Servers: Concurrency and Performance Jeff Chase Duke University

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 \n \n

Inside your server

Server application (Apache, Tomcat/Java, etc) Measures offered load response time throughput utilization accept queue packet queues listen queue

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 connSocket = accept() TCP socket space 128.36.232.5

128.36.230.2

state: listening address: {*.6789, *.*} completed connection queue: sendbuf: recvbuf: read request from connSocket state: established address: {128.36.232.5:6789, 198.69.10.10.1500} sendbuf: recvbuf: read local file write file to connSocket state: listening address: {*.25, *.*} completed connection queue: sendbuf: recvbuf: close connSocket Discussion: what does each step do and how long does it take?

Web Server Processing Steps

may block waiting on network Accept Client Connection Read HTTP Request Header Find File Send HTTP Response Header Read File Send Data may block waiting on disk I/O Want to be able to process requests concurrently.

Process States and Transitions

running (user)

interrupt, exception trap/return Yield Sleep

running (kernel)

Run

blocked ready

Wakeup

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 ) I/O completion CPU I/O device I/O request exit (throughput λ until some center saturates)

Concurrency and Pipelining

CPU DISK NET CPU DISK NET Before 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

Thread 1 Accept Conn

Using Threads

Read Request Find File Send Header Read File Send Data Thread N Accept Conn Read Request Find File Send Header Read File Send Data • 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

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 short lived.

Event Loop Event Handlers [Ousterhout 1995]

Single Process Event Driven (SPED)

Accept Conn Read Request Find File Send Header Read File Send Data Event Dispatcher • Single threaded • Asynchronous (non-blocking) I/O • Advantages – Single address space – No synchronization • Disadvantages – In practice, disk reads still block

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 descriptor.

select f unction to block until: • (a) New connection request arrives on the listening • (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.

} struct callback { bool (*is_ready)(); void (*cb)(arg); void *arg;

Asynchronous I/O

} 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: – Use events, not threads, for GUIs, distributed systems, low-end servers.

– Only use threads where true CPU concurrency is needed.

– Where threads needed, isolate usage in threaded application kernel: keep most of code single-threaded.

Event-Driven Handlers Threaded Kernel [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 Write Response Read File 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 Web Server Accept Conn.

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); } 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 Send Header Read File Send Data Thread N Accept Conn Read Request Find File Send Header Read File Send Data

Internet Growth and Scale

The Internet

How to handle all those client requests raining on your server?

Servers Under Stress

Peak: some resource at max Ideal Overload: some resource thrashing Load (concurrent requests, or arrival rate) [Von Behren]

Response Time

Components • Wire time + • Queuing time + • Service demand + • Wire time (response) Depends on • Cost/length of request • Load conditions at server offered load

Queuing Theory for Busy People

offered load request stream @

arrival rate

λ

wait here

M/M/1 Service Center Process for mean service demand

D

• 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: – λ

R

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 .

R

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

.

U

1(100%) Little’s Law gives response time

R = D/(1 - U).

Intuitively, each task

T

’s response time

R

Substituting λ

R

for

N

:

R

=

D

+

D

λ

R

Substituting

U

for λD:

R

=

D

+

UR R

-

UR

=

D

-->

R

(1 -

U

) =

D

-->

R

= =

D

/(1 -

D U

) +

DN

.

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 ) I/O completion CPU I/O device I/O request exit (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

Clients

L4: TCP L7: HTTP SSL etc.

virtual IP addresses (VIPs)

smart switch server array

Goals server load balancing failure detection access control filtering priorities/QoS request locality transparent caching 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 Site B

Distribute service load across multiple sites.

How to select a server site for each client or request?

Is it scalable?

?

Internet 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 descriptor.

select f unction to block until: • (a) New connection request arrives on the listening • (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 Web server Systems

Valeria Cardellini, Emiliano Casalicchio, Michele Colajanni and Philip S. Yu