Servers: Concurrency and
Performance
Jeff Chase
Duke University
CPS 110 Fall 08
• These slides are included for completeness.
• I used some of them to illustrate how servers are
structured and how they use the OS system call
interface (e.g., *nix), threads, and sockets.
• The server bottleneck material (e.g., Little’s Law) was
also discussed along with scheduling earlier in the
semester. It won’t be tested, but you should
understand how/why response time grows so rapidly
as systems saturate, and the idea of “thrashing” or
“congestion collapse” that often causes throughput to
drop at saturation.
• Server structuring alternatives (event structures,
etc.) will not be tested.
HTTP Server
• HTTP Server
– Creates a socket (socket)
– Binds to an address
– Listens to setup accept backlog
– Can call accept to block waiting for connections
– (Can call select to check for data on multiple socks)
• Handle request
– GET /index.html HTTP/1.0\n
<optional body, multiple lines>\n
\n
Inside your server
Server application
(Apache,
Tomcat/Java, etc)
accept
queue
packet
queues
listen
queue
Measures
offered load
response time
throughput
utilization
Example: Video On Demand
Client() {
fd = connect(“server”);
write (fd, “video.mpg”);
while (!eof(fd))
{
read (fd, buf);
display (buf);
}
}
Server() {
while (1) {
cfd = accept();
read (cfd, name);
fd = open (name);
while (!eof(fd)) {
read(fd, block);
write (cfd, block);
}
close (cfd); close (fd);
}
How many clients can the server support?
Suppose, say, 200 kb/s video on a 100 Mb/s network link?
[MIT/Morris]
Performance “analysis”
• Server capacity:
– Network (100 Mbit/s)
– Disk (20 Mbyte/s)
• Obtained performance: one client stream
• Server is limited by software structure
• If a video is 200 Kbit/s, server should be able to
support more than one client.
500?
[MIT/Morris]
WebServer Flow
Create ServerSocket
TCP socket space
connSocket = accept()
read request from
connSocket
128.36.232.5
128.36.230.2
state: listening
address: {*.6789, *.*}
completed connection queue:
sendbuf:
recvbuf:
state: established
address: {128.36.232.5:6789, 198.69.10.10.1500}
sendbuf:
recvbuf:
read
local file
write file to
connSocket
close connSocket
state: listening
address: {*.25, *.*}
completed connection queue:
sendbuf:
recvbuf:
Discussion: what does step do and how long
does it take?
Web Server Processing Steps
Accept Client
Connection
may block
waiting on
network
Read HTTP
Request Header
Find
File
may block
waiting on
disk I/O
Send HTTP
Response Header
Read File
Send Data
Want to be able to process requests concurrently.
Process States and Transitions
running
(user)
interrupt,
exception
trap/return
Yield
running
Sleep
blocked
(kernel)
Wakeup
Run
ready
Server Blocking
• accept() when no connect requests are waiting on the
listen queue
– What if server has multiple ports to listen from?
• E.g., 80 for HTTP, 443 for HTTPS
• open/read/write on server files
• read() on a socket, if the client is sending too slowly
• write() on socket, if the client is receiving too slowly
– Yup, TCP has flow control like pipes
What if the server blocks while serving one client, and
another client has work to do?
Under the Hood
start (arrival rate λ)
CPU
I/O completion
I/O request
exit
I/O device
(throughput λ until some
center saturates)
Concurrency and Pipelining
CPU
DISK
Before
NET
CPU
DISK
NET
After
Better single-server
performance
• Goal: run at server’s hardware speed
– Disk or network should be bottleneck
• Method:
– Pipeline blocks of each request
– Multiplex requests from multiple clients
• Two implementation approaches:
– Multithreaded server
– Asynchronous I/O
[MIT/Morris]
Concurrent threads or processes
• Using multiple threads/processes
– so that only the flow
processing a particular
request is blocked
– Java: extends Thread or
implements Runnable interface
Example: a Multi-threaded WebServer, which creates a thread for each request
Multiple Process Architecture
Process 1
Accept
Conn
Read
Request
Find
File
Send
Header
Read File
Send Data
…
separate address spaces
Process N
Accept
Conn
•
•
Read
Request
Find
File
Send
Header
Read File
Send Data
Advantages
– Simple programming while addressing blocking issue
Disadvantages
– Many processes; large context switch overheads
– Consumes much memory
– Optimizations involving sharing information among processes
(e.g., caching) harder
Using Threads
Thread 1
Accept
Conn
Read
Request
Find
File
Read File
Send Data
Send
Header
Read File
Send Data
…
Send
Header
Thread N
Accept
Conn
•
•
Read
Request
Find
File
Advantages
– Lower context switch overheads
– Shared address space simplifies optimizations (e.g., caches)
Disadvantages
– Need kernel level threads (why?)
