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<TITLE>State Threads for Internet Applications</TITLE>
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<BODY BGCOLOR=#FFFFFF>
<H2>State Threads for Internet Applications</H2>
<H3>Introduction</H3>
<P>
State Threads is an application library which provides a
foundation for writing fast and highly scalable Internet Applications
on UNIX-like platforms.  It combines the simplicity of the multithreaded 
programming paradigm, in which one thread supports each simultaneous 
connection, with the performance and scalability of an event-driven 
state machine architecture.</P>

<H3>1. Definitions</H3>
<P>
<A NAME="IA">
<H4>1.1 Internet Applications</H4>
</A>
<P>
An <I>Internet Application</I> (IA) is either a server or client network
application that accepts connections from clients and may or may not 
connect to servers.  In an IA the arrival or departure of network data
often controls processing (that is, IA is a <I>data-driven</I> application).
For each connection, an IA does some finite amount of work 
involving data exchange with its peer, where its peer may be either 
a client or a server.
The typical transaction steps of an IA are to accept a connection,
read a request, do some finite and predictable amount of work to 
process the request, then write a response to the peer that sent the 
request.  One example of an IA is a Web server; 
the most general example of an IA is a proxy server, because it both 
accepts connections from clients and connects to other servers.</P>
<P>
We assume that the performance of an IA is constrained by available CPU
cycles rather than network bandwidth or disk I/O (that is, CPU
is a bottleneck resource).
<P>

<A NAME="PS">
<H4>1.2 Performance and Scalability</H4>
</A>
<P>
The <I>performance</I> of an IA is usually evaluated as its
throughput measured in transactions per second or bytes per second (one
can be converted to the other, given the average transaction size).  There are
several benchmarks that can be used to measure throughput of Web serving
applications for specific workloads (such as 
<A HREF="http://www.spec.org/osg/web96/">SPECweb96</A>,
<A HREF="http://www.mindcraft.com/webstone/">WebStone</A>,
<A HREF="http://www.zdnet.com/zdbop/webbench/">WebBench</A>).
Although there is no common definition for <I>scalability</I>, in general it
expresses the ability of an application to sustain its performance when some
external condition changes.  For IAs this external condition is either the
number of clients (also known as "users," "simultaneous connections," or "load
generators") or the underlying hardware system size (number of CPUs, memory
size, and so on).  Thus there are two types of scalability: <I>load
scalability</I> and <I>system scalability</I>, respectively.
<P>
The figure below shows how the throughput of an idealized IA changes with
the increasing number of clients (solid blue line).  Initially the throughput
grows linearly (the slope represents the maximal throughput that one client
can provide). Within this initial range, the IA is underutilized and CPUs are
partially idle.  Further increase in the number of clients leads to a system
saturation, and the throughput gradually stops growing as all CPUs become fully
utilized.  After that point, the throughput stays flat because there are no
more CPU cycles available.
In the real world, however, each simultaneous connection
consumes some computational and memory resources, even when idle, and this
overhead grows with the number of clients.  Therefore, the throughput of the
real world IA starts dropping after some point (dashed blue line in the figure
below).  The rate at which the throughput drops depends, among other things, on
application design.
<P>
We say that an application has a good <I>load scalability</I> if it can
sustain its throughput over a wide range of loads.
Interestingly, the <A HREF="http://www.spec.org/osg/web99/">SPECweb99</A>
benchmark somewhat reflects the Web server's load scalability because it
measures the number of clients (load generators) given a mandatory minimal
throughput per client (that is, it measures the server's <I>capacity</I>).
This is unlike <A HREF="http://www.spec.org/osg/web96/">SPECweb96</A> and
other benchmarks that use the throughput as their main metric (see the figure
below).
<P>
<CENTER><IMG SRC="fig.gif" ALT="Figure: Throughput vs. Number of clients">
</CENTER>
<P>
<I>System scalability</I> is the ability of an application to sustain its
performance per hardware unit (such as a CPU) with the increasing number of
these units.  In other words, good system scalability means that doubling the
number of processors will roughly double the application's throughput (dashed
green line).  We assume here that the underlying operating system also scales
well.  Good system scalability allows you to initially run an application on 
the smallest system possible, while retaining the ability to move that
application to a larger system if necessary, without excessive effort or
expense.  That is, an application need not be rewritten or even undergo a
major porting effort when changing system size.
<P>
Although scalability and performance are more important in the case of server
IAs, they should also be considered for some client applications (such as 
benchmark load generators).
<P>

