USITS '01 Paper
[USITS '01 Tech Program Index]
|Pp. 112 of the Proceedings|
Measurement and Analysis of a Streaming-Media Workload
Maureen Chesire, Alec Wolman, Geoffrey M. Voelker,
and Henry M. Levy
Department of Computer Science and Engineering
University of Washington University of California, San Diego
The increasing availability of continuous-media data is provoking a
significant change in Internet workloads. For example, video from news,
sports, and entertainment sites, and audio from Internet broadcast radio,
telephony, and peer-to-peer networks, are becoming commonplace. Compared
with traditional Web workloads, multimedia objects can require significantly
more storage and transmission bandwidth. As a result, performance
optimizations such as streaming-media proxy caches and multicast delivery are
attractive for minimizing the impact of streaming-media workloads on the
Internet. However, because few studies of streaming-media workloads exist,
the extent to which such mechanisms will improve performance is unclear.
This paper (1) presents and analyzes a client-based streaming-media
workload generated by a large organization, (2) compares media
workload characteristics to traditional Web-object workloads, and (3)
explores the effectiveness of performance optimizations on
streaming-media workloads. To perform the study, we collected traces
of streaming-media sessions initiated by clients from a large
university to servers in the Internet. In the week-long trace used
for this paper, we monitored and analyzed RTSP sessions from 4,786
clients accessing 23,738 distinct streaming-media objects from 866
servers. Our analysis of this trace provides a detailed
characterization of streaming-media for this workload.
Today's Internet is increasingly used for transfer of continuous-media
data, such as video from news, sports, and entertainment Web sites,
and audio from Internet broadcast radio and telephony. As evidence, a
large 1997 Web study from U.C. Berkeley  found no
appreciable use of streaming media, but a study three years later at
the University of Washington found that RealAudio and RealVideo had become
a considerable component of Web-related traffic . In
addition, new peer-to-peer networks such as Napster have dramatically
increased the use of digital audio over the Internet. A 2000 study
of the IP traffic workload seen at the NASA Ames Internet Exchange
found that traffic due to Napster rose from 2% to 4% over the course
of 10 months , and a March 2000 study of Internet
traffic at the University of Wisconsin-Madison found that 23% of its
traffic was due to Napster .
Streaming-media content presents a number of new challenges to systems
designers. First, compared to traditional Internet applications such
as email and Web browsing, multimedia streams can require high data
rates and consume significant bandwidth. Second, streaming data is
often transmitted over UDP , placing responsibility for
congestion control on the application or application-layer protocol.
Third, the traffic generated tends to be bursty  and is
highly sensitive to delay. Fourth, streaming-media objects require
significantly more storage than traditional Web objects, potentially
increasing the storage requirements of media servers and proxy caches,
and motivating more complex cache replacement policies. Fifth,
because media streams have long durations compared to the
request/response nature of traditional Web traffic, multiple
simultaneous requests to shared media objects introduce the
opportunity for using multicast delivery techniques to reduce the
network utilization for transmitting popular media objects.
Unfortunately, despite these new characteristics and the challenges of
a rapidly growing traffic component, few detailed studies of
multimedia workloads exit.
This paper presents and analyzes a client-based streaming-media
workload. To capture this workload, we extended an existing HTTP
passive network monitor  to trace key events from
multimedia sessions initiated inside the University of Washington to
servers in the Internet. For this analysis, we use a week-long trace
of RTSP sessions from 4,786 university clients to 23,738 distinct
streaming-media objects from 866 servers in the Internet, which
together consumed 56 GB of bandwidth.
The primary goal of our analysis is to characterize this
streaming-media workload and compare it to well-studied HTTP Web
workloads in terms of bandwidth utilization, server and object
popularity, and sharing patterns. In particular, we wish to examine
unique aspects of streaming-media workloads, such as session duration,
session bit-rate, and the temporal locality and degree of overlap of
multiple requests to the same media object. Finally, we wish to
consider the effectiveness of performance optimizations, such as proxy
caching and multicast delivery, on streaming-media workloads.