– Some extra memory needed to support multiple stacks
– Need thread-safe programs, synchronization
Threads
• A thread is a schedulable stream of control.
• defined by CPU register values (PC, SP)
• suspend: save register values in
memory
• resume: restore registers from
memory
• Multiple threads can execute independently:
• They can run in parallel on multiple
CPUs...
– - physical concurrency
• …or arbitrarily interleaved on a single
CPU.
– - logical concurrency
• Each thread must have its own stack.
Multithreaded server
server() {
while (1) {
cfd = accept();
read (cfd, name);
fd = open (name);
while (!eof(fd)) {
read(fd, block);
write (cfd, block);
}
close (cfd); close (fd);
}}
for (i = 0; i < 10; i++)
threadfork (server);
• When waiting for I/O,
thread scheduler runs
another thread
• What about references to
shared data?
• Synchronization
[MIT/Morris]
Event-Driven Programming
• One execution stream: no CPU
concurrency.
• Register interest in events
(callbacks).
• Event loop waits for events,
invokes handlers.
• No preemption of event
handlers.
• Handlers generally shortlived.
Event
Loop
Event Handlers
[Ousterhout 1995]
Single Process Event Driven (SPED)
Accept
Conn
Read
Request
Find
File
Event Dispatcher
• Single threaded
• Asynchronous (non-blocking) I/O
• Advantages
– Single address space
– No synchronization
• Disadvantages
– In practice, disk reads still block
Send
Header
Read File
Send Data
Asynchronous Multi-Process Event Driven (AMPED)
Accept
Conn
Read
Request
Find
File
Send
Header
Read File
Send Data
Event Dispatcher
Helper 1
•
•
•
•
Helper 1
Like SPED, but use helper processes/thread for disk I/O
Use IPC to communicate with helper process
Advantages
– Shared address space for most web server functions
– Concurrency for disk I/O
Disadvantages
– IPC between main thread and helper threads
This hybrid model is used by the “Flash” web server.
Helper 1
Event-Based Concurrent
Servers Using I/O Multiplexing
• Maintain a pool of connected descriptors.
• Repeat the following forever:
– Use the Unix select function to block until:
• (a) New connection request arrives on the listening
descriptor.
• (b) New data arrives on an existing connected descriptor.
– If (a), add the new connection to the pool of connections.
– If (b), read any available data from the connection
• Close connection on EOF and remove it from the pool.
[CMU 15-213]
Select
• If a server has many open sockets, how does it know
when one of them is ready for I/O?
int select(int n, fd_set *readfds, fd_set *writefds, fd_set *exceptfds,
struct timeval *timeout);
• Issues with scalability: alternative event interfaces
have been offered.
Asynchronous I/O
struct callback {
bool (*is_ready)();
void (*cb)(arg);
void *arg;
}
main() {
while (1) {
for (c = each callback) {
if (c->is_ready())
c->handler(c->arg);
}
}
}
• Code is structured as a
collection of handlers
• Handlers are nonblocking
• Create new handlers for
blocking operations
• When operation
completes, call handler
[MIT/Morris]
Asychronous server
init() {
on_accept(accept_cb);
}
accept_cb() {
on_readable(cfd,name_cb);
}
on_readable(fd, fn) {
c = new
callback(test_readable, fn, fd);
add c to callback list;
}
name_cb(cfd) {
read(cfd,name);
fd = open(name);
on_readable(fd, read_cb);
}
read_cb(cfd, fd) {
read(fd, block);
on_writeeable(fd, write_cb);
}
write_cb(cfd, fd) {
write(cfd, block);
on_readable(fd, read_cb);
}
[MIT/Morris]
Multithreaded vs. Async
•
•
•
•
•
Hard to program
– Locking code
– Need to know what blocks
Coordination explicit
State stored on thread’s stack
– Memory allocation implicit
Context switch may be
expensive
Multiprocessors
•
•
•
•
•
Hard to program
– Callback code
– Need to know what blocks
Coordination implicit
State passed around explicitly
– Memory allocation explicit
Lightweight context switch
Uniprocessors
[MIT/Morris]
Coordination example
• Threaded server:
– Thread for network
interface
– Interrupt wakes up
network thread
– Protected (locks and
conditional variables)
shared buffer shared
between server threads
and network thread
• Asynchronous I/O
– Poll for packets
• How often to poll?
– Or, interrupt generates
an event
• Be careful: disable
interrupts when
manipulating callback
queue.
[MIT/Morris]
One View
Threads!
Should You Abandon Threads?
• No: important for high-end servers (e.g.
databases).
• But, avoid threads wherever possible:
Event-Driven Handlers
– Use events, not threads, for GUIs,
distributed systems, low-end servers.
– Only use threads where true CPU
Threaded Kernel
concurrency is needed.