<A NAME="CONC">
<H4>1.3 Concurrency</H4>
</A>
<P>
Concurrency reflects the parallelism in a system.  The two unrelated types 
are <I>virtual</I> concurrency and <I>real</I> concurrency.
<UL>
<LI>Virtual (or apparent) concurrency is the number of simultaneous
connections that a system supports.
<BR><BR>
<LI>Real concurrency is the number of hardware devices, including
CPUs, network cards, and disks, that actually allow a system to perform 
tasks in parallel.
</UL>
<P>
An IA must provide virtual concurrency in order to serve many users
simultaneously.
To achieve maximum performance and scalability in doing so, the number of
programming entities than an IA creates to be scheduled by the OS kernel
should be
kept close to (within an order of magnitude of) the real concurrency found on
the system. These programming entities scheduled by the kernel are known as
<I>kernel execution vehicles</I>. Examples of kernel execution vehicles
include Solaris lightweight processes and IRIX kernel threads.
In other words, the number of kernel execution vehicles should be dictated by
the system size and not by the number of simultaneous connections.
<P>

<H3>2. Existing Architectures</H3>
<P>
There are a few different architectures that are commonly used by IAs. 
These include the <I>Multi-Process</I>, 
<I>Multi-Threaded</I>, and <I>Event-Driven State Machine</I> 
architectures.
<P>
<A NAME="MP">
<H4>2.1 Multi-Process Architecture</H4>
</A>
<P>
In the Multi-Process (MP) architecture, an individual process is 
dedicated to each simultaneous connection.
A process performs all of a transaction's initialization steps 
and services a connection completely before moving on to service 
a new connection.
<P>
User sessions in IAs are relatively independent; therefore, no 
synchronization between processes handling different connections is
necessary.  Because each process has its own private address space,
this architecture is very robust. If a process serving one of the connections
crashes, the other sessions will not be affected.  However, to serve many
concurrent connections, an equal number of processes must be employed.
Because processes are kernel entities (and are in fact the heaviest ones), 
the number of kernel entities will be at least as large as the number of 
concurrent sessions. On most systems, good performance will not be achieved 
when more than a few hundred processes are created because of the high 
context-switching overhead. In other words, MP applications have poor load 
scalability.
<P>
On the other hand, MP applications have very good system scalability, because
no resources are shared among different processes and there is no
synchronization overhead.
<P>
The Apache Web Server 1.x (<A HREF=#refs1>[Reference 1]</A>) uses the MP 
architecture on UNIX systems.
<P>
<A NAME="MT">
<H4>2.2 Multi-Threaded Architecture</H4>
</A>
<P>
In the Multi-Threaded (MT) architecture, multiple independent threads 
of control are employed within a single shared address space.  Like a 
process in the MP architecture, each thread performs all of a
transaction's initialization steps and services a connection completely
before moving on to service a new connection.
<P>
Many modern UNIX operating systems implement a <I>many-to-few</I> model when 
mapping user-level threads to kernel entities.  In this model, an 
arbitrarily large number of user-level threads is multiplexed onto a 
lesser number of kernel execution vehicles.  Kernel execution 
vehicles are also known as <I>virtual processors</I>.  Whenever a user-level
thread makes a blocking system call, the kernel execution vehicle it is using
will become blocked in the kernel.  If there are no other non-blocked kernel
execution vehicles and there are other runnable user-level threads, a new
kernel execution vehicle will be created automatically.  This prevents the
application from blocking when it can continue to make useful forward
progress.
<P>
Because IAs are by nature network I/O driven, all concurrent sessions block on
network I/O at various points.  As a result, the number of virtual processors
created in the kernel grows close to the number of user-level threads
(or simultaneous connections).  When this occurs, the many-to-few model
effectively degenerates to a <I>one-to-one</I> model.  Again, like in
the MP architecture, the number of kernel execution vehicles is dictated by
the number of simultaneous connections rather than by number of CPUs.  This
reduces an application's load scalability.  However, because kernel threads
(lightweight processes) use fewer resources and are more light-weight than
traditional UNIX processes, an MT application should scale better with load
than an MP application.
<P>
Unexpectedly, the small number of virtual processors sharing the same address
space in the MT architecture destroys an application's system scalability
because of contention among the threads on various locks.  Even if an
application itself is carefully
optimized to avoid lock contention around its own global data (a non-trivial
task), there are still standard library functions and system calls
that use common resources hidden from the application.  For example,
on many platforms thread safety of memory allocation routines
(<TT>malloc(3)</TT>, <TT>free(3)</TT>, and so on) is achieved by using a single
global lock.  Another example is a per-process file descriptor table.
This common resource table is shared by all kernel execution vehicles within
the same process and must be protected when one modifies it via
certain system calls (such as <TT>open(2)</TT>, <TT>close(2)</TT>, and so on).
In addition to that, maintaining the caches coherent
among CPUs on multiprocessor systems hurts performance when different threads
running on different CPUs modify data items on the same cache line.
<P>
In order to improve load scalability, some applications employ a different
type of MT architecture:  they create one or more thread(s) <I>per task</I>
rather than one thread <I>per connection</I>.  For example, one small group
of threads may be responsible for accepting client connections, another 
for request processing, and yet another for serving responses.  The main
advantage of this architecture is that it eliminates the tight coupling
between the number of threads and number of simultaneous connections. However,
in this architecture, different task-specific thread groups must share common
work queues that must be protected by mutual exclusion locks (a typical
producer-consumer problem).  This adds synchronization overhead that causes an
application to perform badly on multiprocessor systems.  In other words, in
this architecture, the application's system scalability is sacrificed for the
sake of load scalability.
<P>
Of course, the usual nightmares of threaded programming, including data
corruption, deadlocks, and race conditions, also make MT architecture (in any
form) non-simplistic to use.
<P>

