d)Figure 7: Video rental and box office sales popularity. (a) The popularity distribution from a 1992 video rental data setused to justify Zipf ’s law in many video-on-demand papers, along with a Zipf curve fit with α = 0.9, and (b) the samedata set and curve fit plotted on a log-log scale. Contrary to the assumption of many papers, video rental data doesnot appear to follow Zipf ’s law. (c) The distribution of 2002 U.S. box office ticket sales on a linear scale, along with aZipf-fit with α = 2.28, and (d) on a log-log scale. This data set also appears to be non-Zipf.our fetch-at-most-once popularity distribution. Symbol Meaning Base value The VoD community has recently begun to recognize the C # of clients 1,000importance of object births in video-on-demand systems [16]. O # of objects 40,000Because new video titles are created (often with high pop- λR per-user request rate 2 objects/dayularity), and because the popularity of existing video titles Zipf parameter driving α 1.0tends to diminish over time, VoD workload models are start- object popularitying to incorporate schemes for changing popularity distribu- probability that a user based on Zipf(1)tions over time. This is consistent with our earlier observa- P(x) requests an object of (see text)tions that both client and object birth rates are important popularity rank xfactors in multimedia workloads. probability that a newly In the next section, we present a model of fetch-at-most- A(x) arrived object is inserted Zipf(1) at popularity rank xonce systems. We use this model to demonstrate the impli- cache size, measured ascations of the resulting non-Zipf behavior and explore the M fraction of all objects variesimportance of client and object birth rates on system per- λO object arrival rate variesformance. We believe that this model is relevant to both λC client arrival rate variesP2P file-sharing and video-on-demand systems. Table 4: Model structure and notation. These parame-4. A MODEL OF P2P FILE-SHARING ter settings reflect the values seen in our trace for large (>100MB) objects. WORKLOADS In the previous section, we hypothesized that the non-Zipfbehavior of Kazaa, and of multimedia workloads in gen-eral, is best explained by the fetch-at-most-once behavior fetch-at-most-once behavior has on overall object popularity.of clients. This section presents a model of P2P file-sharing We use caching as a lens to observe how the addition ofsystems that enables us to explore this hypothesis further. new objects and new clients affects performance in a fetch-The model, used to generate Figure 6 in the previous section, at-most-once file-sharing system. Finally, we validate thecaptures key parameters of the P2P file-sharing workload, model by comparing the popularity distributions of a model-such as request rates, number of clients, number of objects, generated workload with that extracted from our trace data.and changes to the set of clients and objects. From theseparameters, it produces a stream of client requests for ob- 4.1 Model Descriptionjects that we then analyze. By varying model parameters, Table 4 summarizes the parameters used in our model. Wewe explore the underlying processes that lead to certain ob- chose parameter values that reflect our trace measurementsservable workload characteristics and how changes to the for large objects, since this component dominates bandwidthparameters affect the system and its performance. consumption. One parameter value that differs from the The remainder of this section describes our basic model of measured trace is the number of clients. In order to run aa P2P file-sharing system. This model shows the impact that substantially larger number of experiments, we model a sys-
  10. 10. 1tem with 1,000 clients rather than the roughly 7,000 large- fetch-repeatedlyobject clients in our trace. We verified that the predictions 4000 1000 0.8 2000of our model were not affected by this difference. To simplifyour model, we also assumed that all objects in the system hit rate 0.6were of equal size. Our model captures key aspects of our P2P file-sharing 0.4workload, in particular, the differences between file-sharing 0.2and Web workloads. In a Web workload, clients select ob- 1000 2000 4000jects from a Zipf distribution, P (x), in an independent and fetch-at-most-once 0identically distributed fashion. In contrast, the object selec- 0 200 400 600 800 1000tion process in a file-sharing system depends on three fac- daystors: (1) the Zipf distribution, P (x), (2) the way in whichnew objects are inserted into that distribution, A(x), and Figure 8: File-sharing effectiveness diminishes with client(3) the clients’ fetch-at-most-once behavior. age. After an initial cache warm-up period, the request Our model generates requests as follows. On average, a stream generated by fetch-at-most-once client behaviorclient requests two objects per day, choosing which object to experiences rapidly decreasing cache performance, whilefetch from a Zipf probability distribution with parameter 1.0 the stream generated