Our analysis shows that, for our trace, most streaming-media objects
accessed by clients are encoded at low bit-rates (<56 Kb/s), are
modest in size (<1 MB), and tend to be short in duration (<10
mins). However, a small percentage of the requests (3%) are
responsible for almost half of the total bytes downloaded. We find
that the distributions of client requests to multimedia servers and
objects are somewhat less skewed towards the popular servers and
objects than with traditional Web requests. We also find that the
degree of multimedia object sharing is smaller than for traditional
Web objects for the same client population. Shared multimedia objects
do exhibit a high degree of temporal locality, with 20-40% of active
sessions during peak loads sharing streams concurrently; this suggests
that multicast delivery can potentially exploit the multimedia object
sharing that exists.
The rest of the paper is organized as follows. In
Section 2 we provide an overview of previous related
work. Section 3 provides a high-level description of
streaming-media protocols for background. Section 4
describes the trace collection methodology. Section 5
presents the basic workload characteristics, while Section 6
focuses on our cache simulation results. Section 7
presents our stream merging results. Finally, Section 8
While Web client workloads have been studied
relatively little research has been done on multimedia traffic analysis.
Acharya et al.  analyzed video files stored on Web servers
to characterize non-streaming multimedia content on the Internet. Their
study showed that these files had a median size of 1.1 MB and most of them
contained short videos (< 45 seconds). However, since their study was
based upon a static analysis of stored content, it is unclear how that
content is actually accessed by Web clients.
Mena et al.  analyzed streaming audio traffic using traces
collected from a set of servers at Broadcast.com, a major Internet audio
site. The focus of their study was on network-level workload
characteristics, such as packet size distributions and other packet flow
characteristics. From their analyses, they derive heuristics for
identifying and simulating audio flows from Internet servers. Their study
showed that most of the streaming-media traffic (60-80%) was transmitted
over UDP, and most clients received audio streams at low bit-rates (16-20
Kb/s). The most striking difference between results obtained from their
trace and our analysis is that most of their streaming sessions were
long-lived; 75% of the sessions analyzed lasted longer than one hour. We
attribute this difference to the fact that they studied server-based audio
traces from a single site, while we study a client-based trace of both audio
and video streams to a large number of Internet servers.
Van der Merwe et al.  extended the functionality of tcpdump (a
popular packet monitoring utility) to include support for monitoring
multimedia traffic. Although the primary focus of their work was building
the multimedia monitoring utility, they also reported preliminary results
from traces collected from WorldNet, AT&T's commercial IP network. As
in , their study of over 3,000 RTSP flows also focused on
network-level characteristics, such as packet length distributions and
packet arrival times. In addition, they characterized the popularity of
object accesses in the trace and similarly found that they matched a
Zipf-like distribution. In contrast to our university client trace, the
WorldNet workload peaks later in the evening and has relatively larger weekend
workloads. We attribute this difference to the time-of-day usage patterns
of the two different client populations; WorldNet users are not active until
after work, while the university users are active during their work day.
There has been significant commercial activity recently (by companies
such as FastForward Networks, Inktomi, and Akamai) on building caching
and multicast infrastructure for the delivery of both on-demand and live
multimedia content. However, little has been published about these
Our paper builds upon this previous work in a number of significant
ways. First, we study a trace with an order of magnitude more
sessions. Second, we focus on application-level characteristics of
streaming-media workloads - such as session duration and sizes,
server and object popularity, sharing patterns, and temporal locality
- and compare and contrast those characteristics with those of
non-streaming Web workloads. Lastly, we explore the potential
benefits of performance optimizations such as proxy caching and multicast
delivery on streaming-media workloads.
Streaming Media Background
This section defines a number of terms and concepts that are used throughout
the paper. We use the term streaming media to refer to the transfer of live
or stored multimedia data that the media player can render as soon as it is
received (rather than waiting for the full download to complete before
rendering begins). Although streaming techniques are typically used to
transfer audio and video streams, they are sometimes used to deliver
traditional media (such as streaming text or still images). A wide variety
of streaming-media applications are in use today on the Internet. They
employ a wide variety of protocols and algorithms that can generally be
classified into five categories:
- Stream control protocols enable users to interactively
control the media stream, e.g., pausing, rewinding, forwarding or stopping
stream playback. Examples include RTSP, PNA,
MMS and XDMA. These protocols typically rely on
TCP as the underlying transport protocol.
- Media packet protocols support real-time data delivery and
facilitate the synchronization of multiple streams. These protocols define
how a media server encapsulates a media stream into data packets, and how a
media player decodes the received data. Most media packet protocols rely on
UDP to transport the packets. Examples include RDT,
RTP, PNA, MMSU and MMST.