<A NAME="EDSM">
<H4>2.3 Event-Driven State Machine Architecture</H4>
</A>
<P>
In the Event-Driven State Machine (EDSM) architecture, a single process
is employed to concurrently process multiple connections. The basics of this
architecture are described in Comer and Stevens
<A HREF=#refs2>[Reference 2]</A>.
The EDSM architecture performs one basic data-driven step associated with
a particular connection at a time, thus multiplexing many concurrent
connections.  The process operates as a state machine that receives an event
and then reacts to it.
<P>
In the idle state the EDSM calls <TT>select(2)</TT> or <TT>poll(2)</TT> to
wait for network I/O events.  When a particular file descriptor is ready for
I/O, the EDSM completes the corresponding basic step (usually by invoking a
handler function) and starts the next one.  This architecture uses
non-blocking system calls to perform asynchronous network I/O operations.
For more details on non-blocking I/O see Stevens
<A HREF=#refs3>[Reference 3]</A>.
<P>
To take advantage of hardware parallelism (real concurrency), multiple
identical processes may be created.  This is called Symmetric Multi-Process
EDSM and is used, for example, in the Zeus Web Server
(<A HREF=#refs4>[Reference 4]</A>).  To more efficiently multiplex disk I/O,
special "helper" processes may be created.  This is called Asymmetric
Multi-Process EDSM and was proposed for Web servers by Druschel
and others <A HREF=#refs5>[Reference 5]</A>.
<P>
EDSM is probably the most scalable architecture for IAs.
Because the number of simultaneous connections (virtual concurrency) is
completely decoupled from the number of kernel execution vehicles (processes),
this architecture has very good load scalability.  It requires only minimal 
user-level resources to create and maintain additional connection.
<P>
Like MP applications, Multi-Process EDSM has very good system scalability
because no resources are shared among different processes and there is no
synchronization overhead.
<P>
Unfortunately, the EDSM architecture is monolithic rather than based on the
concept of threads, so new applications generally need to be implemented from
the ground up.  In effect, the EDSM architecture simulates threads and their
stacks the hard way.
<P>

<A NAME="ST">
<H3>3. State Threads Library</H3>
</A>
<P>
The State Threads library combines the advantages of all of the above
architectures.  The interface preserves the programming simplicity of thread
abstraction, allowing each simultaneous connection to be treated as a separate
thread of execution within a single process. The underlying implementation is
close to the EDSM architecture as the state of each particular concurrent
session is saved in a separate memory segment.
<P>

