blob: c340e9f784ebe646ffda2559f9534ae408408a0b [file] [log] [blame]
Internet Engineering Task Force M. Sustrik, Ed.
Internet-Draft GoPivotal Inc.
Intended status: Informational August 09, 2013
Expires: February 10, 2014
Request/Reply Scalability Protocol
This document defines a scalability protocol used for distributing
processing tasks among arbitrary number of stateless processing nodes
and returning the results of the processing.
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1. Introduction
One of the most common problems in distributed applications is how to
delegate a work to another processing node and get the result back to
the original node. In other words, the goal is to utilise the CPU
power of a remote node.
There's a wide range of RPC systems addressing the problem, however,
instead of relying on simple RPC algorithm, we will aim at solving a
more general version of the problem. First, we want to issue
processing requests from multiple clients, not just a single one.
Second, we want to distribute the tasks to any number processing
nodes instead of a single one so that the processing can be scaled up
by adding new processing nodes as necessary.
Solving the generalised problem requires that the algorithm executing
the task in question -- also known as "service" -- is stateless.
To put it simply, the service is called "stateless" when there's no
way for the user to distinguish whether a request was processed by
one instance of the service or another one.
So, for example, a service which accepts two integers and multiplies
them is stateless. Request for "2x2" is always going to produce "4",
no matter what instance of the service have computed it.
Service that accepts empty requests and produces the number of
requests processed so far (1, 2, 3 et c.), on the other hand, is not
stateless. To prove it you can run two instances of the service.
First reply, no matter which instance produces it is going to be 1.
Second reply though is going to be either 2 (if processed by the same
instance as the first one) or 1 (if processed by the other instance).
You can distinguish which instance produced the result. Thus,
according to the definition, the service is not stateless.
Despite the name, being "stateless" doesn't mean that the service has
no state at all. Rather it means that the service doesn't retain any
business-logic-related state in-between processing two subsequent
requests. The service is, of course, allowed to have state while
processing a single request. It can also have state that is
unrelated to its business logic, say statistics about the processing
that are used for administrative purposes and never returned to the
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Also note that "stateless" doesn't necessarily mean "fully
deterministic". For example, a service that generates random numbers
is non-deterministic. However, the client, after receiving a new
random number cannot tell which instance has produced it, thus, the
service can be considered stateless.
While stateless services are often implemented by passing the entire
state inside the request, they are not required to do so. Especially
when the state is large, passing it around in each request may be
impractical. In such cases, it's typically just a reference to the
state that's passed in the request, such as ID or path. The state
itself can then be retrieved by the service from a shared database, a
network file system or similar storage mechanism.
Requiring services to be stateless serves a specific purpose. It
allows for using any number of service instances to handle the
processing load. After all, the client won't be able to tell the
difference between replies from instance A and replies from instance
B. You can even start new instances on the fly and get away with it.
The client still won't be able to tell the difference. In other
words, statelessness is a prerequisite to make your service cluster
fully scalable.
Once it is ensured that the service is stateless there are several
topologies for a request/reply system to form. What follows are the
most common:
1. One client sends a request to one server and gets a reply. The
common RPC scenario.
2. Many clients send requests to one server and get replies. The
classic client/server model. Think of a database server and
database clients. Alternatively think of a messaging broker and
messaging clients.
3. One client send requests to many servers and gets replies. The
load-balancer model. Think of HTTP load balancers.
4. Many clients send requests to be processed by many servers. The
"enterprise service bus" model. In the simplest case the bus can
be implemented as a simple hub-and-spokes topology. In complex
cases the bus can span multiple physical locations or multiple
oraganisations with intermediate nodes at the boundaries
connecting different parts of the topology.
In addition to distributing tasks to processing nodes, request/reply
model comes with full end-to-end reliability. The reliability
guarantee can be defined as follows: As long as the client is alive
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and there's at least one server accessible from the client, the task
will eventually get processed and the result will be delivered back
to the client.
