NSDI '06 Paper
[NSDI '06 Technical Program]
Scale and Performance in the
CoBlitz Large-File Distribution Service
KyoungSoo Park and Vivek S. Pai
Department of Computer Science
Scalable distribution of large files has been the area of much
research and commercial interest in the past few years. In this paper,
we describe the CoBlitz system, which efficiently distributes large
files using a content distribution network (CDN) designed for HTTP. As
a result, CoBlitz is able to serve large files without requiring any
modifications to standard Web servers and clients, making it an
interesting option both for end users as well as infrastructure
services. Over the 18 months that CoBlitz and its partner service,
CoDeploy, have been running on PlanetLab, we have had the opportunity
to observe its algorithms in practice, and to evolve its design. These
changes stem not only from observations on its use, but also from a
better understanding of their behavior in real-world conditions. This
utilitarian approach has led us to better understand the effects of
scale, peering policies, replication behavior, and congestion, giving
us new insights into how to better improve their performance. With
these changes, CoBlitz is able to deliver in excess of 1 Gbps on
PlanetLab, and to outperform a range of systems, including research
systems as well as the widely-used BitTorrent.
Many new content distribution networks (CDNs) have recently been
developed to focus on areas not generally associated with
``traditional'' Web (HTTP) CDNs. These systems often focus on
distributing large files, especially in flash crowd situations where a
news story or software release causes a spike in demand. These new
approaches break away from the ``whole-file'' data transfer model, the
common access pattern for Web content. Instead, clients download
pieces of the file (called chunks, blocks, or objects) and exchange
these chunks with each other to form the complete file. The most
widely used system of this type is BitTorrent (12),
while related research systems include Bullet (20),
Shark (2), and FastReplica (9).
Using peer-to-peer systems makes sense when the window of interest in
the content is short, or when the content provider cannot afford
enough bandwidth or CDN hosting costs. However, in other scenarios, a
managed CDN service may be an attractive option, especially for
businesses that want to offload their bandwidth but want more
predictable performance. The problem arises from the fact that HTTP
CDNs have not traditionally handled this kind of traffic, and are not
optimized for this workload. In an environment where objects average
10KB, and where whole-file access is dominant, suddenly introducing
objects in the range of hundreds of megabytes may have undesirable
consequences. For example, CDN nodes commonly cache popular objects in
main memory to reduce disk access, so serving several large files at
once could evict thousands of small objects, increasing their latency
as they are reloaded from disk.
To address this problem, we have developed the CoBlitz large file
transfer service, which runs on top of the CoDeeN content distribution
network, an HTTP-based CDN. This combination provides several
benefits: (a) using CoBlitz to serve large files is as simple as
changing its URL - no rehosting, extra copies, or additional protocol
support is required; (b) CoBlitz can operate with unmodified clients,
servers, and tools like curl or wget, providing greater ease-of-use
for users and for developers of other services; (c) obtaining maximum
per-client performance does not require multiple clients to be
downloading simultaneously; and (d) even after an initial burst of
activity, the file stays cached in the CDN, providing latecomers with
the cached copy.
From an operational standpoint, this approach of running a large-file
transfer service on top of an HTTP content distribution network also
has several benefits: (a) given an existing CDN, the changes to
support scalable large-file transfer are small; (b) no dedicated
resources need to be devoted for the large-file service, allowing it
to be practical even if utilization is low or bursty; (c) the
algorithmic changes to efficiently support large files also benefit
Over the 18 months that CoBlitz and its partner service, CoDeploy,
have been running on PlanetLab, we have had the opportunity to observe
its algorithms in practice, and to evolve its design, both to reflect
its actual use, and to better handle real-world conditions. This
utilitarian approach has given us a better understanding of the
effects of scale, peering policies, replication behavior, and
congestion, giving us new insights into how to improve performance and
reliability. With these changes, CoBlitz is able to deliver in excess
of 1 Gbps on PlanetLab, and to outperform a range of systems,
including research systems as well as BitTorrent.
In this paper, we discuss what we have learned in the process, and how
the observations and feedback from long-term deployment have shaped
our system. We discuss how our algorithms have evolved, both to
improve performance and to cope with the scalability aspects of our
system. Some of these changes stem from observing the real behavior of
the system versus the abstract underpinnings of our original
algorithms, and others from observing how our system operates when
pushed to its limits. We believe that our observations will be useful
for three classes of researchers: (a) those who are considering
deploying scalable large-file transfer services; (b) those trying to
understand how to evaluate the performance of such systems, and; (c)
those who are trying to capture salient features of real-world
behavior in order to improve the fidelity of simulators and emulators.
In this section, we provide general information about HTTP CDNs, the
problems caused by large files, and the history of CoBlitz and
Content distribution networks relieve Web congestion by replicating
content on geographically-distributed servers. To provide load
balancing and to reduce the number of objects served by each node,
they use partitioning schemes, such as consistent
hashing (17), to assign objects to nodes. CDN
nodes tend to be modified proxy servers that fetch files on demand and
cache them as needed. Partitioning reduces the number of nodes that
need to fetch each object from the origin servers (or other CDN
nodes), allowing the nodes to cache more objects in main memory,
eliminating disk access latency and improving throughput.
In this environment, serving large files can cause several problems.
Loading a large file from disk can temporarily evict several thousand
small files from the in-memory cache, reducing the proxy's
effectiveness. Popular large files can stay in the main memory for a
longer period, making the effects more pronounced. To get a sense of
the performance loss that can occur, one can examine results from the
Proxy Cacheoffs (25), which show that the same proxies,
when operating as ``Web accelerators,'' can handle 3-6 times the
request rate than operating in ``forward mode,'' with much larger
working sets. So, if a CDN node suddenly starts serving a data set
that exceeds its physical memory, its performance will drop
dramatically, and latency rises sharply. Bruce Maggs, Akamai's VP of
``Memory pressure is a concern for CDN developers, because for optimal
latency, we want to ensure that the tens of thousands of popular
objects served by each node stay in the main memory. Especially in
environments where caches are deployed inside the ISP, any increase in
latency caused by objects being fetched from disk would be a
noticeable degradation. In these environments, whole-file caching of
large files would be a concern (21).''
Akamai has a service called EdgeSuite Net Storage,
where large files reside in specialized replicated storage, and are
served to clients via overlay routing (1).
We believe that this service demonstrates
that large files are a qualitatively different problem for
As a result of these problems and other concerns, most systems to
scalably serve large files departed from the use of HTTP-based CDNs.
Two common design principles are evident in these systems: treat large
files as a series of smaller chunks, and exchange chunks between
clients, instead of always using the origin server. Operating on
chunks allows finer-grained load balancing, and avoids the trade-offs
associated with large-file handling in traditional CDNs. Fetching
chunks from other peers not only reduces load on the origin, but also
increases aggregate capacity as the number of clients increases.
We subdivide these systems based on their inter-client communication
topology. We term those that rely on greedy selection or all-to-all
communication as examples of the swarm approach, while those
that use tree-like topologies are termed stream systems.
Swarm systems, such as BitTorrent (12) and
FastReplica (9), preceded stream systems, and scaled
despite relatively simple topologies. BitTorrent originally used a
per-file centralized directory, called a tracker, that lists clients
that are downloading or have recently downloaded the file. Clients use
this directory to greedily find peers that can provide them with
chunks. The newest BitTorrent can operate with tracker information
shared by clients. In FastReplica, all clients are known at the start,
and each client downloads a unique chunk from the origin. The clients
then communicate in an all-to-all fashion to exchange chunks. These
systems reduce link stress compared to direct downloads from the
origin, but some chunks may traverse shared links repeatedly if
multiple clients download them.