- Encoding formats dictate how a digitized media stream is
represented in a compact form suitable for streaming. Examples of encoding
schemes commonly used include WMA, MP3,
MPEG-2, RealAudio G2 and RealVideo G2
- Storage formats define how encoded media streams are
stored in ``container'' files, which hold one or more streams. Headers in
the container files can be used to specify the properties of a stream such
as the encoding bit-rate, the object duration, the media content type, and
the object name. ASF and RMFF are examples of
container file formats.
- Metafile formats provide primitives that can be used to
identify the components (URLs) in a media presentation and define their
temporal and spatial attributes. SDP, SMIL and
ASX are examples of metafile formats.
We collected a continuous trace of RTSP traffic flowing across the border
routers serving the University of Washington campus over a one week period
between April 18th and April 25th, 2000. In addition to monitoring RTSP
streams, the trace tool also maintained connection counts for other popular
stream control protocols: PNA used by Real Networks' servers,
MMS used by Microsoft Windows Media servers, and
XDMA, used by Xing's StreamWorks servers. Our trace data was
collected using a DEC Alpha workstation connected to four Ethernet switches
at the University network border. The software used to monitor and log
streaming-media packets is described in Section 4.2.
Capturing streaming-media traffic is challenging because applications may
use a variety of protocols. Moreover, while efforts have been made to
develop common standardized protocols, many commercial applications continue
to use proprietary protocols. Given the diversity of protocols, we decided
to focus initially on the standardized and well documented RTSP
protocol . Widely used media players that support RTSP
include Real Networks' RealPlayer and Apple's QuickTime Player.
A high level description of the RTSP protocol is presented below.
The RTSP protocol is used by media players and streaming servers
to control the transmission of media streams. RTSP is a request-response
protocol that uses a MIME header format, similar to HTTP. Unlike HTTP,
the RTSP protocol is stateful and requests may be initiated by either
clients or servers. In practice, the RTSP control traffic is always
sent over TCP.
The media data is often sent over UDP, but it may also be interleaved
with the control traffic on the same TCP connection. RTSP uses
sequence numbers to match requests and responses. Media
objects are identified by an RTSP URL, with the prefix ``rtsp:''.
Streaming media communication.
Figure 1 illustrates a common streaming media usage
scenario. First, a user downloads a Web page that contains a link to
a media presentation. This link points to a metafile hosted by the
media server. The Web browser then downloads the metafile that contains
RTSP URLs for all the multimedia objects in the presentation (e.g., a music
clip and streaming text associated with the audio). Next, the browser
launches the appropriate media player and passes the metafile contents to
the player. The media player in turn parses the metafile and initiates an
RTSP connection to the media server.
Many of our analysis results will refer to an RTSP ``session.'' An
RTSP session is similar to an HTTP ``GET'' request, in that typically
there will be one session for each access to the object. A session
begins when the media player first accesses the object, and it ends
when the media player sends a TEARDOWN message, though there may be a
number of intervening PAUSE and PLAY events. There is not a
one-to-one mapping between sessions and RTSP control connections;
instead, the protocol relies on session identifiers to distinguish
among different streams. In order to make our results easier to
understand, when a single RTSP session accesses multiple objects,
we consider it to be multiple sessions - one for each object.
Trace Collection and Analysis Software
We extended our existing HTTP passive network monitor  to
support monitoring the RTSP streaming-media protocol.
Our trace collection application has three main components: a packet
capture and analysis module that extracts packet payloads and reconstructs
TCP flows and media sessions; a protocol parsing module that parses RTSP
headers; and a logging module that writes the parsed data
to disk. After the traces are produced, we analyze the collected trace
data in an off-line process. We now provide a brief overview of the
operation of the trace collection software.
The packet capture module uses the Berkeley packet filter 
to extract packets from the kernel and then performs TCP reassembly for each
connection. Simple predicates are used on the first data bytes of each
connection to classify TCP connections as RTSP control connections. This
module also maintains a session state table that is used to map control
messages to client streaming sessions; each table entry models the state
machine for a client's multimedia session. In addition to maintaining the
session table, this module provides support for: handling responses that
arrive earlier than the corresponding requests due to asymmetric routing;
merging control messages that cross packet boundaries; and extracting
messages from connections transmitting control data interleaved with media
Data in the control connections is used to determine which UDP datagrams
to look at. We record timing and size information about the UDP
data transfers, but we do not attempt to process the contents of media
stream packets because almost all commonly used encoding formats and
packet protocols are proprietary.