End-to-end reliability is achieved, similar to TCP, by re-sending the
request if the client believes the original instance of the request
has failed. Typically, request is believed to have failed when
there's no reply received within a specified time.
Note that, unlike with TCP, the reliability algorithm is resistant to
a server failure. Even if server fails while processing a request,
the request will be re-sent and eventually processed by a different
instance of the server.
As can be seen from the above, one request may be processed multiple
times. For example, reply may be lost on its way back to the client.
Client will assume that the request was not processed yet, it will
resend it and thus cause duplicit execution of the task.
Some applications may want to prevent duplicit execution of tasks.
It often turns out that hardening such applications to be idempotent
is relatively easy as they already possess the tools to do so. For
example, a payment processing server already has access to a shared
database which it can use to verify that the payment with specified
ID was not yet processed.
On the other hand, many applications don't care about occasional
duplicitly processed tasks. Therefore, request/reply protocol does
not require the service to be idempotent. Instead, the idempotancy
issue is left to the user to decide on.
Finally, it should be noted that this specification discusses several
features that are of little use in simple topologies and are rather
aimed at large, geographically or organisationally distributed
topologies. Features like channel prioritisation and loop avoidance
fall into this category.
2. Underlying protocol
The request/reply protocol can be run on top of any SP mapping, such
as, for example, SP TCP mapping.
Also, given that SP protocols describe the behaviour of entire
arbitrarily complex topology rather than of a single node-to-node
communication, several underlying protocols can be used in parallel.
For example, a client may send a request via WebSocket, then, on the
edge of the company network an intermediary node may retransmit it
using TCP et c.
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+---+ WebSocket +---+ TCP +---+
| |-------------| |-----------| |
+---+ +---+ +---+
| |
+---+ IPC | | SCTP +---+ DCCP +---+
| |---------+ +--------| |-----------| |
+---+ +---+ +---+
3. Overview of the algorithm
Request/reply protocol defines two different endpoint types: The
requester or REQ (the client) and the replier or REP (the service).
REQ endpoint can be connected only to a REP endpoint. REP endpoint
can be connected only to the REQ endpoint. If the underlying
protocol indicates that there's an attempt to create a channel to an
incompatible endpoint, the channel MUST NOT be used. In the case of
TCP mapping, for example, the underlying TCP connection MUST be
When creating more complex topologies, REQ and REP endpoints are
paired in the intermediate nodes to form a forwarding component, so
called "device". Device receives requests from the REP endpoint and
forwards them to the REQ endpoint. At the same time it receives
replies from the REQ endpoint and forwards them to the REP endpoint:
--- requests -->
+-----+ +-----+-----+ +-----+-----+ +-----+
| |-->| | |-->| | |-->| |
| REQ | | REP | REQ | | REP | REQ | | REP |
| |<--| | |<--| | |<--| |
+-----+ +-----+-----+ +-----+-----+ +-----+
<-- replies ---
Using devices, arbitrary complex topologies can be built. The rest
of this section explains how are the requests routed through a
topology towards processing nodes and how are replies routed back
from processing nodes to the original clients, as well as how the
reliability is achieved.
The idea for routing requests is to implement a simple coarse-grained
scheduling algorithm based on pushback capabilities of the underlying
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The algorithm works by interpreting pushback on a particular channel
as "the part of topology accessible through this channel is busy at
the moment and doesn't accept any more requests."
Thus, when a node is about to send a request, it can choose to send
it only to one of the channels that don't report pushback at the
moment. To implement approximately fair distibution of the workload
the node choses a channel from that pool using the round-robin
As for delivering replies back to the clients, it should be
understood that the client may not be directly accessible (say using
TCP/IP) from the processing node. It may be beyond a firewall, have
no static IP address et c. Furthermore, the client and the processing
may not even speak the same transport protocol -- imagine client
connecting to the topology using WebSockets and processing node via
Given the above, it becomes obvious that the replies must be routed
back through the existing topology rather than directly. In fact,
request/reply topology may be thought of as an overlay network on the
top of underlying transport mechanisms.
