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505 lines
23 KiB
HTML
5 years ago
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<HTML>
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<HEAD>
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<TITLE>State Threads for Internet Applications</TITLE>
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</HEAD>
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<BODY BGCOLOR=#FFFFFF>
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<H2>State Threads for Internet Applications</H2>
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<H3>Introduction</H3>
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<P>
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State Threads is an application library which provides a
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foundation for writing fast and highly scalable Internet Applications
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on UNIX-like platforms. It combines the simplicity of the multithreaded
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programming paradigm, in which one thread supports each simultaneous
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connection, with the performance and scalability of an event-driven
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state machine architecture.</P>
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<H3>1. Definitions</H3>
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<P>
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<A NAME="IA">
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<H4>1.1 Internet Applications</H4>
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</A>
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<P>
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An <I>Internet Application</I> (IA) is either a server or client network
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application that accepts connections from clients and may or may not
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connect to servers. In an IA the arrival or departure of network data
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often controls processing (that is, IA is a <I>data-driven</I> application).
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For each connection, an IA does some finite amount of work
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involving data exchange with its peer, where its peer may be either
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a client or a server.
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The typical transaction steps of an IA are to accept a connection,
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read a request, do some finite and predictable amount of work to
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process the request, then write a response to the peer that sent the
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request. One example of an IA is a Web server;
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the most general example of an IA is a proxy server, because it both
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accepts connections from clients and connects to other servers.</P>
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<P>
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We assume that the performance of an IA is constrained by available CPU
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cycles rather than network bandwidth or disk I/O (that is, CPU
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is a bottleneck resource).
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<P>
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<A NAME="PS">
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<H4>1.2 Performance and Scalability</H4>
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</A>
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<P>
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The <I>performance</I> of an IA is usually evaluated as its
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throughput measured in transactions per second or bytes per second (one
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can be converted to the other, given the average transaction size). There are
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several benchmarks that can be used to measure throughput of Web serving
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applications for specific workloads (such as
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<A HREF="http://www.spec.org/osg/web96/">SPECweb96</A>,
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<A HREF="http://www.mindcraft.com/webstone/">WebStone</A>,
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<A HREF="http://www.zdnet.com/zdbop/webbench/">WebBench</A>).
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Although there is no common definition for <I>scalability</I>, in general it
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expresses the ability of an application to sustain its performance when some
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external condition changes. For IAs this external condition is either the
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number of clients (also known as "users," "simultaneous connections," or "load
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generators") or the underlying hardware system size (number of CPUs, memory
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size, and so on). Thus there are two types of scalability: <I>load
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scalability</I> and <I>system scalability</I>, respectively.
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<P>
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The figure below shows how the throughput of an idealized IA changes with
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the increasing number of clients (solid blue line). Initially the throughput
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grows linearly (the slope represents the maximal throughput that one client
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can provide). Within this initial range, the IA is underutilized and CPUs are
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partially idle. Further increase in the number of clients leads to a system
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saturation, and the throughput gradually stops growing as all CPUs become fully
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utilized. After that point, the throughput stays flat because there are no
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more CPU cycles available.
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In the real world, however, each simultaneous connection
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consumes some computational and memory resources, even when idle, and this
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overhead grows with the number of clients. Therefore, the throughput of the
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real world IA starts dropping after some point (dashed blue line in the figure
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below). The rate at which the throughput drops depends, among other things, on
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application design.
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<P>
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We say that an application has a good <I>load scalability</I> if it can
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sustain its throughput over a wide range of loads.
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Interestingly, the <A HREF="http://www.spec.org/osg/web99/">SPECweb99</A>
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benchmark somewhat reflects the Web server's load scalability because it
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measures the number of clients (load generators) given a mandatory minimal
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throughput per client (that is, it measures the server's <I>capacity</I>).
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This is unlike <A HREF="http://www.spec.org/osg/web96/">SPECweb96</A> and
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other benchmarks that use the throughput as their main metric (see the figure
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below).
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<P>
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<CENTER><IMG SRC="fig.gif" ALT="Figure: Throughput vs. Number of clients">
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</CENTER>
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<P>
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<I>System scalability</I> is the ability of an application to sustain its
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performance per hardware unit (such as a CPU) with the increasing number of
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these units. In other words, good system scalability means that doubling the
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number of processors will roughly double the application's throughput (dashed
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green line). We assume here that the underlying operating system also scales
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well. Good system scalability allows you to initially run an application on
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the smallest system possible, while retaining the ability to move that
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application to a larger system if necessary, without excessive effort or
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expense. That is, an application need not be rewritten or even undergo a
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major porting effort when changing system size.
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<P>
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Although scalability and performance are more important in the case of server
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IAs, they should also be considered for some client applications (such as
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benchmark load generators).
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<P>
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<A NAME="CONC">
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<H4>1.3 Concurrency</H4>
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</A>
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<P>
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Concurrency reflects the parallelism in a system. The two unrelated types
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are <I>virtual</I> concurrency and <I>real</I> concurrency.