The protocol parsing module extracts pertinent information from RTSP headers
such as media stream object names, transport parameters and stream play
ranges. All sensitive information extracted by the parser, such as IP
addresses and URLs, is anonymized to protect user privacy. Finally, the
logging module converts the data to a compact binary representation and then
saves it to stable storage.
This section analyzes the basic characteristics of our streaming-media
workload; when appropriate we compare these characteristics to those
of standard Web object workloads. The analysis ignores
non-continuous media data (e.g., streaming text and still images).
Since we were interested in the access patterns of the UW client
population, we ignored sessions initiated by clients external to
UW that accessed servers inside the campus network.
|| 58808 (RTSP); 44878 (MMS);
|| 35230 (PNA); 3930 (XDMA)
|| 866 (External)
RTSP Total Bytes
|| 56 GB (Continuous media)
|| 4786 (UW clients)
|| 40070 (Continuous media)
|| 23738 (Continuous media);
|| 3760 (Other)
Table 1 summarizes the high-level characteristics of the
trace. During this one-week period, 4,786 UW clients accessed 23,738
distinct RTSP objects from 866 servers, transferring 56 GB of streaming
media data. Using the connection counts from
Table 1, we estimate that RTSP accounts for approximately
40% of all streaming media usage by UW clients.
The detailed analyses in the following sections examine various
attributes of the traffic workload, such as popularity distributions,
object size distributions, sharing patterns, bandwidth utilization,
and temporal locality. Overall, our analysis shows that:
Bandwidth utilization over time (in Kbits/sec).
- Most of the streaming data accessed by clients is transmitted at
low bit-rates: 81% of the accesses are transmitted at a bit-rate less
that 56 Kb/s.
- Most of the media streams accessed have a short duration
(< 10 minutes) and a modest size (< 1 MB).
- A small percentage of the sessions (3%) are responsible for almost
half of the bytes downloaded.
- The distribution of client requests to objects is Zipf-like, with an
parameter of 0.47.
- While clients do share streaming-media objects, the degree of object
sharing is not as high as that observed in Web traffic
- There is a high degree of temporal locality in client requests to
repeatedly accessed objects.
Figure 2 shows a time-series plot of the aggregate
bandwidth consumed by clients streaming animations, audio, and video data.
We see that the offered load on the network follows a diurnal cycle, with
peaks generally between 11 AM and 4 PM. The volume of traffic is
significantly lower during weekends; peak bandwidth over a five-minute
period was 2.8 Mb/s during weekdays, compared to 1.3 Mb/s on weekends. We
found that, on average, clients received streaming content at the rate of
66 Kb/s. This bit-rate is much lower than the capacity of the high-bandwidth
links of the clients and the UW ISP links. We conclude from the prevalence
of these low-bit-rate sessions that the sites that clients are accessing
encode streaming content at modem bit-rates, the lowest common denominator.
Advertised stream length. (a) Normal and (b) CDF.
In this section, we provide a detailed analysis of the advertised
duration of continuous media streams referenced during the trace.
Note that the advertised duration of a stream is different from the
length of time that the client actually downloads the stream (e.g., if
the user hits the stop button before the stream is finished).
Since media servers generally do not advertise the duration of live
streams, we limit our analysis to on-demand (stored) media streams.
Sessions accessing these on-demand streams account for 85% of
Figure 3a is a histogram of all streams lasting less than
seven minutes, and Figure 3b plots the cumulative
distribution of all stream lengths advertised by media servers. The peaks
in the histogram in Figure 3a indicate that many streams
are extremely short (less than a minute), but the most common stream lengths
are between 2.5 and 4.5 minutes. These results suggest that clients have a
stronger preference for viewing short multimedia streams. One important
observation from Figure 3b is that the stream-length
distribution has a long tail. Although the vast majority of the streams
(93%) have a duration of less than 10 minutes, the remaining 7% of the
objects have advertised lengths that range between 10 minutes and 6 weeks.
Session duration vs. session size.