As for routing replies within the request/topology, it is designed in
such a way that each reply contains the whole routing path, rather
than containing just the address of destination node, as is the case
with, for example, TCP/IP.
The downside of the design is that replies are a little bit longer
and that is in intermediate node gets restarted, all the requests
that were routed through it will fail to complete and will have to be
resent by request/reply end-to-end reliability mechanism.
The upside, on the other hand, is that the nodes in the topology
don't have to maintain any routing tables beside the simple table of
adjacent channels along with thier IDs. There's also no need for any
additional protocols for distributing routing information within the
The most important reason for adopting the design though is that
there's no propagation delay and any nodes becomes accessible
immediately after it is started. Given that some nodes in the
topology may be extremely short-lived this is a crucial requirement.
Imagine a database client that sends a query, reads the result and
terminates. It makes no sense to delay the whole process till the
routing tables are synchronised between the client and the server.
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The algorithm thus works as follows: When request is routed from the
client to the processing node, every REP endpoint determines which
channel it was received from and adds the ID of the channel to the
request. Thus, when the request arrives at the ultimate processing
node it already contains a full backtrace stack, which in turn
contains all the info needed to route a message back to the original
After processing the request, the processing node attaches the
backtrace stack from the request to the reply and sends it back to
the topology. At that point every REP endpoint can check the
traceback and determine which channel it should send the reply to.
In addition to routing, request/reply protocol takes care of
reliability, i.e. ensures that every request will be eventually
processed and the reply will be delivered to the user, even when
facing failures of processing nodes, intermediate nodes and network
Reliability is achieved by simply re-sending the request, if the
reply is not received with a certain timeframe. To make that
algorithm work flawlessly, the client has to be able to filter out
any stray replies (delayed replies for the requests that we've
already received reply to).
The client thus adds an unique request ID to the request. The ID
gets copied from the request to the reply by the processing node.
When the reply gets back to the client, it can simply check whether
the request in question is still being processed and if not so, it
can ignore the reply.
To implement all the functionality described above, messages (both
requests and replies have the following format:
+-+------------+-+------------+ +-+------------+-------------+
|0| Channel ID |0| Channel ID |...|1| Request ID | payload |
+-+------------+-+------------+ +-+------------+ ------------+
Payload of the message is preceded by a stack of 32-bit tags. The
most significant bit of each tag is set to 0 except for the very last
tag. That allows the algorithm to find out where the tags end and
where the message payload begins.
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As for the reamining 31 bits, they are either request ID (in the last
tag) or a channel ID (in all the remaining tags). The first channel
ID is added and processed by the REP endpoint closest to the
processing node. The last channel ID is added and processed by the
REP endpoint closest to the client.
Following picture shows an example of request saying "Hello" being
routed from the client through two intermediate nodes to the
processing node and the reply "World" being routed back. It shows
what messages are passed over the network at each step of the
Hello | World
| +-----+ ^
| | REQ | |
V +-----+ |
1|823|Hello | 1|823|World
| +-----+ ^
| | REP | |
| +-----+ |
| | REQ | |
V +-----+ |
0|299|1|823|Hello | 0|299|1|823|World
| +-----+ ^
| | REP | |
| +-----+ |
| | REQ | |
V +-----+ |
0|446|0|299|1|823|Hello | 0|446|0|299|1|823|World
| +-----+ ^
| | REP | |
V +-----+ |
Hello | World
4. Hop-by-hop vs. End-to-end
All endpoints implement so called "hop-by-hop" functionality. It's
the functionality concerned with sending messages to the immediately
adjacent components and receiving messages from them.
In addition to that, the endpoints on the edge of the topology
implement so called "end-to-end" functionality that is concerned with
issues such as, for example, reliability.