<H4>3.1 State Changes and Scheduling</H4>
<P>
The state of each concurrent session includes its stack environment 
(stack pointer, program counter, CPU registers) and its stack.  Conceptually, 
a thread context switch can be viewed as a process changing its state.  There 
are no kernel entities involved other than processes.  
Unlike other general-purpose threading libraries, the State Threads library
is fully deterministic.  The thread context switch (process state change) can
only happen in a well-known set of functions (at I/O points or at explicit
synchronization points).  As a result, process-specific global data does not
have to be protected by mutual exclusion locks in most cases.  The entire
application is free to use all the static variables and non-reentrant library
functions it wants, greatly simplifying programming and debugging while
increasing performance.  This is somewhat similar to a <I>co-routine</I> model
(co-operatively multitasked threads), except that no explicit yield is needed
--
sooner or later, a thread performs a blocking I/O operation and thus surrenders
control.  All threads of execution (simultaneous connections) have the
same priority, so scheduling is non-preemptive, like in the EDSM architecture.
Because IAs are data-driven (processing is limited by the size of network 
buffers and data arrival rates), scheduling is non-time-slicing.
<P>
Only two types of external events are handled by the library's
scheduler, because only these events can be detected by
<TT>select(2)</TT> or <TT>poll(2)</TT>: I/O events (a file descriptor is ready
for I/O) and time events
(some timeout has expired).  However, other types of events (such as
a signal sent to a process) can also be handled by converting them to I/O
events.  For example, a signal handling function can perform a write to a pipe
(<TT>write(2)</TT> is reentrant/asynchronous-safe), thus converting a signal
event to an I/O event.
<P>
To take advantage of hardware parallelism, as in the EDSM architecture,
multiple processes can be created in either a symmetric or asymmetric manner.
Process management is not in the library's scope but instead is left up to the
application.
<P>
There are several general-purpose threading libraries that implement a
<I>many-to-one</I> model (many user-level threads to one kernel execution
vehicle), using the same basic techniques as the State Threads library 
(non-blocking I/O, event-driven scheduler, and so on).  For an example, see GNU
Portable Threads (<A HREF=#refs6>[Reference 6]</A>).  Because they are
general-purpose, these libraries have different objectives than the State 
Threads library.  The State Threads library is <I>not</I> a general-purpose
threading library,
but rather an application library that targets only certain types of
applications (IAs) in order to achieve the highest possible performance and
scalability for those applications.
<P>

<H4>3.2 Scalability</H4>
<P>
State threads are very lightweight user-level entities, and therefore creating
and maintaining user connections requires minimal resources.  An application
using the State Threads library scales very well with the increasing number
of connections.
<P>
On multiprocessor systems an application should create multiple processes
to take advantage of hardware parallelism.  Using multiple separate processes
is the <I>only</I> way to achieve the highest possible system scalability.
This is because duplicating per-process resources is the only way to avoid
significant synchronization overhead on multiprocessor systems.  Creating
separate UNIX processes naturally offers resource duplication.  Again,
as in the EDSM architecture, there is no connection between the number of
simultaneous connections (which may be very large and changes within a wide
range) and the number of kernel entities (which is usually small and constant).
In other words, the State Threads library makes it possible to multiplex a
large number of simultaneous connections onto a much smaller number of
separate processes, thus allowing an application to scale well with both
the load and system size.
<P>

<H4>3.3 Performance</H4>
<P>
Performance is one of the library's main objectives.  The State Threads
library is implemented to minimize the number of system calls and 
to make thread creation and context switching as fast as possible.
For example, per-thread signal mask does not exist (unlike
POSIX threads), so there is no need to save and restore a process's
signal mask on every thread context switch. This eliminates two system
calls per context switch.  Signal events can be handled much more
efficiently by converting them to I/O events (see above).
<P>

<H4>3.4 Portability</H4>
<P>
The library uses the same general, underlying concepts as the EDSM 
architecture, including non-blocking I/O, file descriptors, and 
I/O multiplexing.  These concepts are available in some form on most 
UNIX platforms, making the library very portable across many 
flavors of UNIX.  There are only a few platform-dependent sections in the
source.
<P>