Shared and unshared session characteristics. (a) Time and (b) Size.
Modem and LAN session characteristics. (a) Time and (b) Size.
In this section, we examine two closely related properties of sessions:
the amount of time that a client spends accessing a media stream, and
the number of bytes downloaded during a stream access.
In Figure 4 we present the relationship
between the duration of a streaming-media session and the number of bytes
transferred. In Figure 5 we look at the
distinguishing characteristics between sessions accessing shared objects and
sessions accessing unshared objects. Finally, in Figure 6 we
compare the size and duration characteristics of sessions from clients in the
campus modem pool to sessions from clients connected by high-speed
A number of important trends emerge from these graphs. First, we see that
client sessions tend to be short. From Figure 5a
we see that 85% of all sessions (the solid black line) lasted less than 5
minutes, and the median session duration was 2.2 minutes. The bandwidth
consumed by most sessions was also relatively modest. From
Figure 5b we see that 84% of the sessions
transferred less than 1 MB of data and only 5% accessed more than 5 MB. In
terms of bytes downloaded, the median session size was 0.5 MB. Both the
session duration and the session size distributions have long tails: 20
sessions accessed more than 100 MB of data each, while 57 sessions remained
active for at least 6 hours, and one session was active for 4 days.
Although the long-lived sessions (> 1 hour) account for only 3% of all
client sessions, these sessions account for about half of the bandwidth
consumed by the workload. From Figure 4 we
see that these long sessions account for 47% of all bytes downloaded.
Most of the media objects accessed are downloaded at relatively low bit-rates
despite the fact that most of the clients are connected by high-speed links.
Using the raw data from Figure 4, we
calculated that 81% of the streams are downloaded at bit-rates less than
56 Kb/s (the peak bandwidth supported by most modems today). In
Figure 6, we separate all the trace sessions into
those made from clients in the modem pool and those made from LAN clients.
Although it does appear that the duration of modem sessions is shorter than
the duration of LAN sessions (Figure 6a), the
difference is not large. On the other hand, the difference in bytes
downloaded between modem sessions and LAN sessions
(Figure 6b) appears to be much more pronounced. For
modem users, the median session size is just 97 KB, whereas for LAN users it
is more than 500 KB.
Figures 5a and 5b also
distinguish between accesses to shared objects (the dashed lines) and
accesses to unshared objects (the grey lines). A shared object is one that
is accessed by more than one client in the trace; an unshared object is
accessed by only one client, although it may be accessed multiple times.
Overall, sessions that request shared objects tend to be shorter than
sessions accessing unshared objects. For example, 46% of shared sessions
lasted less than one minute, compared with only 30% of the unshared
sessions. Furthermore, we found that most of the sessions accessing shared
objects transferred less data than unshared sessions. For example, 44% of
shared sessions transferred less than 200 KB of data compared to only 24% of
unshared sessions. However, Figure 5 shows that
the situation changes for sessions on the tails of both curves, where the
sessions accessing shared objects are somewhat longer and larger than
sessions accessing unshared objects.
Server popularity by object and session.
In this section we examine the popularity of media servers and
objects. Figure 7 plots (a) the cumulative
distribution of continuous media objects across the 866 distinct
servers referenced, as well as (b) the cumulative distribution of
requests to these servers. These graphs show that client load is
heavily skewed towards the popular servers. For example, 80% of the
streaming-media sessions were served by the top 58 (7%) media
servers, and 80% of the streaming-media objects originated from the
33 (4%) most popular servers. This skew to popular servers is
slightly less pronounced than for requests to non-streaming Web
objects. From a May 1999 trace of the same client population, 80% of
the requests to non-streaming Web objects were served by the top 3% of Web
Object popularity by number of sessions (note log scale).
One of the goals of our analysis was to understand how client requests were
distributed over the set of multimedia streams accessed during the trace
period. To determine this, we ranked multimedia objects by popularity
(based on the number of accesses to each stream) and plotted the results on
the log-scale graph shown in Figure 8. Our analysis found
that of the 23,738 media objects referenced, 78% were accessed only once.