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end to end
| |
+-----+ +-----+-----+ +-----+-----+ +-----+
| |-->| | |-->| | |-->| |
| REQ | | REP | REQ | | REP | REQ | | REP |
| |<--| | |<--| | |<--| |
+-----+ +-----+-----+ +-----+-----+ +-----+
| | | | | |
+---------+ +---------+ +---------+
hop by hop hop by hop hop by hop
To make an analogy with the TCP/IP stack, IP provides hop-by-hop
functionality, i.e. routing of the packets to the adjacent node,
while TCP implements end-to-end functionality such resending of lost
As a rule of thumb, raw hop-by-hop endpoints are used to build
devices (intermediary nodes in the topology) while end-to-end
endpoints are used directly by the applications.
To prevent confusion, the specification of the endpoint behaviour
below will discuss hop-by-hop and end end-to-end functionality in
separate chapters.
5. Hop-by-hop functionality
5.1. REQ endpoint
The REQ endpoint is used by the user to send requests to the
processing nodes and receive the replies afterwards.
When user asks REQ endpoint to send a request, the endpoint should
send it to one of the associated outbound channels (TCP connections
or similar). The request sent is exactly the message supplied by the
user. REQ socket MUST NOT modify an outgoing request in any way.
If there's no channel to send the request to, the endpoint won't send
the request and MUST report the backpressure condition to the user.
For example, with BSD socket API, backpressure is reported as EAGAIN
If there are associated channels but none of them is available for
sending, i.e. all of them are already reporting backpressure, the
endpoint won't send the message and MUST report the backpressure
condition to the user.
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Backpressure is used as a means to redirect the requests from the
congested parts of the topology to to the parts that are still
responsive. It can be thought of as a crude scheduling algorithm.
However crude though, it's probably still the best you can get
without knowing estimates of execution time for individual tasks, CPU
capacity of individual processing nodes et c.
Alternatively, backpressure can be thought of as a congestion control
mechanism. When all available processing nodes are busy, it slows
down the client application, i.e. it prevents the user from sending
any more requests.
If the channel is not capable of reporting backpressure (e.g. DCCP)
the endpoint SHOULD consider it as always available for sending new
request. However, such channels should be used with care as when the
congestion hits they may suck in a lot of requests just to discard
them silently and thus cause re-transmission storms later on. The
implementation of the REQ endpoint MAY choose to prohibit the use of
such channels altogether.
When there are multiple channels available for sending the request
endpoint MAY use any prioritisation mechanism to decide which channel
to send the request to. For example, it may use classic priorities
attached to channels and send message to the channel with the highest
priority. That allows for routing algorithms such as: "Use local
processing nodes if any are available. Send the requests to remote
nodes only if there are no local ones available." Alternatively, the
endpoint may implement weighted priorities ("send 20% of the request
to node A and 80% to node B). The endpoint also may not implement
any prioritisation strategy and treat all channels as equal.
Whatever the case, two rules must apply.
First, by default the priority settings for all channels MUST be
equal. Creating a channel with different priority MUST be triggered
by an explicit action by the user.
Second, if there are several channels with equal priority, the
endpoint MUST distribute the messages among them in fair fashion
using round-robin algorithm. The round-robin implementation MUST
also take care not to become unfair when new channels are added or
old ones are removed on the fly.
As for incoming messages, i.e. replies, REQ endpoint MUST fair-queues
them. In other words, if there are replies available on several
channels, it MUST receive them in a round-robin fashion. It mast
also take care not to compromise the fairness when new channels are
added or old ones removed.
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In addition to providing basic fairness, the goal of fair-queueing is
to prevent DoS attacks where a huge stream of fake replies from one
channel would be able to block the real replies coming from different
channels. Fair queueing ensures that messages from every channel are
received at approximately the same rate. That way, DoS attack can
slow down the system but it can't entirely block it.
Incoming replies MUST be handed to the user exactly as they were
received. REQ endpoint MUST not modify the replies in any way.