The stream systems, such as ESM (10),
SplitStream (8), Bullet (20),
Astrolabe (30), and FatNemo (4) address
the issues of load balancing and link stress by optimizing the
peer-selection process. The result generates a tree-like topology (or
a mesh or gossip-based network inside the tree), which tends to stay
relatively stable during the download process. The effort in
tree-building can produce higher aggregate bandwidths, suitable for
transmitting the content simultaneously to a large number of
receivers. The trade-off, however, is that the higher link utilization
is possible only with greater synchrony. If receivers are only
loosely synchronized and chunks are transmitted repeatedly on some
links, the transmission rate of any subtrees using those nodes also
decreases. As a result, these systems are best suited for synchronous
activity of a specified duration.
Operational model for CoBlitz improvement
This paper discusses our experience running two large-file
distribution systems, CoBlitz and CoDeploy, which operate on top of
the CoDeeN content distribution network. CoDeeN is a HTTP CDN that
runs on every available PlanetLab node, with access restrictions in
place to prevent abuse and to comply with hosting site policies. It
has been in operation for nearly three years, and currently handles
over 25 million requests per day. To use CoDeeN, clients configure
their browsers to use a CoDeeN node as a proxy, and all of their Web
traffic is then handled by CoDeeN. Note that this behavior is only
part of CoDeeN as a policy decision - CoBlitz does not require
changing any browser setting.
Both CoBlitz and CoDeploy use the same infrastructure, which we call
CoBlitz in the rest of this paper for simplicity. The main difference
between the two is the access mechanism - CoDeploy requires the
client to be a PlanetLab machine, while CoBlitz is publicly
accessible. CoDeploy was launched first, and allows PlanetLab
researchers to use a local instance of CoDeeN to fetch experiment
files. CoBlitz allows the public to access CoDeploy by providing a
simpler URL-based interface. To use CoBlitz, clients prepend the
original URL with https://coblitz.codeen.org:3125/ and fetch it
like any other URL. A customized DNS server maps the name
coblitz.codeen.org to a nearby PlanetLab.
In 18 months of operation, the system has undergone three sets of
changes: scaling from just North American PlanetLab nodes to all of
PlanetLab, changing the algorithms to reduce load at the origin
server, and changing the algorithms to reduce overall congestion and
increase performance. Our general mode of operation is shown in
Figure 1, and consists of four steps: (1)
deploy the system, (2) observe its behavior in actual operation, (3)
determine how the underlying algorithms, when exposed to the real
environment, cause the behaviors, and (4) adapt the algorithms to make
better decisions using the real-world data. We believe this approach
has been absolutely critical to our success in improving CoBlitz, as
we describe later in this paper.
Before discussing CoBlitz's optimizations, we first describe how we
have made HTTP CDNs amenable to handling large files. Our approach
has two components: modifying large file handling to efficiently
support them on HTTP CDNs, and modifying the request routing for these
CDNs to enable more swarm-like behavior under heavy load. Though we
build on the CoDeeN CDN, we do not believe any of these changes are
CoDeeN-specific - they could equally be applied to other
CDNs. Starting from an HTTP CDN maintains compatibility with standard
Web clients and servers, whereas starting with a stream-oriented CDN
might require more effort to efficiently support standard Web traffic.
We treat large files as a set of small files that can be spread across
the CDN. To make this approach as transparent as possible to clients
and servers, the dynamic fragmentation and reassembly of these small
files is performed inside the CDN, on demand. Each CDN node has an
agent that accepts clients' requests for large files and converts them
into a series of requests for pieces of the file. Pieces are
specified using HTTP1.1 byte ranges (14), which the
Apache Web server has supported since August 1996 (version 1.2), and
which appeared in other servers in the same timeframe. After these
requests are injected into the CDN, the results are reassembled by the
agent and passed to the client. For simplicity, this agent occupies a
different port number than regular CoDeeN requests. The process has
some complications, mostly related to the design of traditional CDNs,
limitations of HTTP, and the limitations of standard HTTP proxies
(which are used as the CDN nodes). Some of these problems include:
The client-facing agent converts a
single request for a large file into a series of requests for smaller
files. The new URLS are only a CDN-internal representation - neither
the client nor the origin server see them.
Chunk naming - If chunks are named using the original URL,
all of a file's chunks will share the same name, and will be routed
similarly since CDNs hash URLs for routing (31,16). Since we want to spread chunks across the CDN, we must
use a different chunk naming scheme.
Range caching - We know of no HTTP proxies that cache
arbitrary ranges of Web objects, though some can serve ranges from
cached objects, and even recreate a full object from all of its
chunks. Since browsers are not likely to ask for arbitrary and
disjoint pieces of an object, no proxies have developed the necessary
support. Since we want to cache at the chunk level instead of the file
level, we must address this limitation.
Congestion - During periods of bursty demand and heavy
synchrony, consistent hashing may produce roving instantaneous
congestion. If many clients at different locations suddenly ask for
the same file, a lightly-loaded CDN node may see a burst of request.
If the clients all ask for another file as soon as the first download
completes, another CDN node may become instantly congested. This
bursty congestion prevents using the aggregate CDN bandwidth
effectively over short time scales.
We address these problems as a whole, to avoid new problems from
piecemeal fixes. For example, adding range caching to the Squid proxy
has been discussed since 1998 (24), but would expand the
in-memory metadata structures, increasing memory pressure, and would
require changing the internet cache protocol (ICP) used by caches to
query each other. Even if we added this support to CoDeeN's proxies,
it would still require extra support in the CDN, since the range
information would have to be hashed along with the URL.
Large-file processing - 1. the
client sends the agent a request, 2. the agent generates a series of
URL-mangled chunk requests, 3. those requests are spread across the
CDN, 4. assuming cache misses, the URLs are de-mangled on egress, and
the responses are modified, 5. the agent collects the responses,
reassembles if needed, and streams it to the client
We modify intra-CDN chunk handling and request redirection by treating
each chunk as a real file with its own name, so the bulk of the CDN
does not need to be modified. This name contains the start and end
ranges of the file, so different chunks will have different hash
values. Only the CDN ingress/egress points are affected, at the
boundaries with the client and the origin server.
Egress and ingress transformations
when the CDN communicates with the origin server. The CDN internally
believes it is requesting a small file, and the egress transformation
requests a byte-range of a large file. The ingress converts the
server's response to a response for a complete small file, rather than
a piece of a large file.
The agent takes the client's request, converts it into a series of
requests for chunks, reassembles the responses, and sends it to the
client. The client is not aware that the request is handled in pieces,
and no browser modifications are needed. This process is implemented
in a small program on each CDN node, so communication between it and
the CDN infrastructure is cheap. The requests sent into the CDN,
shown in Figure 2, contain extended filenames that
specify the actual file and the desired byte range, as well as a
special header so that the CDN modifies these requests on egress.
Otherwise, these requests look like ordinary requests with slightly
longer filenames. The full set of steps are shown in
Figure 3, where each solid rectangle is a separate
machine connected via the Internet.