Only 1% of the objects were accessed by ten or more sessions, and the 12
most popular objects were accessed more than 100 times each. From
Figure 8, one can see that the popularity distribution fits a
straight line fairly well, which implies that the distribution is
Zipf-like . Using the least squares method, we calculated
parameter to be 0.47. In contrast, the
reported in  for HTTP proxies ranged from 0.64 to 0.83. The
implication is that accesses to streaming-media objects are somewhat less
concentrated on the popular objects in comparison with previously reported
Web object popularity distributions.
Number of clients accessing popular objects.
In this section we explore the sharing patterns of streaming-media
objects among clients. We first examine the most popular objects to
determine whether the repeated accesses come from a single client, or
whether those popular objects are widely shared. In
Figure 9, we compute the number of unique clients
that access each of the 200 most popular streaming-media objects.
This figure shows that the most popular streams are widely shared,
and that as the popularity declines, so does the number of unique clients
that access the stream.
Figure 10 presents per-object sharing statistics. Of
the streaming-media objects requested, only 1.6% were accessed by five or
more clients, while 84% were viewed by only one client. Only 16% of the
objects were shared (i.e., accessed by two or more clients), yet requests
for these shared objects account for 40% of all sessions recorded. From
this data, we conclude that the shared objects are also more frequently
accessed and can therefore benefit from caching. Note, however, that the
degree of object sharing is low compared to the sharing rate for web
documents [5,30]. Consequently, multimedia caching may not
be as effective as Web caching in improving performance.
Concurrent sharing over time.
Figure 11 shows the overlap among accesses to
the shared media objects by plotting the number of sessions that
access unshared objects (black) and the number of sessions that access
shared objects (grey) over time for the entire trace. During peak
load periods between 11 AM and 4 PM (weekdays), we see that 20%-40%
of the active sessions share streams concurrently. This temporal
locality suggests that (1) caching will work best when it is needed
the most (during peak loads), and that (2) multicast delivery has the
opportunity to exploit temporal locality and considerably reduce
Caching is an important performance optimization for standard Web objects.
It has been used effectively to reduce average download latency, network
utilization, and server load. Given the large sizes of streaming-media
objects and the significant network bandwidth that they can consume, caching
will also be important for improving the performance of streaming-media
objects. In this section, we study the potential benefits of proxy caching
for streaming-media objects. In particular, we determine cache hit rates
and bandwidth savings for our workload, explore the tradeoff of cache
storage and hit rate, and examine the sensitivity of hit rate to eviction
Cache size growth over time.
Bandwidth saved over time due to caching.
Cache accesses: Hits, partial hits, and misses.
We use a simulator to model a streaming media caching system for our
analyses. The simulator caches the entire portion of any on-demand stream
retrieved by a client, making the simplifying assumption that it is allowed
to cache all stored media objects. For live streams, the simulator assumes
that the cache can effectively merge multiple client accesses to the same
live stream by caching a fixed-size sliding interval of bytes from that
stream . The simulator assumes unlimited cache capacity, and
it uses a timeout-based cache eviction policy to expire cached objects. For
Figures 12 through 14, an object is removed
from the cache two hours after the end of the most recent access.
The results of the simulation are presented in the set of graphs below.
Figure 12 is a time-series plot showing cache size
growth over time, while Figure 13 shows potential bandwidth
savings due to caching. The total height of each bar in the stacked bar
graph in Figure 14 reflects the total number of client
accesses started within a one-hour time window. The lightest area of the
graph shows the number of accesses that requested fully-cached objects;
the medium-grey section represents the number of accesses that resulted
in partial cache hits. Partial cache hits are recorded when a later
request retrieves a larger portion of the media stream than was previously
accessed. The height of the darkest part of the graph represents the number
of accesses that resulted in cache misses.
Since streaming objects are comparatively large in size, the
replacement policy for streaming proxy caches may be an important design
decision. Many proposed designs for streaming proxy caches assume
that multimedia streams are too large to be cached in their
entirety [24,27]. As a result, specialized caches are
designed to cache only selected portions of a media stream; uncached
portions of the stream have to be retrieved from the server (or
neighboring caches) to satisfy client requests.
To determine the need for these complex caching strategies, we
explored the sensitivity of hit rate to cache replacement eviction
policies by varying the timeout for cached objects. Using the default
two hour expiration of our simulator, we found that the simulated
cache achieved an aggregate request hit rate of 24% (including
partial cache hits) and a byte hit rate of 24% using less than 940 MB
of cache storage. Because the required cache size is relatively
small (when compared to the total 56 GB of data transferred), it appears
that conventional caching techniques and short expiration times might be just
as effective as specialized streaming media caching schemes for client
populations similar to this.