<H4>3.5 State Threads and NSPR</H4>
<P>
The State Threads library is a derivative of the Netscape Portable 
Runtime library (NSPR) <A HREF=#refs7>[Reference 7]</A>. The primary goal of 
NSPR is to provide a platform-independent layer for system facilities, 
where system facilities include threads, thread synchronization, and I/O.
Performance and scalability are not the main concern of NSPR.  The 
State Threads library addresses performance and scalability while 
remaining much smaller than NSPR.  It is contained in 8 source files 
as opposed to more than 400, but provides all the functionality that 
is needed to write efficient IAs on UNIX-like platforms.
<P>

<TABLE CELLPADDING=3>
<TR>
<TD></TD>
<TH>NSPR</TH>
<TH>State Threads</TH>
</TR>
<TR>
<TD><B>Lines of code</B></TD>
<TD ALIGN=RIGHT>~150,000</TD>
<TD ALIGN=RIGHT>~3000</TD>
</TR>
<TR>
<TD><B>Dynamic library size&nbsp;&nbsp;<BR>(debug version)</B></TD>
<TD></TD>
<TD></TD>
</TR>
<TR>
<TD>IRIX</TD>
<TD ALIGN=RIGHT>~700 KB</TD>
<TD ALIGN=RIGHT>~60 KB</TD>
</TR>
<TR>
<TD>Linux</TD>
<TD ALIGN=RIGHT>~900 KB</TD>
<TD ALIGN=RIGHT>~70 KB</TD>
</TR>
</TABLE>
<P>

<H3>Conclusion</H3>
<P>
State Threads is an application library which provides a foundation for
writing <A HREF=#IA>Internet Applications</A>.  To summarize, it has the
following <I>advantages</I>:
<P>
<UL>
<LI>It allows the design of fast and highly scalable applications.  An
application will scale well with both load and number of CPUs.
<P>
<LI>It greatly simplifies application programming and debugging because, as a
rule, no mutual exclusion locking is necessary and the entire application is
free to use static variables and non-reentrant library functions.
</UL>
<P>
The library's main <I>limitation</I>:
<P>
<UL>
<LI>All I/O operations on sockets must use the State Thread library's I/O
functions because only those functions perform thread scheduling and prevent
the application's processes from blocking.
</UL>
<P>

<H3>References</H3>
<OL>
<A NAME="refs1">
<LI> Apache Software Foundation,
<A HREF="http://www.apache.org">http://www.apache.org</A>.
<A NAME="refs2">
<LI> Douglas E. Comer, David L. Stevens, <I>Internetworking With TCP/IP,
Vol. III: Client-Server Programming And Applications</I>, Second Edition,
Ch. 8, 12.
<A NAME="refs3">
<LI> W. Richard Stevens, <I>UNIX Network Programming</I>, Second Edition,
Vol. 1, Ch. 15.
<A NAME="refs4">
<LI> Zeus Technology Limited,
<A HREF="http://www.zeus.co.uk/">http://www.zeus.co.uk</A>.
<A NAME="refs5">
<LI> Peter Druschel, Vivek S. Pai, Willy Zwaenepoel,
<A HREF="http://www.cs.rice.edu/~druschel/usenix99flash.ps.gz">
Flash: An Efficient and Portable Web Server</A>. In <I>Proceedings of the
USENIX 1999 Annual Technical Conference</I>, Monterey, CA, June 1999.
<A NAME="refs6">
<LI> GNU Portable Threads,
<A HREF="http://www.gnu.org/software/pth/">http://www.gnu.org/software/pth/</A>.
<A NAME="refs7">
<LI> Netscape Portable Runtime,
<A HREF="http://www.mozilla.org/docs/refList/refNSPR/">http://www.mozilla.org/docs/refList/refNSPR/</A>.
</OL>

<H3>Other resources covering various architectural issues in IAs</H3>
<OL START=8>
<LI> Dan Kegel, <I>The C10K problem</I>,
<A HREF="http://www.kegel.com/c10k.html">http://www.kegel.com/c10k.html</A>.
</LI>
<LI> James C. Hu, Douglas C. Schmidt, Irfan Pyarali, <I>JAWS: Understanding
High Performance Web Systems</I>,
<A HREF="http://www.cs.wustl.edu/~jxh/research/research.html">http://www.cs.wustl.edu/~jxh/research/research.html</A>.</LI>
</OL>
<P>
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