All byte-range interactions take place between the proxy and the
origin server - on egress, the request's name is reverted, and range
headers are added. The server's response is changed from a HTTP 206
code (partial content received) to 200 (full file received). The
underlying proxy never sees the byte-range transformations, so no
range-caching support is required. Figure 4 shows
this process with additional temporary headers. These headers contain
the file length, allowing the agent to provide the content length for
the complete download.
Having the agent use the local proxy avoids having to reimplement CDN
code (such as node liveness, or connection management) in the agent,
but can cause cache pollution if the proxy caches all of the agent's
requests. The ingress add a cache-control header that disallows local
caching, which is removed on egress when the proxy routes the request
to the next CDN node. As a result, chunks are cached at the next-hop
CDN nodes instead of the local node.
Since the CDN sees a large number of small file requests, it can use
its normal routing, replication, and caching policies. These cached
pieces can then be used to serve future requests. If a node
experiences cache pressure, it can evict as many pieces as needed,
instead of evicting one large file. Similarly, the addition/departure
of nodes will only cause missing pieces to be re-fetched, instead of
the whole file. The only external difference is that the server sees
byte-range requests from many proxies instead of one large file
request from one proxy.
The agent is the most complicated part of CoBlitz, since it must
operate smoothly, even in the face of unpredictable CDN nodes and
origin servers outside our control. The agent monitors the chunk
downloads for correctness checking and for performance. The
correctness checking consists of issues such as ensuring that the
server is capable of serving HTTP byte-range requests, verifying that
the response is cacheable, and comparing modification headers (file
length, last-modified time, etc) to detect if a file has changed at
the origin during its download. In the event of problems, the agent
can abort the download and return an error message to the client. The
agent is the largest part of CoBlitz - it consists of 770
semicolon-lines of code (1975 lines total), versus 60-70 lines of
changes for ingress/egress modifications.
To determine when to re-issue chunk fetches, the agent maintains
overall and per-chunk statistics during the download. Several factors
may slow chunk fetching, including congestion between the proxy and
its peers, operational problems at the peers, and congestion between
the peers and the origin. After downloading the first chunk, the agent
has the header containing the overall file size, and knows the total
number of chunks to download. It issues parallel requests up to its
limit, and uses non-blocking operations to read data from the sockets
as it becomes available.
Using an approach inspired by LoCI (3), slow transfers are
addressed by issuing multiple requests - whenever a chunk exceeds its
download deadline, the agent opens a new connection and re-issues the
chunk request. The most recent request for the same chunk is allowed
to continue downloading, and any earlier requests for the chunk are
terminated. In this way, each chunk can have at most two requests for
it in flight from the agent, a departure from LoCI where even more
connections are made as the deadline approaches. The agent modifies a
non-critical field of the URL in retry requests beyond the first
retried request for each chunk. This field is stripped from the URL on
egress, and exists solely to allow the agent to randomize the peer
serving the chunk. In this way, the agent can exert some control over
which peer serves the request, to reduce the chance of multiple
failures within the CDN. Keeping the same URL on the first retry
attempts to reduce cache pollution - in a load-balanced, replicated
CDN, the retry is unlikely to be assigned to the same peer that is
handling the original request.
The first retry timeout for each chunk is set using a combination of
the standard deviation and exponentially-weighted moving average for
recent chunks. Subsequent retries use exponential backoff to adjust
the deadline, up to a limit of 10 backoffs per chunk. To bound the
backoff time, we also have a hard limit of 10 seconds for the chunk
timeout. The initial timeout is set to 3 seconds for the first chunk
- while most nodes finish faster, using a generous starting point
avoids overloading slow origin servers. In practice, 10-20% of chunks
are retried, but the original fetch usually completes before the
retry. We could reduce retry aggressiveness, but this approach is
unlikely to cause much extra traffic to the origin since the first
retry uses a different replica with the same URL.
By default, the agent sends completed chunks to the client as soon as
they finish downloading, as long as all preceding chunks have also
been sent. If the chunk at the head of the line has not completed
downloading, no new data is sent to the client until the chunk
completes. By using enough parallel chunk fetches, delays in
downloading chunks can generally be overlapped with others in the
pipeline. If clients that can use chunked transfer encoding provide a
header in the request indicating they are capable of handling chunks
in any order, the agent sends chunks as they complete, with no
head-of-line blocking. Chunk position information is returned in a
trailer following each chunk, which the client software can use to
assemble the file in the correct order.
The choice of chunk size is a trade-off between efficiency and latency
- small chunks will result in faster chunk downloads, so slower
clients will have less impact. However, the small chunks require more
processing at all stages - the agent, the CDN infrastructure, and
possibly the origin server. Larger chunks, while more efficient, can
also cause more delay if head-of-line blocking arises. After some
testing, we chose a chunk size of 60KB, which is large enough to be
efficient, but small enough to be manageable. In particular, this
chunk size can easily fit into Linux's default outbound kernel socket
buffers, allowing the entire chunk to be written to the socket with a
single system call that returns without blocking.
We believe that this design has several important features that not
only make it practical for deployment now, but will continue to make
it useful in the future:
- No client synchronization - Since chunks are cached in
the CDN when first downloaded, no client synchronization is needed to
reduce origin traffic. If clients are highly synchronized, agents can
use the same chunk to serve many client requests, reducing the number
of intra-CDN transfers, but synchronization is not required for
- Trading bandwidth for disk seeks - Fetching most chunks
from other CDN nodes trades bandwidth for disk seeks. Given the rate
of improvement of each, this trade-off will hold for the foreseeable
future. Bandwidth is continually dropping in price, and disk seek
times are not scaling. If this bandwidth cost is an issue, it can be
billed just as regular bandwidth is billed.
- Increasing chunk utility - Having all nodes store
chunks makes them available to a larger population than storing the
entire file at a small number of nodes. Many more nodes can now serve
large files, so the total capacity is the sum of the bandwidths they
have to serve clients, and the aggregate intra-CDN capacity is
available to exchange chunks.
- Using cheaper bandwidth - When CDN nodes communicate
with each other, this bandwidth consumption is either within a LAN
cluster hosting the CDN nodes, or toward the network core, away from
the clients that sit at the edge of the network. Core bandwidth has
been improving in price/performance more rapidly than edge bandwidth,
and LAN bandwidth is virtually free, so this consumption is in the
more desirable direction.
- Scaling with CDN size - As CDN size increases, and
aggregate physical memory increases, chunks can be replicated more
widely. The net result is that desired chunks are more likely to be in
nearby nodes, so link stress drops as the CDN grows.
- Tunable memory consumption - Varying the number of
parallel chunks downloads that are used for each client controls the
memory consumption of this approach. Slower clients can be allocated
fewer parallel chunks, and the aggregate number of chunks can be
reduced if a node is experiencing heavy load.
- In-order or out-of-order delivery - For regular
browsers or other standard client software, chunks are delivered in
order so that the download appears exactly like a non-CoBlitz download
from the origin, and performance-hungry clients can use software
that supports chunked encoding.
One first challenge for CoBlitz was handling scale - at the time of
CoBlitz's original deployment, CoDeeN was running on all 100 academic
PlanetLab node in North America. The first major scale issue was
roughly quadrupling the node count, to include every PlanetLab node.
In the process, we adopted three design decisions that have served us
well: (a) make peering a unilateral, asynchronous decision, (b) use
minimum application-level ping times when determining suitable peers,
and (c) apply hysteresis to the peer set. These are described in the
remainder of this section.