Effect of eviction time on cache hit rates.
Figure 15 plots request and byte hit rates as object
eviction time is increased from 5 minutes to 7 days (the entire trace
duration). Notice that reducing the caching window to 5 minutes still
yields reasonably high request hit rates (20%). By keeping objects in
the cache for only two hours after the last access, we achieve 90% of
the maximum possible byte hit rate for this workload while saving
significant storage overhead. From this data, we can infer that
requests to streaming-media objects that are accessed at least twice
have a high degree of temporal locality.
Stream merging is a recently developed technique that uses multicast to
reduce the bandwidth requirements for delivering on-demand streaming
media. The details of stream merging are covered extensively in
[6,7]. In this section we provide a high level overview of
stream merging to motivate our measurements.
Stream merging occurs when a new client requests a stream that is already in
transmission. In this case, the server begins sending the client two
streams simultaneously: (1) a ``patch stream'' starting at the beginning of
the client's request, and (2) a multicast stream of the existing
transmission in progress. The new client buffers the multicast stream while
displaying the patch stream. When the patch stream is exhausted, the client
displays the multicast stream from its buffer while it continues to receive
and buffer the simultaneous multicast stream ahead of the display. At the
merge point, only one stream, via multicast, is being transmitted to both
clients. The cost of stream merging is that clients must be able to receive
data faster than the required stream playback rate and must buffer the
Effectiveness of stream merging.
To evaluate the effectiveness of stream merging for our workload, we consider
consecutive overlapping accesses to each stream object in our trace and calculate the time
it takes to reach the merge point based on . Given the time
of the merge point, we then calculate what percentage of the overlap
period occurs after the merge point. This corresponds to the percentage
of time that only one stream is being transmitted via multicast to both
clients. The results
of this analysis are shown as a
cumulative distribution in Figure 16. Because this stream
merging technique is only needed for on-demand streams, live streams are
not included in Figure 16. This figure shows that
stream merging is quite effective for this workload - for more than 50%
of the overlapping stream accesses, shared multicast can be used
for at least 80% of the overlap period. This
result indicates strong temporal locality in our trace, which is
consistent with our concurrent sharing and cache simulation results.
We have collected and analyzed a one-week trace of all RTSP client
activity originating from a large university. In our analyses, we
characterized our streaming multimedia workload and compared it to
well-studied HTTP Web workloads in terms of bandwidth utilization,
server and object popularity, and sharing patterns. In particular,
we examined aspects unique to streaming-media workloads, such as
session duration, session bit-rate, temporal locality, and the
degree of overlap of multiple requests to the same media object. We
also explored the effectiveness of performance optimizations, such as
proxy caching and multicast delivery, on streaming-media workloads.
We have observed a number of properties (e.g., degree of object
sharing and lower Zipf
parameter for the object-popularity
distribution) indicating that multimedia workloads may benefit less
from proxy caching, on average, than traditional Web workloads. On
the other hand, we have found that multimedia workloads exhibit
stronger temporal locality than we expected, especially during peak
hours. This suggests that multicast and stream-merging techniques may
prove to be useful for these workloads.
Our results are fundamentally based upon the workload that we captured
and observed. It is clear that usage of streaming media in our
environment is still relatively small, and our results could
change as the use of streaming media becomes more prevalent. Our
one-week trace contains only 40,000 sessions from fewer than 5,000
clients. A one-week trace of Web activity for the same population
about a year earlier showed more than 22,000 active clients, and more
than 80 million web requests during one week. Shifts in technology
use, such as the widespread use of DSL and cable modems, will likely
increase the use of streaming media and change underlying session
characteristics. As the use of streaming media matures in the
Internet environment, further client workload studies will be required to
update our understanding of the impact of streaming-media data.
We would like to thank David Richardson, Terry Gray, Art Dong, and others at
the UW Computing and Communications organization for supporting our effort to
collect traces. We would also like to thank the USITS referees for their
comments and suggestions. This research was supported in part by DARPA
Grant F30602-97-2-0226, National Science Foundation Grant EIA-9870740,
Compaq's Systems Research Center, and a Microsoft Graduate Research
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