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<UL>
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<LI>Virtual (or apparent) concurrency is the number of simultaneous
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connections that a system supports.
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<BR><BR>
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<LI>Real concurrency is the number of hardware devices, including
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CPUs, network cards, and disks, that actually allow a system to perform
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tasks in parallel.
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</UL>
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<P>
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An IA must provide virtual concurrency in order to serve many users
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simultaneously.
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To achieve maximum performance and scalability in doing so, the number of
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programming entities than an IA creates to be scheduled by the OS kernel
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should be
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kept close to (within an order of magnitude of) the real concurrency found on
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the system. These programming entities scheduled by the kernel are known as
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<I>kernel execution vehicles</I>. Examples of kernel execution vehicles
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include Solaris lightweight processes and IRIX kernel threads.
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In other words, the number of kernel execution vehicles should be dictated by
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the system size and not by the number of simultaneous connections.
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<P>
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<H3>2. Existing Architectures</H3>
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<P>
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There are a few different architectures that are commonly used by IAs.
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These include the <I>Multi-Process</I>,
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<I>Multi-Threaded</I>, and <I>Event-Driven State Machine</I>
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architectures.
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<P>
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<A NAME="MP">
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<H4>2.1 Multi-Process Architecture</H4>
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</A>
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<P>
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In the Multi-Process (MP) architecture, an individual process is
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dedicated to each simultaneous connection.
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A process performs all of a transaction's initialization steps
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and services a connection completely before moving on to service
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a new connection.
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<P>
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User sessions in IAs are relatively independent; therefore, no
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synchronization between processes handling different connections is
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necessary. Because each process has its own private address space,
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this architecture is very robust. If a process serving one of the connections
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crashes, the other sessions will not be affected. However, to serve many
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concurrent connections, an equal number of processes must be employed.
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Because processes are kernel entities (and are in fact the heaviest ones),
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the number of kernel entities will be at least as large as the number of
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concurrent sessions. On most systems, good performance will not be achieved
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when more than a few hundred processes are created because of the high
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context-switching overhead. In other words, MP applications have poor load
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scalability.
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<P>
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On the other hand, MP applications have very good system scalability, because
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no resources are shared among different processes and there is no
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synchronization overhead.
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<P>
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The Apache Web Server 1.x (<A HREF=#refs1>[Reference 1]</A>) uses the MP
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architecture on UNIX systems.
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<P>
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<A NAME="MT">
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<H4>2.2 Multi-Threaded Architecture</H4>
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</A>
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<P>
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In the Multi-Threaded (MT) architecture, multiple independent threads
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of control are employed within a single shared address space. Like a
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process in the MP architecture, each thread performs all of a
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transaction's initialization steps and services a connection completely
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before moving on to service a new connection.
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<P>
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Many modern UNIX operating systems implement a <I>many-to-few</I> model when
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mapping user-level threads to kernel entities. In this model, an
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arbitrarily large number of user-level threads is multiplexed onto a
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lesser number of kernel execution vehicles. Kernel execution
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vehicles are also known as <I>virtual processors</I>. Whenever a user-level
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thread makes a blocking system call, the kernel execution vehicle it is using
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will become blocked in the kernel. If there are no other non-blocked kernel
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execution vehicles and there are other runnable user-level threads, a new
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kernel execution vehicle will be created automatically. This prevents the
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application from blocking when it can continue to make useful forward
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progress.
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<P>
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Because IAs are by nature network I/O driven, all concurrent sessions block on
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network I/O at various points. As a result, the number of virtual processors
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created in the kernel grows close to the number of user-level threads
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(or simultaneous connections). When this occurs, the many-to-few model
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effectively degenerates to a <I>one-to-one</I> model. Again, like in
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the MP architecture, the number of kernel execution vehicles is dictated by
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the number of simultaneous connections rather than by number of CPUs. This
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reduces an application's load scalability. However, because kernel threads
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(lightweight processes) use fewer resources and are more light-weight than
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traditional UNIX processes, an MT application should scale better with load
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than an MP application.
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<P>
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Unexpectedly, the small number of virtual processors sharing the same address
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space in the MT architecture destroys an application's system scalability
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because of contention among the threads on various locks. Even if an
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application itself is carefully
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optimized to avoid lock contention around its own global data (a non-trivial
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task), there are still standard library functions and system calls
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that use common resources hidden from the application. For example,
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on many platforms thread safety of memory allocation routines
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(<TT>malloc(3)</TT>, <TT>free(3)</TT>, and so on) is achieved by using a single
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global lock. Another example is a per-process file descriptor table.
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This common resource table is shared by all kernel execution vehicles within
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the same process and must be protected when one modifies it via
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certain system calls (such as <TT>open(2)</TT>, <TT>close(2)</TT>, and so on).
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In addition to that, maintaining the caches coherent
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among CPUs on multiprocessor systems hurts performance when different threads
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running on different CPUs modify data items on the same cache line.