– Where threads needed, isolate usage
in threaded application kernel: keep
most of code single-threaded.
[Ousterhout 1995]
Another view
• Events obscure control flow
– For programmers and tools
Threads
thread_main(int sock) {
struct session s;
accept_conn(sock, &s);
read_request(&s);
pin_cache(&s);
write_response(&s);
unpin(&s);
}
pin_cache(struct session *s) {
pin(&s);
if( !in_cache(&s) )
read_file(&s);
}
Events
AcceptHandler(event e) {
struct session *s = new_session(e);
RequestHandler.enqueue(s);
}
RequestHandler(struct session *s) {
…; CacheHandler.enqueue(s);
}
CacheHandler(struct session *s) {
pin(s);
if( !in_cache(s) ) ReadFileHandler.enqueue(s);
else
ResponseHandler.enqueue(s);
}
...
ExitHandlerr(struct session *s) {
…; unpin(&s); free_session(s); }
Web Server
Accept
Conn.
Read
Request
Pin
Cache
Read
File
Write
Response
Exit
[von Behren]
Control Flow
• Events obscure control flow
– For programmers and tools
Threads
thread_main(int sock) {
struct session s;
accept_conn(sock, &s);
read_request(&s);
pin_cache(&s);
write_response(&s);
unpin(&s);
}
pin_cache(struct session *s) {
pin(&s);
if( !in_cache(&s) )
read_file(&s);
}
Events
CacheHandler(struct session *s) {
pin(s);
if( !in_cache(s) ) ReadFileHandler.enqueue(s);
else
ResponseHandler.enqueue(s);
}
RequestHandler(struct session *s) {
…; CacheHandler.enqueue(s);
}
...
ExitHandlerr(struct session *s) {
…; unpin(&s); free_session(s);
}
AcceptHandler(event e) {
struct session *s = new_session(e);
RequestHandler.enqueue(s); }
Web Server
Accept
Conn.
Read
Request
Pin
Cache
Write
Response
Exit
[von Behren]
Read
File
Exceptions
• Exceptions complicate control flow
– Harder to understand program flow
– Cause bugs in cleanup code
Threads
thread_main(int sock) {
struct session s;
accept_conn(sock, &s);
if( !read_request(&s) )
return;
pin_cache(&s);
write_response(&s);
unpin(&s);
}
pin_cache(struct session *s) {
pin(&s);
if( !in_cache(&s) )
read_file(&s);
}
Events
CacheHandler(struct session *s) {
pin(s);
if( !in_cache(s) ) ReadFileHandler.enqueue(s);
else
ResponseHandler.enqueue(s);
}
RequestHandler(struct session *s) {
…; if( error ) return; CacheHandler.enqueue(s);
}
...
ExitHandlerr(struct session *s) {
…; unpin(&s); free_session(s);
}
AcceptHandler(event e) {
struct session *s = new_session(e);
RequestHandler.enqueue(s); }
Web Server
Accept
Conn.
Read
Request
Pin
Cache
Write
Response
Exit
[von Behren]
Read
File
State Management
• Events require manual state management
• Hard to know when to free
– Use GC or risk bugs
Threads
thread_main(int sock) {
struct session s;
accept_conn(sock, &s);
if( !read_request(&s) )
return;
pin_cache(&s);
write_response(&s);
unpin(&s);
}
pin_cache(struct session *s) {
pin(&s);
if( !in_cache(&s) )
read_file(&s);
}
Events
CacheHandler(struct session *s) {
pin(s);
if( !in_cache(s) ) ReadFileHandler.enqueue(s);
else
ResponseHandler.enqueue(s);
}
RequestHandler(struct session *s) {
…; if( error ) return; CacheHandler.enqueue(s);
}
...
ExitHandlerr(struct session *s) {
…; unpin(&s); free_session(s);
}
AcceptHandler(event e) {
struct session *s = new_session(e);
RequestHandler.enqueue(s); }
Web Server
Accept
Conn.
Read
Request
Pin
Cache
Write
Response
Exit
[von Behren]
Read
File
Thread 1
Accept
Conn
Read
Request
Find
File
Read File
Send Data
Send
Header
Read File
Send Data
…
Send
Header
Thread N
Accept
Conn
Read
Request
Find
File
Internet Growth and Scale
The Internet
How to handle all those
client requests raining on
your server?
Servers Under Stress
Performance
Ideal
Peak: some
resource at max
Overload: some
resource thrashing
Load (concurrent requests, or arrival rate)
[Von Behren]
Components
• Wire time +
• Queuing time +
• Service demand +
• Wire time (response)
Depends on
• Cost/length of request
• Load conditions at server
latency
Response Time
offered load
Queuing Theory for Busy People
wait here
offered load
request stream @
arrival rate λ
Process for mean
service demand D
M/M/1 Service Center
• Big Assumptions
– Queue is First-Come-First-Served (FIFO, FCFS).