5.2. REP endpoint
REP endpoint is used to receive requests from the clients and send
replies back to the clients.
First of all, REP socket is responsible for assigning unique 31-bit
channel IDs to the individual associated channels.
First ID assigned MUST be random. Next is computed by adding 1 to
the previous one with potential overflow to 0.
The implementation MUST ensure that the random number is different
each time the endpoint is re-started, the process that contains it is
restarted or similar. So, for example, using pseudo-random generator
with a constant seed won't do.
The goal of the algorithm is to the spread of possible channel ID
values and thus minimise the chance that a reply is routed to an
unrelated channel, even in the face of intermediate node failures.
When receiving a message, REP endpoint MUST fair-queue among the
channels available for receiving. In other words it should round-
robin among such channels and receive one request from a channel at a
time. It MUST also implement the round-robin algorithm is such a way
that adding or removing channels don't break its fairness.
In addition to guaranteeing basic fairness in access to computing
resources the above algorithm makes it impossible for a malevolent or
misbehaving client to completely block the processing of requests
from other clients by issuing steady stream of requests.
After getting hold on the request, the REP socket should prepend it
by 32 bit value, consisting of 1 bit set to 0 followed by the 31-bit
ID of the channel the request was received from. The extended
request will be then handed to the user.
The goal of adding the channel ID to the request is to be able to
route the reply back to the original channel later on. Thus, when
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the user sends a reply, endpoint strips first 32 bits off and uses
the value to determine where it is to be routed.
If the reply is shorter than 32 bits, it is malformed and the
endpoint MUST ignore it. Also, if the most relevant bit of the
32-bit value isn't set to 0, the reply is malformed and MUST be
Otherwise, the endpoint checks whether its table of associated
channels contains the channel with a corresponding ID. If so, it
sends the reply (with first 32 bits stripped off) to that channel.
If the channel is not found, the reply MUST be dropped. If the
channel is not available for sending, i.e. it is applying
backpressure, the reply MUST be dropped.
Note that when the reply is unroutable two things might have
happened. Either there was some kind of network disruption, in which
case the request will be re-sent later on, or the original client
have failed or been shut down. In such case the request won't be
resent, however, it doesn't really matter because there's no one to
deliver the reply to any more anyway.
Also note that unlike with the requests there's no pushback applied
to the replies. The replies are simply dropped in the case of
pushback. The reason for this behaviour is that if the endpoint
blocked and waited for the channel be become available, all the
subsequent replies, possibly destined for different, unblocked
channels would be blocked in the meantime anyway. That would allow
for a DoS attack by simply firing a lot of requests and not receiving
the replies.
6. End-to-end functionality
End-to-end functionality is built on top of hop-to-hop functionality.
Thus, an endpoint on the edge of a topology contains all the hop-by-
hop functionality, but also implements additional functionality of
its own. This end-to-end functionality acts basically as a user of
the underlying hop-by-hop functionality.
6.1. REQ endpoint
End-to-end functionality for REQ sockets is concerned with re-sending
the requests in case of failure and with filtering out stray or
outdated replies.
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To be able to do the latter, the endpoint must tag the requests with
unique 31-bit request IDs. First request ID is picked at random.
All subsequent request IDs are generated by adding 1 to the last
request ID and possibly overflowing to 0.
To improve robustness of the system, the implementation MUST ensure
that the random number is different each time the endpoint, the
process or the machine is restarted. Pseudo-random generator with
fixed seed won't do.
When user asks the endpoint to send a message, the endpoint prepends
a 32-bit value to the message, consisting of a single bit set to 1
followed by a 31-bit request ID and passes it on in a standard hop-
by-hop way.
If the hop-by-hop layer reports pushback condition, the end-to-end
layer considers the request unsent and MUST report pushback condition
to the user.