Standard peering for 6 unrestricted nodes
Peering with semi-routable Internet2
Peering with policy-restricted nodes
In CoDeeN, we have intentionally avoided any synchronized
communication for group maintenance, which results in avoiding any
quorum protocols, 2-phase behavior, or any group membership protocols.
The motivations behind this decision were simplicity and robustness -
by making every decision unilaterally and independently at each node,
we avoid any situation where forward progress fails because some
handshaking protocol fails. As a result, CoDeeN has been operational
even in some very extreme circumstances, such as in February 2005,
when a kernel bug caused the sudden, near-simultaneous failures of
nodes, with more than half of all PlanetLab nodes freezing.
One side-effect of asynchronous communication is that all peering is
unilateral - nodes independently pick their peers, using periodic
heartbeats and acknowledgments to judge peer health. Pairwise
heartbeats are simple, robust, and particularly useful for testing
reachability. More sophisticated techniques, such as aggregating node
health information using trees, can reduce the number of heartbeats,
but can lead to worse information, since the tree may miss or
use different links than those used for pairwise communication.
Unilateral and unidirectional peering improves CoBlitz's scalability,
since it allows nodes with heterogeneous connectivity or policy issues
to participate to the extent possible. These scenarios are shown in
Figures 5, 6,
and 7. For example, research networks like
Internet2 or CANARIE (the Canadian high-speed network) do not peer
with the commercial Internet, but are reachable from a number of
research sites including universities and corporate labs. These nodes
advertise that they do not want any nodes (including each other) using
them as peers, since they cannot fetch content from the commercial
Internet. These sites can unidirectionally peer with any CoDeeN nodes
they can reach - regular CoDeeN nodes do not reciprocate, since the
restricted nodes cannot fetch arbitrary Web content. Also, in certain
PlanetLab locations, both corporate and regional, political/policy
considerations make the transit of arbitrary content an unwise idea,
but the area may have a sizable number of nodes. These nodes
advertise that only other nodes from the same organization can use
them as peers. These nodes will peer both with each other and with
unrestricted nodes, giving them more peers for CoBlitz transfers than
they would have available otherwise. Policy restrictions are not
PlanetLab-specific - ISPs host commercial CDN nodes in their network
with the restriction that the CDN nodes only serve their own
With the worldwide deployment of CoDeeN, some step had to be taken to
restrict the set of CoDeeN nodes that each node would use as peers.
Each CoDeeN node sends one heartbeat per second to another node, so at
600 PlanetLab nodes (of which 400 are alive at any time), a full
sweep would take 10 minutes. Using our earlier measurements that node
liveness is relatively stable at short timescales (32), we
limit the peer set to 60 nodes, which means that using an additional
once-per-second ping, the peers can be swept once per minute.
To get some indication of application health, CoDeeN uses
application-level pings, rather than network pings, to determine round
trip times (RTTs). Originally, CoDeeN kept the average of the four
most recent RTT values, and selected the 60 closest peers within a
100ms RTT cutoff. The 100ms cutoff was to reduce noticeable lag in
interactive settings, such as Web browsing. In parts of the world
where nodes could not find 20 peers within 100ms, this cutoff is
raised to 200ms and the 20 best peers are selected.
This approach exhibited two problems - a high rate of change in the
peer sets, and low overlap among peer sets for nearby peers. The high
change rate potentially impacts chunk caching in CoBlitz - if the
peer that previously fetched a chunk is no longer in the peer set, the
new peer that replaces it may not yet have fetched the chunk. To
address this issue, hysteresis was added to the peer set selection
process. Any peer not on the set could only replace a peer on the set
if it was closer in two-thirds of the last 32 heartbeats. Even under
the worst-case conditions, using the two-thirds threshold would keep a
peer on the set for 10 minutes at a time. While hysteresis reduced
peer set churn, it also reinforced the low overlap between neighboring
peer sets. Further investigation indicated that CoDeeN's
application-level heartbeats had more than an order of magnitude
variance than network pings. This variance led to instability in the
average RTT calculations, so once nodes were added to the peer set,
they rarely got displaced.
Switching from an average application-level RTT to the minimum observed RTT (an approach also used in other
systems (6,13,22)) and increasing the
number of samples yielded significant improvement, with
application-level RTTs correlating well with ping time on all
functioning nodes. Misbehaving nodes still showed large
application-level minimum RTTs, despite having low ping times. The
overlap of peer lists for nodes at the same site increased from
roughly half to almost 90%. At the same time, we discovered that many
intra-PlanetLab paths had very low latency, and restricting the peer
size to 60 was needlessly constrained. We increased this limit to 120
nodes, and issued 2 heartbeats per second. Of the nodes regularly
running CoDeeN, two-thirds tend to now have 100+ peers. More details
of the redesign process and its corresponding performance improvement
can be found in our previous study (5).
Replicated Highest Random Weight
with Load Balancing, as used in CoDeeN
It is interesting to consider whether this approach could scale to a
much larger system, such as a commercial CDN like Akamai. By the
numbers, Akamai is about 40 times as large as our deployment, at
15,000 servers across 1,100 networks. However, part of what makes
scaling to this size simpler is deploying clusters at each network
point-of-presence (POP), which number only 2,500. Further, their
servers have the ability to issue reverse ARPs and assume the IP
addresses of failing nodes in the cluster, something not permitted on
PlanetLab. With this ability, the algorithms need only scale to the
number of POPs, since the health of a POP can be used instead of
querying the status of each server. Finally, by imposing geographic
hierarchy and ISP-level restrictions, the problem size is further
reduced. With these assumptions, we believe that we can scale to
larger sizes without significant problems.
Reducing origin server load and reducing CDN-wide congestion are
related, so we present them together in this section. Origin load is
an important metric for CoBlitz, because it determines CoBlitz's
caching benefit and impacts the system's overall performance. From a
content provider's standpoint, CoBlitz would fetch only a single copy
of the content, no matter what the demand. However, for reasons
described below, this goal may not be practical.
CoDeeN uses the Highest Random Weight (HRW) (29)
algorithm to route requests from clients. This algorithm is
functionally similar to Consistent Hashing (17),
but has some properties that make it attractive when object
replication is desired (31). The algorithm used in
CoDeeN, Replicated HRW with Load Balancing, is shown in
For each URL, CoDeeN generates an array of values by hashing the URL
with the name of each node in the peer set. It then prunes this list
based on the replication factor specified, and then prunes it again so
only the nodes with the lowest load values remain. The final candidate
is chosen randomly from this set. Using replication and load balancing
reduces hot spots in the CDN - raising the replication factor reduces
the chance any node gets a large number of requests, but also
increases the node's working set, possibly degrading performance.
Increasing the peer set size, as described in
Section 4.2 has two effects - each node appears as
a peer of many more nodes than before, and the number of nodes chosen
to serve a particular URL is reduced. In the extreme, if all CDN
nodes were in each others' peer sets, then the total number of nodes
handling any URL would equal to NumCandidates. In practice, the
peer sets give rise to overlapping regions, so the number of nodes
serving a particular URL is tied to the product of the number of
regions and NumCandidates.
When examining origin server load in CoBlitz, we found that nodes with
fewer than five peers generate almost one-third of the traffic. Some
poorly-connected sites have such high latency that even with an
expanded RTT criterion, they find few peers. At the same time, few
sites use them as peers, leading to them being an isolated cluster.