– Request arrivals are independent (poisson arrivals).
– Requests have independent service demands.
– i.e., arrival interval and service demand are
exponentially distributed (noted as “M”).
Utilization
• What is the probability that the center is busy?
– Answer: some number between 0 and 1.
• What percentage of the time is the center busy?
– Answer: some number between 0 and 100
• These are interchangeable: called utilization U
• If the center is not saturated, i.e., it completes all its
requests in some bounded time, then:
• U = λD = (arrivals/T * service demand)
• “Utilization Law”
• The probability that the service center is idle is 1-U.
Little’s Law
• For an unsaturated queue in steady state, mean
response time R and mean queue length N are
governed by:
Little’s Law: N = λR
• Suppose a task T is in the system for R time units.
• During that time:
– λR new tasks arrive.
– N tasks depart (all tasks ahead of T).
• But in steady state, the flow in balances flow out.
– Note: this means that throughput X = λ.
Inverse Idle Time “Law”
Service center saturates as 1/ λ
approaches D: small increases in
λ cause large increases in the
expected response time R.
R
U
1(100%)
Little’s Law gives response time R = D/(1 - U).
Intuitively, each task T’s response time R = D + DN.
Substituting λR for N: R = D + D λR
Substituting U for λD: R = D + UR
R - UR = D --> R(1 - U) = D --> R = D/(1 - U)
Why Little’s Law Is Important
1. Intuitive understanding of FCFS queue behavior.
• Compute response time from demand parameters (λ, D).
• Compute N: how much storage is needed for the queue.
2. Notion of a saturated service center.
– Response times rise rapidly with load and are unbounded.
• At 50% utilization, a 10% increase in load increases R by 10%.
• At 90% utilization, a 10% increase in load increases R by 10x.
3. Basis for predicting performance of queuing networks.
• Cheap and easy “back of napkin” estimates of system
performance based on observed behavior and proposed
changes, e.g., capacity planning, “what if” questions.
What does this tell us about
server behavior at saturation?
Under the Hood
start (arrival rate λ)
CPU
I/O completion
I/O request
exit
I/O device
(throughput λ until some
center saturates)
Common Bottlenecks
•
•
•
•
•
No more File Descriptors
Sockets stuck in TIME_WAIT
High Memory Use (swapping)
CPU Overload
Interrupt (IRQ) Overload
[Aaron Bannert]
Scaling Server Sites: Clustering
Goals
server load balancing
failure detection
access control filtering
priorities/QoS
request locality
transparent caching
L4: TCP
L7: HTTP
SSL
etc.
Clients
virtual IP
addresses
(VIPs)
smart
switch
server array
What to switch/filter on?
L3 source IP and/or VIP
L4 (TCP) ports etc.
L7 URLs and/or cookies
L7 SSL session IDs
Scaling Services: Replication
Site A
Distribute service load across
multiple sites.
Site B
?
Internet
How to select a server site for each
client or request?
Is it scalable?
Client
Extra Slides
(Any new information on the following
slides will not be tested.)
Event-Based Concurrent
Servers Using I/O Multiplexing
• Maintain a pool of connected descriptors.
• Repeat the following forever:
– Use the Unix select function to block until:
• (a) New connection request arrives on the listening
descriptor.
• (b) New data arrives on an existing connected descriptor.
– If (a), add the new connection to the pool of connections.
– If (b), read any available data from the connection
• Close connection on EOF and remove it from the pool.
[CMU 15-213]
Problems of Multi-Thread Server
• High resource usage, context switch overhead, contended
locks
• Too many threads  throughput meltdown, response time
explosion
• Solution: bound total number of threads
Event-Driven Programming
•
•
•
•
Event-driven programming, also called asynchronous i/o
Using Finite State Machines (FSM) to monitor the progress of requests
Yields efficient and scalable concurrency
Many examples: Click router, Flash web server, TP Monitors, etc.
•
Java: asynchronous i/o
– for an example see: http://www.cafeaulait.org/books/jnp3/examples/12/
Traditional Processes
•
•
•
•
Expensive and “heavyweight”
One system call per process
Fork overhead
Coordination
Events
•
•
•
•
•
•
•
•
Need async I/O
Need select
Wasn’t originally available
Not standardized
Immature
But efficient
Code is distributed all through the program
Harder to debug and understand
Threads
•
•
•
•
•
Separate interface and implementation
Pthreads interface
Implementation is user-level or kernel (native)
If user-level, needs async I/O
But hide the abstraction behind the thread interface
Reference
The State of the Art in Locally Distributed Webserver Systems
Valeria Cardellini, Emiliano Casalicchio, Michele Colajanni and Philip S.
Yu