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<P>
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In order to improve load scalability, some applications employ a different
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type of MT architecture: they create one or more thread(s) <I>per task</I>
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rather than one thread <I>per connection</I>. For example, one small group
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of threads may be responsible for accepting client connections, another
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for request processing, and yet another for serving responses. The main
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advantage of this architecture is that it eliminates the tight coupling
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between the number of threads and number of simultaneous connections. However,
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in this architecture, different task-specific thread groups must share common
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work queues that must be protected by mutual exclusion locks (a typical
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producer-consumer problem). This adds synchronization overhead that causes an
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application to perform badly on multiprocessor systems. In other words, in
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this architecture, the application's system scalability is sacrificed for the
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sake of load scalability.
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<P>
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Of course, the usual nightmares of threaded programming, including data
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corruption, deadlocks, and race conditions, also make MT architecture (in any
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form) non-simplistic to use.
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<P>
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<A NAME="EDSM">
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<H4>2.3 Event-Driven State Machine Architecture</H4>
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</A>
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<P>
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In the Event-Driven State Machine (EDSM) architecture, a single process
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is employed to concurrently process multiple connections. The basics of this
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architecture are described in Comer and Stevens
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<A HREF=#refs2>[Reference 2]</A>.
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The EDSM architecture performs one basic data-driven step associated with
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a particular connection at a time, thus multiplexing many concurrent
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connections. The process operates as a state machine that receives an event
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and then reacts to it.
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<P>
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In the idle state the EDSM calls <TT>select(2)</TT> or <TT>poll(2)</TT> to
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wait for network I/O events. When a particular file descriptor is ready for
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I/O, the EDSM completes the corresponding basic step (usually by invoking a
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handler function) and starts the next one. This architecture uses
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non-blocking system calls to perform asynchronous network I/O operations.
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For more details on non-blocking I/O see Stevens
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<A HREF=#refs3>[Reference 3]</A>.
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<P>
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To take advantage of hardware parallelism (real concurrency), multiple
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identical processes may be created. This is called Symmetric Multi-Process
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EDSM and is used, for example, in the Zeus Web Server
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(<A HREF=#refs4>[Reference 4]</A>). To more efficiently multiplex disk I/O,
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special "helper" processes may be created. This is called Asymmetric
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Multi-Process EDSM and was proposed for Web servers by Druschel
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and others <A HREF=#refs5>[Reference 5]</A>.
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<P>
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EDSM is probably the most scalable architecture for IAs.
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Because the number of simultaneous connections (virtual concurrency) is
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completely decoupled from the number of kernel execution vehicles (processes),
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this architecture has very good load scalability. It requires only minimal
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user-level resources to create and maintain additional connection.
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<P>
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Like MP applications, Multi-Process EDSM has very good system scalability
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because no resources are shared among different processes and there is no
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synchronization overhead.
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<P>
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Unfortunately, the EDSM architecture is monolithic rather than based on the
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concept of threads, so new applications generally need to be implemented from
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the ground up. In effect, the EDSM architecture simulates threads and their
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stacks the hard way.
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<P>
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<A NAME="ST">
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<H3>3. State Threads Library</H3>
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</A>
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<P>
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The State Threads library combines the advantages of all of the above
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architectures. The interface preserves the programming simplicity of thread
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abstraction, allowing each simultaneous connection to be treated as a separate
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thread of execution within a single process. The underlying implementation is
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close to the EDSM architecture as the state of each particular concurrent
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session is saved in a separate memory segment.
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<P>
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<H4>3.1 State Changes and Scheduling</H4>
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<P>
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The state of each concurrent session includes its stack environment
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(stack pointer, program counter, CPU registers) and its stack. Conceptually,
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a thread context switch can be viewed as a process changing its state. There
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are no kernel entities involved other than processes.
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Unlike other general-purpose threading libraries, the State Threads library
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is fully deterministic. The thread context switch (process state change) can
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only happen in a well-known set of functions (at I/O points or at explicit
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synchronization points). As a result, process-specific global data does not
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have to be protected by mutual exclusion locks in most cases. The entire
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application is free to use all the static variables and non-reentrant library
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functions it wants, greatly simplifying programming and debugging while
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increasing performance. This is somewhat similar to a <I>co-routine</I> model
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(co-operatively multitasked threads), except that no explicit yield is needed
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--
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sooner or later, a thread performs a blocking I/O operation and thus surrenders
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control. All threads of execution (simultaneous connections) have the
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same priority, so scheduling is non-preemptive, like in the EDSM architecture.
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Because IAs are data-driven (processing is limited by the size of network
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buffers and data arrival rates), scheduling is non-time-slicing.
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<P>
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Only two types of external events are handled by the library's
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scheduler, because only these events can be detected by
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<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 <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>
|
||
|
|
<HR>
|
||
|
|
<P>
|
||
|
|
|
||
|
|
<CENTER><FONT SIZE=-1>Portions created by SGI are Copyright © 2000
|
||
|
|
Silicon Graphics, Inc. All rights reserved.</FONT></CENTER>
|
||
|
|
<P>
|
||
|
|
|
||
|
|
</BODY>
|
||
|
|
</HTML>
|
||
|
|
|