If the request is successfully sent, the endpoint stores the request
including its request ID, so that it can be resent later on if
needed. At the same time it sets up a timer to trigger the re-
transimission in case the reply is not received within a specified
timeout. The user MUST be allowed to specify the timeout interval.
The default timeout interval must be 60 seconds.
When a reply is received from the underlying hop-by-hop
implementation, the endpoint should strip off first 32 bits from the
reply to check whether it is a valid reply.
If the reply is shorter than 32 bits, it is malformed and the
endpoint MUST ignore it. If the most significant bit of the 32-bit
value is set to 0, the reply is malformed and MUST be ignored.
Otherwise, the endpoint should check whether the request ID in the
reply matches any of the request IDs of the requests being processed
at the moment. If not so, the reply MUST be ignored. It is either a
stray message or a duplicate reply.
Please note that the endpoint can support either one or more requests
being processed in parallel. Which one is the case depends on the
API exposed to the user and is not part of this specification.
If the ID in the reply matches one of the requests in progress, the
reply MUST be passed to the user (with the 32-bit prefix stripped
off). At the same time the stored copy of the original request as
well as re-transmission timer must be deallocated.
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Finally, REQ endpoint MUST make it possible for the user to cancel a
particular request in progress. What it means technically is
deleting the stored copy of the request and cancelling the associated
timer. Thus, once the reply arrives, it will be discarded by the
algorithm above.
The cancellation allows, for example, the user to time out a request.
They can simply post a request and if there's no answer in specific
timeframe, they can cancel it.
6.2. REP endpoint
End-to-end functionality for REP endpoints is concerned with turning
requests into corresponding replies.
When user asks to receive a request, the endpoint gets next request
from the hop-by-hop layer and splits it into the traceback stack and
the message payload itself. The traceback stack is stored and the
payload is returned to the user.
The algorithm for splitting the request is as follows: Strip 32 bit
tags from the message in one-by-one manner. Once the most
significant bit of the tag is set, we've reached the bottom of the
traceback stack and the splitting is done. If the end of the message
is reached without finding the bottom of the stack, the request is
malformed and MUST be ignored.
Note that the payload payload produced by this procedure is the same
as the request payload sent by the original client.
Once the user processes the request and sends the reply, the endpoint
prepends the reply with the stored traceback stack and sends it on
using the hop-by-hop layer. At that point the stored traceback stack
MUST be deallocated.
Additionally, REP endpoint MUST support cancelling any request being
processed at the moment. What it means, technically, is that state
associated with the request, i.e. the traceback stack stored by the
endpoint is deleted and reply to that particular request is never
The most important use of cancellation is allowing the service
instances to ignore malformed requests. If the application-level
part of the request doesn't conform to the application protocol the
service can simply cancel the request. In such case the reply is
never sent. Of course, if application wants to send an application-
specific error massage back to the client it can do so by not
cancelling the request and sending a regular reply.
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7. Loop avoidance
It may happen that a request/reply topology contains a loop. It
becomes increasingly likely as the topology grows out of scope of a
single organisation and there are multiple administrators involved in
maintaining it. Unfortunate interaction between two perfectly
legitimate setups can cause loop to be created.
With no additional guards against the loops, it's likely that
requests will be caugth inside the loop, rotating there forever, each
message gradually growing in size as new prefixes are added to it by
each REP endpoint on the way. Eventually, a loop can cause
congestion and bring the whole system to a halt.
To deal with the problem REQ endpoints MUST check the depth of the
traceback stack for every outgoing request and discard any requests
where it exceeds certain threshold. The threshold should be defined
by the user. The default value is suggested to be 8.
8. IANA Considerations
New SP endpoint types REQ and REP should be registered by IANA. For
now, value of 16 should be used for REQ endpoints and value of 17 for
REP endpoints.
9. Security Considerations
The mapping is not intended to provide any additional security to the
underlying protocol. DoS concerns are addressed within the
10. References
Author's Address
Martin Sustrik (editor)
GoPivotal Inc.
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