For regular Web CDN traffic, these small clusters are not much of an
issue, but for large-file traffic, the extra load these clusters cause
on the origin server slows the rest of the CDN
significantly. Increasing the minimum number of peers per node to 60
reduces traffic to the origin. Because of unilateral peering, this
change does not harm nearby nodes - other nodes still avoid these
Reducing the number of replicas per URL reduces origin server load,
since fewer nodes fetch copies from the origin, but it also causes
more bursty traffic at those replicas if downloading is
synchronized. For CoBlitz, synchronized downloads occur when
developers push software updates to all nodes, or when cron-initiated
tasks simultaneously fetch the same file. In these cases, demand at
any node experiences high burstiness over short time scales, which
leads to congestion in the CDN.
Once other problems are addressed, differences in peer sets can also
cause a substantial load on the origin server. To understand how this
arises, imagine a CDN of 60 nodes, where each node does not see one
peer at random. If we ask all nodes for the top candidate in the HRW
list for a given URL, at least one node is likely to return the
candidate that would have been the second-best choice elsewhere. If we
ask for the top k candidates, the set will exceed k candidates with
very high probability. If each node is missing two peers at random,
the union of the sets is likely to be at least k+2. Making the matter
worse is that these ``extra'' nodes fetching from the origin also
provide very low utility to the rest of the nodes - since few nodes
are using them to fetch the chunk, they do not reduce the traffic at
the other replicas.
To fix this problem, we observe that when a node receives a forwarded
request, it can independently check to see whether it should be the
node responsible for serving that request. On every forwarded request
that is not satisfied from the cache, the receiving node performs its
own HRW calculation. If it finds itself as one of the top candidates,
it considers the forwarded request reasonable and fetches it from the
origin server. If the receiver finds that it is not one of the top
candidates, it forwards the request again. We find that 3-7% of
chunks get re-forwarded this way in CoBlitz, but it can get as high as
10-15% in some cases. When all PlanetLab nodes act as clients, this
technique cuts origin server load almost in half.
Due to the deterministic order of HRW, this approach is guaranteed to
make forward progress and be loop-free. While the worst case is a
number of hops linear in the number of peer groups, this case is also
exponentially unlikely. Even so, we limit this approach to only one
additional hop in the redirection, to avoid forwarding requests across
the world and to limit any damage caused by bugs in the forwarding
logic. Given the relatively low rate of chunks forwarded in this
manner, restricting it to only one additional hop appears sufficient.
To illustrate the burstiness resulting from improved peering, consider
a fully-connected clique of 120 CDN nodes that begin fetching a large
file simultaneously. If all have the same peer set, then each node in
the replica set k will receive 120/k requests, each for a
60KB chunk. Assuming 2 replicas, the traffic demand on each is 28.8
Mbits. Assuming a 10 Mbps link, it will be fully utilized for 3
seconds just for this chunk, and then the utilization will drop until
the next burst of chunks.
The simplest way of reducing the short time-scale node congestion is
to increase the number of replicas for each chunk, but this would
increase the number of fetches to the origin. Instead, we can improve
on the purely mesh-based topology by taking some elements of the
stream-oriented systems, which are excellent for reducing link
stress. These systems all build communication trees, which eliminates
the need to have the same data traverse a link multiple times. While
trees are an unattractive option for standard Web CDNs because they
add extra latency to every request fetched from the origin, a hybrid
scheme can help the large-file case, if the extra hops can reduce
We take the re-forwarding support to forward misdirected chunks, and
use it to create broader routing trees in the peer sets. We change the
re-forwarding logic to use a different number of replicas when
calculating the HRW set, leading to a broad replica set and a smaller
set of nodes that fetch from the origin. We set the NumCandidates value to 1 when evaluating the re-forwarding logic,
while dynamically selecting the value at the first proxy. The larger
replica set at the first hop reduces the burstiness at any node
without increasing origin load.
To dynamically select the number of replicas, we observe that we can
eliminate burstiness by spreading the requests equally across the
peers at all times. With a target per-client memory consumption, we
can determine how many chunks are issued in parallel. So, the
replication factor is governed by the following equation:
At 1 MB of buffer space per client, a 60KB chunk size, and 120 peers,
our replication factor will be 7. We can, of course, cap the number of
peers at some reasonable fraction of the maximum number of peers so
that memory pressure does not cause runaway replication for the sake
of load balancing. In practice, we limit the replication factor to
20% of the minimum target peer set, which yields a maximum factor of
Although parallel chunk downloads can exploit multi-path bandwidth and
reduce the effect of slow transfers, using a fixed number of parallel
chunks also has some congestion-related drawbacks which we address.
When the content is not cached, the origin server may receive more
simultaneous requests than it can handle if each client is using a
large number of parallel chunks. For example, the Apache Web Server is
configured by default to allow 150 simultaneous connection, and some
sites may not have changed this value. If a CDN node has limited
bandwidth to the rest of the CDN, too many parallel fetches can cause
self-congestion, possibly underutilizing bandwidth, and slowing down
the time of all fetches. The problem in this scenario is that too many
slow chunks will cause more retries than needed.
Throughput distribution for various window adjusting functions - Test scheme is described in section 6
In either of these scenarios, using a smaller number of simultaneous
fetches would be beneficial, since the per-chunk download time would
improve. We view finding the ``right'' number of parallel chunks as a
congestion issue, and address it in a manner similar to how TCP
performs congestion control. Note that changing the number of parallel
chunks is not an attempt to perform low-level TCP congestion control
- since the fetches are themselves using TCP, we have this benefit
already. Moreover, since the underlying TCP transport is already
using additive-increase multiplicative-decrease, we can choose
whatever scheme we desire on top of it.
Drawing on TCP Vegas (6), we use the extra information we
have in the CoBlitz agent to make the chunk ``congestion window'' a
little more stable than a simple sawtooth. We use three criteria: (1)
if the chunk finishes in less than the average time, increase the
window, (2) if the first fetch attempt is killed by retries, shrink
the window, and (3) otherwise, leave the window size unmodified. We
also decide that if more chunk fetches are in progress than the window
size dictates, existing fetches are allowed to continue, but no new
fetches (including retries) are allowed. Given that our condition for
increasing the window is already conservative, we give ourselves some
flexibility on exactly how much to add. Similarly, given that the
reason for requiring a retry might be that any peer is slow, we decide
against using multiplicative decrease when a chunk misses the
While determining the decrease rate is fairly easy, choosing a
reasonable increase rate required some experimentation. The decrease
rate was chosen to be one full chunk for each failed chunk, which
would have the effect of closing the congestion window very quickly if
all of the chunks outstanding were to retry. This logic is less severe
than multiplicative decrease if only a small number of chunks miss
their deadlines, but can shrink the window to a single chunk within
one ``RTT'' (in this case, average chunk download time) in the case of
Some experimentation with different increase rates is shown in
Figure 9. The purely additive condition,
on each fast chunk (where x is the current number of
chunks allowed), fares poorly. Even worse is adding one-tenth of a
chunk per fast chunk, which would be a slow multiplicative increase.