by fetch-repeatedly clients remains(“Zipf(1)”).4 We hypothesize that the underlying popularity stable. This effect is shown for various cache sizes (1000,of objects in a fetch-at-most-once file-sharing system is still 2000, and 4000 objects).driven by Zipf’s law, even though the observed workloadbecomes non-Zipf because of fetch-at-most-once clients. Inour model, subsequent requests from the same client obey the optimistic assumption that all objects are cachable anddistributions obtained by removing already fetched objects are never updated.from the candidate object set and re-scaling so the totalprobability is 1.0. Given two previously unrequested objects, 4.2 File-Sharing Effectiveness Diminishes withthe ratio of the probabilities that the client will request these Client Ageobjects next is identical to their ratio in the original Zipf Imagine an organization experiencing current demand fordistribution. For fetch-repeatedly systems, each request is external bandwidth due to fetches of P2P file-sharing ob-made according to the original Zipf distribution. jects. How should this organization expect bandwidth de- When modeling fetch-at-most-once systems, λO > 0 is the mand to change over time, given a shared proxy cache? Weobject arrival rate. When an object is born in a fetch-at- address this question using the “static” case in which no newmost-once system, its popularity rank is determined by se- clients or objects arrive. This static analysis allows us to fo-lecting randomly from the Zipf(1) distribution. Pre-existing cus on one factor at a time (fetch-at-most-once behavior, inobjects of equal or lesser popularity are “pushed down” one this case). We relax these assumptions in later subsectionsZipf position, and the resulting distribution is re-normalized to show the impact of other factors, such as new object andso the total probability is again 1.0. In fetch-repeatedly sys- client arrivals.tems, we set the object arrival rate to 0. Objects may be Figure 8 shows hit rate against time for various sharedupdated, but for simplicity we ignore the second-order effect cache sizes, assuming that at time zero no clients have fetchedof completely new objects on request behavior. any objects. After a brief cache warm-up period, hit rate de- While our trace shows all requested objects, it cannot ob- creases as clients age, even for a cache that can hold 10% ofserve the total object population, since many available ob- all file-sharing objects. This is because fetch-at-most-oncejects were never accessed. However, total object population clients consume the most popular objects early. Later re-is a key parameter of our model, as it influences the amount quests are selected with nearly uniform likelihood from anof overlap that will likely occur in requests from different increasingly large number of objects. That is, the systemclients. We therefore estimated a base value for total object evolves towards one in which there is no locality, and ob-population by back-inference: how many large objects are jects are chosen at random from a very large space.most likely to have existed in total, given that we saw about This behavior suggests that if client request rates remain18,000 distinct large objects requested in the trace? We find constant over time, the external bandwidth load they presentthat a total population of about 40,000 large-media objects increases, since more of their requests are directed to objectsis consistent with the trace data; therefore, we use this num- that are only available externally. Conversely, if we hope tober as the base value. This number is also comparable to have stable bandwidth demand over time, clients must be-statistics that describe commercial movie releases: the In- have in a way that reduces the intensity of their requests internet Movie Database reports between 50,000 and 60,000 a manner consistent with the shape of the hit-rate decreasesmovie releases world-wide over the past 20 years [34]. shown in Figure 8. To quantify file-sharing effectiveness, we use the hit rate The decrease in hit-rate over time is a strong property ofthat the aggregate workload experiences against a 100% fetch-at-most-once behavior. The underlying popularity dis-available shared cache with LRU replacement, whose size tribution need not be heavy tailed for it to occur. We havewe vary in each experiment. Selected experiments using performed experiments using initial object popularity distri-optimal replacement showed no qualitative differences from butions that have higher locality (i.e., Zipf parameters largerLRU results, and quantitative differences varied by only a than 1.0). In a fetch-repeatedly context, the larger skew offew percent. For Web (fetch-repeatedly) scenarios, we make these distributions towards the most popular objects makes4 Attempting to best-fit a Zipf curve to our measured non- file sharing easier and hit rates rise. For fetch-at-most-onceZipf distribution resulted in a Zipf parameter of 0.98. systems, hit rates start out much higher, but as clients age