The more promising approaches, adding
(where we use log(1) = 1) produce much better
results. The case is not surprising, since it will
always be no more than additive, since the window grows only when
performing well. In TCP, the ``slow start'' phase would open the
window exponentially faster, so we choose to use
achieve a similar effect - it grows relatively quickly at first, and
more slowly with larger windows. The chunk congestion window is
maintained as a floating-point value, which has a lower bound of 1
chunk, and an upper bound as dictated by the buffer size available,
which is normally 60 chunks. The final line in the graph, showing a
fixed-size window of 60 chunks, appears to produce better performance,
but comes at the cost of a higher node failure rate - 2.5 times as
many nodes fail to complete with the fixed window size versus the
In this section, we evaluate the performance of CoBlitz, both in
various scenarios, and in comparison with
BitTorrent (12). We use BitTorrent because of its wide
use in large-file transfer (7), and because other
research systems, such as Slurpie, Bullet' and
Shark (2,26,19), are not running (or in some cases,
available) at the time of this writing. As many of these have been
evaluated on PlanetLab, we draw some performance and behavior
comparisons in Section 7.
One unique aspect of our testing is the scale - we use every running
PlanetLab node except those at Princeton, those designated as alpha
testing nodes, and those behind firewalls that prevent CoDeeN
traffic. The reason for excluding the Princeton nodes is because we
place our origin server at Princeton, so the local PlanetLab nodes
would exhibit unrealistically large throughputs and skew the
means. During our testing in September and early October 2005, the
number of available nodes that met the criteria above ranged from
360-380 at any given time, with a union size of 400 nodes.
Our test environment consists of a server with an AMD Sempron
processor running at 1.5 GHz, with Linux 2.6.9 as its operating system
and lighttpd 1.4.4 (18) as our web server. Our testing
consists of downloading a 50MB file in various scenarios. The choice
of this file size was to facilitate comparisons with other
work (2,19), which uses file sizes of 40-50MB in their
testing. Our testing using a 630MB ISO image for the Fedora Core 4
download yielded slightly higher performance, but would complicate
comparisons with other systems. Given that some PlanetLab nodes are in
parts of the world with limited bandwidth, our use of 50MB files also
reduces contention problems for them. Each test is run three times,
and the reported numbers are the average value across the tests for
which the node was available. Due to the dynamics of PlanetLab, over
any long period of time, the set of available nodes will change, and
given the span of our testing, this churn is unavoidable.
We tune BitTorrent for performance - the clients and the server are
configured to seed the peers indefinitely, and the maximum number of
peers is increased to 60. While live BitTorrent usage will have lower
performance due to fewer peers and peers departing after downloading,
we want the maximum BitTorrent performance.
We test a number of scenarios, as follows:
- Direct - all clients fetch from the origin in a single
download, which would be typical of standard browsers. For
performance, we increase the socket buffer sizes from the system
defaults to cover the bandwidth-delay product.
- BitTorrent Total - this is a wall-clock timing of
BitTorrent, which reflects the user's viewpoint. Even when all
BitTorrent clients are started simultaneously, downloads begin
at different times since clients spend different amounts of time
contacting the tracker and finding peers.
- BitTorrent Core - this is the BitTorrent performance from
the start of the actual downloading at each client. In general, this
value is 25-33% higher than the BitTorrent Total time, but is
sometimes as much as 4 times larger.
- In-order CoBlitz with Synchronization - Clients use
CoBlitz to fetch a file for the first time and the chunks are
delivered in order. All clients start at the same time.
- In-order CoBlitz with Staggering - We stagger the start
of each client by the same amount of time that BitTorrent uses before
it starts downloading. These stagger times are typically 20 to
40 seconds, with a few outliers as high as 150-230 seconds.
- In-order CoBlitz with Contents Cached - Clients ask for
a file that has already been fetched previously, and whose chunks are
cached at the reverse proxies. All clients begin at the same time.
- Out-of-order tests - Out-of-order CoBlitz with
Synchronization, Out-of-order CoBlitz with Staggering, and
Out-of-order CoBlitz with Contents Cached are the same as their
in-order counterparts described above, but the chunks are delivered to
the clients out of order.
Achieved throughput distribution for all live PlanetLab nodes
Download times across all live PlanetLab nodes
The throughputs and download times for all tests are shown in
Figure 10 and Figure 11, with
summaries presented in Table 1. For clarity, we
trim the x axes of both graphs, and the CDFs shown are of all nodes
completing the tests. The actual number of nodes finishing each test
are shown in the table. In the throughput graph, lines to the right are
more desirable, while in the download time graph, lines to the left
are more desirable.
Throughputs (in Mbps) and
times (in seconds) for various downloading approaches with all live
PlanetLab nodes. The count of good nodes is the typical value for nodes
completing the download, while the count of failed nodes shows the range
of node failures.
| Download Time
|CoBlitz In-order Sync'd
|CoBlitz In-order Staggered
|CoBlitz In-order Cached
|CoBlitz Out-of-order Sync'd
|CoBlitz Out-of-order Staggered
|CoBlitz Out-of-order Cached
From the graphs, we can see several general trends: all schemes beat
direct downloading, uncached CoBlitz generally beats BitTorrent,
out-of-order CoBlitz beats in-order delivery, staggered downloading
beats synchronized delivery, and cached delivery, even when
synchronized, beats the others. Direct downloading at this scale is
particularly problematic - we had to abruptly shut down this test
because it was consuming most of Princeton's bandwidth and causing
noticeable performance degradation.
The worst-case performance for CoBlitz occurs for the uncached case
where all clients request the content at exactly the same time and
more load is placed on the origin server at once. This case is also
very unlikely for regular users, since even a few seconds of
difference in start times defeats this problem.
The fairest comparison between BitTorrent and CoBlitz is BT-Total
versus CoBlitz out-of-order with Staggering, in which case CoBlitz
beats BitTorrent by 55-86% in throughput and factor of 1.7 to 4.94 in
download time. Even the worst-case performance for CoBlitz, when all
clients are synchronized on uncached content, generally beats
BitTorrent by 27-48% in throughput and a factor of 1.47 to 2.48 in
In assessing how well CoBlitz compares against BitTorrent, it is
interesting to examine the 90 percentile download times in
Table 1 and compare them to the mean and median
throughputs. This comparison has appeared in other papers comparing
with BitTorrent (26,19).
We see that the tail of BitTorrent's download times is much worse than
comparing the mean or median values. As a result, systems that
compare themselves primarily with the worst-case times may be
presenting a much more optimistic benefit than seen by the majority of
It may be argued that worst-case times are important for systems that
need to know an update has been propagated to its members, but if this
is an issue, more important than delay is failure to complete. In
Table 1, we show the number of nodes that finish
each test, and these vary considerably despite the fact that the same
set of machines is being used. Of the approximately 400 machines
available across the union of all tests, only about 5-12 nodes fail to
complete using CoBlitz, while roughly 17-18 fail in direct testing,
and about 21-25 fail with BitTorrent. The 5-12 nodes where CoBlitz
eventually stops trying to download are at PlanetLab sites with
highly-congested links, poor bandwidth, and other problems - India,
Australia, and some Switzerland nodes.
Bandwidth consumption at the origin,
measured in multiples of the file size
Another metric of interest is how much traffic reaches the origin
server in these different tests, and this information is provided in
Table 2, shown as a multiple of the file size. We
see that the CoBlitz scenarios fetch a total of 7 to 9 copies in the
various tests, which yields a utility of 43-55 nodes served per fetch
(or a cache hit rate of 97.6 - 98.2%). BitTorrent has comparable
overall load on the origin, at 10 copies, but has a lower utility
value, 35, since it has fewer nodes complete. For Shark, the authors
observed it downloading 24 copies from the origin to serve 185 nodes,
yielding a utility of 7.7. We believe that part of the difference may
stem from peering policy - CoDeeN's unilateral peering approach
allows poorly-connected nodes to benefit from existing clusters, while
Coral's latency-oriented clustering may adversely impact the number of
Reverse proxy location distribution
A closer examination of fetches per chunk, shown in
Figure 12, shows that CoBlitz's average of 8
copies varies from 4-11 copies by chunk, and these copies appear to be
spread fairly evenly geographically. The chunks that receive only 4
fetches are particularly interesting, because they suggest it may be
possible to cut CoBlitz's origin load by another factor of 2. We are
investigating whether these chunks happen to be served by nodes that
overlap with many peer sets, which would further validate CoBlitz's
Finally, we evaluate the performance of CoBlitz after a flash crowd,
where the CDN nodes can still have the file cached. This was one of
motivations for building CoBlitz on top of CoDeeN - that by using an
infrastructure geared toward long-duration caching, we could serve the
object quickly even after demand for it drops. This test is shown in
Figure 13, where clients at all PlanetLab nodes try
downloading the file individually, with no two operating
simultaneously. We see that performance is still good after the flash
crowd has dissipated - the median for this in-order test is above 7
Mbps, almost tripling the median for in-order uncached and doubling
the median of in-order cached. At this bitrate, clients can watch
DVD-quality video in real time. We include BitTorrent only for
comparison purposes, and we see that its median has only marginally
improved in this scenario.
Single node download after flash crowds
One of our main motivations when developing CoBlitz was to build a
system that could be used in production, and that could operate with
relatively little monitoring. These decisions have led us not only to
use simpler, more robust algorithms where possible, but also to
restrict the content that we serve. To keep the system usage focused
on large-file transfer with a technical focus, and to prevent
general-purpose bandwidth cost-shifting, we have placed restrictions
on what the general public can serve using CoBlitz. Unless the original
file is hosted at a university, CoBlitz will not serve HTML files,
most graphics types, and most audio/video formats. As a result of these
policies, we have not received any complaints related to the content
served by CoBlitz, which has simplified our operational overhead.
CoBlitz Feb 2006 usage by requests
CoBlitz Feb 2006 usage by bytes served
CoBlitz traffic in Kbps on release of Fedora Core 5, averaged
over 15-minute intervals. The 5-minute peaks exceeded 700 Mbps.
To get a sense of a typical month's CoBlitz usage, we present the
breakdown for February 2006 traffic in Figures 14
(by number of requests) and 15 (by bytes
served). Most of the requests for files less than 2MB come from the
Stork service (28), which provides package management on
PlanetLab, and the CiteSeer Digital Library (11), which
provides document downloads via CoBlitz. The two spikes in bytes
served are from the Fedora Core Linux distribution, available as
either downloadable CD images or DVD images. Most of the remaining
traffic comes from smaller sites, other PlanetLab users, and Fedora
Core RPM downloads.
A more unusual usage pattern occurred on March 20, 2006, when the
Fedora Core 5 Linux distribution was released. Within minutes of the
official announcement on the Fedora mailing lists, the availability
was mentioned on the front page of Slashdot (27), on a
Monday morning for the US. The measurements from this day and the
previous day are shown in Figure 16. In less than an
hour, CoBlitz went from an average of 20Mbps of traffic to over 400
Mbps, and sustained 5-minute peaks exceeded 700Mbps. CoBlitz
functioned as expected, with one exception - many of the clients were
using ``download agents'' that fetch files using a ``no-cache'' HTTP
header. CoBlitz had been honoring these requests for PlanetLab
researchers who wanted to force refreshes, and we had not seen a
problem in other environments. However, for this download, these
headers were causing unnecessary fetches to the origin that were
impacting performance. We made a policy decision to disregard these
headers for the Fedora Mirror sites, at which point origin traffic
dropped dramatically. This flash crowd had a relatively long tail -
it average 200-250Mbps on the third day, and only dropped to less than
100Mbps on the fifth day, a weekend. The memory footprint of CoBlitz
was also low - even serving the CD and DVD images on several
platforms (PPC, i386, x86_64), the average memory consumption was
only 75MB per node.
Several projects that perform large file transfer have been measured
on PlanetLab, with the most closely related ones being
Bullet' (19), and Shark (2), which is built on
Coral (15). Though neither system is currently accessible to
the public, both have been evaluated recently. Bullet', which
operates out-of-order and uses UDP, is reported to achieve 7 Mbps when
run on 41 PlanetLab hosts at different sites. In testing under
similar conditions, CoBlitz achieves 7.4 Mbps (uncached) and 10.6 Mbps
(cached) on average. We could potentially achieve even higher results
by using a UDP-based transport protocol, but our experience suggests
that UDP traffic causes more problems, both from intrusion detection
systems as well as stateful firewalls. Shark's performance for
transferring a 40MB file across 185 PlanetLab nodes shows a median
throughput of 0.96 Mbps. As discussed earlier, Shark serves an average
of only 7.7 nodes per fetch, which suggests that their performance may
improve if they use techniques similar to ours to reduce origin server
load. The results for all of these systems are shown in
Table 3. The missing data for Bullet' and
Shark reflect the lack of information in the publications, or
difficulty extracting the data from the provided graphs.
The use of parallel downloads to fetch a file has been explored
before, but in a more narrow context - Rodriguez et al. use
HTTP byte-range queries to simultaneously download chunks in parallel
from different mirror sites (23). Their primary goal was
to improve single client downloading performance, and the full file is
pre-populated on all of their mirrors. What distinguishes CoBlitz from
this earlier work is that we make no assumptions about the existence
of the file on peers, and we focus on maintaining stability of the
system even when a large number of nodes are trying to download
simultaneously. CoBlitz works if the chunks are fully cached,
partially cached, or not at all cached, fetching any missing chunks
from the origin as needed. In the event that many chunks need to be
fetched from the origin, CoBlitz attempts to reduce origin server
overload. Finally, from a performance standpoint, CoBlitz attempts to
optimize the memory cache hit rate for chunks, something not
considered in Rodriguez's system.
Throughput results (in Mbps)
for various systems at specified deployment sizes on PlanetLab. All
measurements are for 50MB files, except for Shark, which uses 40MB.
While comparing with other work is difficult due to the difference in
test environment, we can make some informed conjecture based on our
experiences. FastReplica's evaluation includes tests of 4-8 clients,
and their per-client throughput drops from 5.5 Mbps with 4 clients to
3.6 Mbps with 8 clients (9). Given that their file is
broken into a small number of equal-sized pieces, the slowest node in
the system is the overall bottleneck. By using a large number of
small, fixed-size pieces, CoBlitz can mitigate the effects of slow
nodes, either by increasing the number of parallel fetches, or by
retrying chunks that are too slow. Another system, Slurpie, limits the
number of clients that can access the system at once by having each
one randomly back off such that only a small number are contacting the
server regardless of the number of nodes that want service. Their
local-area testing has clients contact the server at the rate of one
every three seconds, which staggers it far more than
BitTorrent. Slurpie's evaluation on PlanetLab provides no absolute
performance numbers (26), making it difficult to draw
comparisons. However, their performance appears to degrade beyond 16
The scarcity of deployed systems for head-to-head
comparisons supports part of our motivation - by reusing CDN
infrastructure, we have been able to easily deploy CoBlitz and keep
We show that, with a relatively small amount of modification, a
traditional, HTTP-based content distribution network can be made to
efficiently support scalable large-file transfer. Even with no
modifications to clients, servers, or client-side software, our
approach provides good performance under demanding conditions, but can
provide even higher performance if clients implement a relatively
simple HTTP feature, chunked encoding.
Additionally, we show how we have taken the experience gained from 18
months of CoBlitz deployment, and used it to adapt our algorithms
to be more aware of real-world conditions. We demonstrate the
advantages provided by this approach by evaluating CoBlitz's
performance across all of PlanetLab, where it exceeds the performance
of BitTorrent as well as all other research efforts known to us.
In the process of making CoBlitz handle scale and reduce congestion
both within the CDN and at the origin server, we identify a number of
techniques and observations that we believe can be applied to other
systems of this type. Among them are: (a) using unilateral peering,
which simplifies communication as well as enabling the inclusion of
policy-limited or poorly-connected nodes, (b) using request
re-forwarding to reduce the origin server load when nodes send
requests to an overly-broad replica set, (c) dynamically adjusting
replica sets to reduce burstiness in short time scales, (d)
congestion-controlled parallel chunk fetching, to reduce both origin
server load as well as self-interference at slower CDN nodes.
We believe that the lessons we have learned from CoBlitz should help
not only the designers of future systems, but also provide a better
understanding of how to design these kinds of algorithms to reflect
the unpredictable behavior we have seen in real deployment.
We would like to thank our shepherd, Neil Spring, and the anonymous
reviewers for their useful feedback on the paper. This work was
supported in part by NSF Grants ANI-0335214, CNS-0439842, and
Akamai Technologies Inc., 1999.
S. Annapureddy, M. J. Freedman, and D. Mazières.
Shark: Scaling file servers via cooperative caching.
In 2nd USENIX/ACM Symposium on Networked Systems Design and
Implementation (NSDI '05), Boston, MA, May 2005.
M. Beck, D. Arnold, A. Bassi, F. Berman, H. Casanova, J. Dongarra, T. Moore,
G. Obertelli, J. Plank, M. Swany, S. Vadhiyar, and R. Wolski.
Logistical computing and internetworking: Middleware for the use of
storage in communication.
In 3rd Annual International Workshop on Active Middleware
Services (AMS), San Francisco, August 2001.
S. Birrer, D. Lu, F. E. Bustamante, Y. Qiao, and P. Dinda.
FatNemo: Building a resilient multi-source multicast fat-tree.
In Proceedings of Ninth International Workshop on Web Content
Caching and Distribution (IWCW'04), Beijing, China, October 2004.
B. Biskeborn, M. Golightly, K. Park, and V. S. Pai.
(Re)Design considerations for scalable large-file content
In Proceedings of Second Workshop on Real, Large Distributed
Systems(WORLDS), San Francisco, CA, December 2005.
L. Brakmo, S. O'Malley, and L. Peterson.
Tcp vegas: New techniques for congestion detection and avoidance.
In Proceedings of the SIGCOMM '94 Symposium, August 1994.
M. Castro, P. Drushcel, A. Kermarrec, A. Nandi, A. Rowstron, and A. Singh.
SplitStream: High-bandwidth content distribution in a cooperative
In Proceedings of SOSP'03, Oct 2003.
L. Cherkasova and J. Lee.
FastReplica: Efficient large file distribution within content
In Proceedings of the 4th USITS, Seattle, WA, March 2003.
Y. Chu, S. G. Rao, S. Seshan, and H. Zhang.
A case for end system multicast.
In IEEE Journal on Selected Areas in Communication (JSAC),
Special Issue on Networking Support for Multicast, 2002.
CiteSeer Scientific Literature Digital Library.
F. Dabek, R. Cox, F. Kaashoek, and R. Morris.
Vivaldi: A decentralized network coordinate system.
In Proceedings of the ACM SIGCOMM '04 Conference, Portland,
Oregon, August 2004.
R. Fielding, J. Gettys, J. Mogul, H. Frystyk, L. Masinter, P. Leach, and
Hypertext transfer protocol - HTTP1.1.
RFC 2616, June 1999.
M. J. Freedman, E. Freudenthal, and D. Mazieres.
Democratizing content publication with coral.
In Proceedings of the 1st USENIX/ACM Symposium on Networked
Systems Design and Implementation(NSDI'04), 2004.
D. Karger, A. Sherman, A. Berkheimer, B. Bogstad, R. Dhanidina, K. Iwamoto,
B. Kim, L. Matkins, and Y. Yerushalmi.
Web caching with consistent hashing.
In Proceedings of the Eighth International World-Wide Web
D. R. Karger, E. Lehman, F. T. Leighton, R. Panigrahy, M. S. Levine, and
Consistent hashing and random trees: Distributed caching protocols
for relieving hot spots on the world wide web.
In ACM Symposium on Theory of Computing, 1997.
D. Kostic, R. Braud, C. Killian, E. Vandekieft, J. W. Anderson, A. C. Snoeren,
and A. Vahdat.
Maintaining high bandwidth under dynamic network conditions.
In Proceedings of 2005 USENIX Annual Technical Conference,
D. Kostic, A. Rodriguez, J. Albrecht, and A. Vahdat.
Bullet: high bandwidth data dissemination using an overlay mesh.
In Proceedings of the 19th ACM SOSP, 2003.
Personal communication (email) with Vivek S. Pai, October
S. Rhea, D. Geels, T. Roscoe, and J. Kubiatowicz.
Handling churn in a DHT.
In Proceedings of the USENIX Annual Technical Conference,
P. Rodriguez, A. Kirpal, and E. W. Biersack.
Parallel-access for mirror sites in the Internet.
In Proceedings of IEEE Infocom, Tel-Aviv, Israel, March 2000.
Range requests - squid mailing list.
A. Rousskov, M. Weaver, and D. Wessels.
The fourth cache-off.
R. Sherwood, R. Braud, and B. Bhattacharjee.
Slurpie: A cooperative bulk data transfer protocol.
In Proceedings of IEEE Infocom, Hong Kong, 2004.
Stork on PlanetLab.
D. G. Thaler and C. V. Ravishankar.
Using name-based mappings to increase hit rates.
IEEEACM Transactions on Networking, 6(1):1-14, Feb.
R. van Renesse, K. Birman, and W. Vogels.
Astrolabe: A robust and scalable technology for distributed system
monitoring, management, and data mining.
In ACM Transactions on Computer Systems, May 2003.
L. Wang, V. Pai, and L. Peterson.
The Effectiveness of Request Redirecion on CDN Robustness.
In Proceedings of the Fifth Symposium on Operating Systems
Design and Implementation(OSDI), Boston, MA, December 2002.
L. Wang, K. Park, R. Pang, V. Pai, and L. Peterson.
Reliability and security in the CoDeeN content distribution
In Proceedings of the USENIX Annual Technical Conference,
Scale and Performance in the
CoBlitz Large-File Distribution Service
This document was generated using the
LaTeX2HTML translator Version 2002-1 (1.69)
Copyright © 1993, 1994, 1995, 1996,
Computer Based Learning Unit, University of Leeds.
Copyright © 1997, 1998, 1999,
Mathematics Department, Macquarie University, Sydney.
The command line arguments were:
latex2html -split 0 -show_section_numbers -local_icons bigfile.tex
The translation was initiated by KyoungSoo Park on 2006-03-28