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  • 1. Web Servers: Implementation and Performance Erich Nahum IBM T.J. Watson Research Center www.research.ibm.com/people/n/nahum [email_address]
  • 2. Contents and Timeline:
    • Introduction to the Web (30 min):
      • HTTP, Clients, Servers, Proxies, DNS, CDN’s
    • Outline of a Web Server Transaction (25 min):
      • Receiving a request, generating a response
    • Web Server Architectural Models (20 min):
      • Processes, threads, events
    • Web Server Workload Characteristics (30 min):
      • File sizes, document popularity, embedded objects
    • Web Server Workload Generation (20 min):
      • Webstone, SpecWeb, TPC-W
  • 3. Things Not Covered in Tutorial
    • Client-side issues: HTML rendering, Javascript interpretation
    • TCP issues: implementation, interaction with HTTP
    • Proxies: some similarities, many differences
    • Dynamic Content: CGI, PHP, EJB, ASP, etc.
    • QoS for Web Servers
    • SSL/TLS and HTTPS
    • Content Distribution Networks (CDN’s)
    • Security and Denial of Service
  • 4. Assumptions and Expectations
    • Some familiarity with WWW as a user
      • (Has anyone here not used a browser?)
    • Some familiarity with networking concepts
      • (e.g., unreliability, reordering, race conditions)
    • Familiarity with systems programming
      • (e.g., know what sockets, hashing, caching are)
    • Examples will be based on C & Unix
      • taken from BSD, Linux, AIX, and real servers
      • (sorry, Java and Windows fans)
  • 5. Objectives and Takeaways
    • Basics of the Web, HTTP, clients, servers, DNS
    • Basics of server implementation & performance
    • Pros and cons of various server architectures
    • Characteristics of web server workloads
    • Difficulties in workload generation
    • Design loop of implement, measure, debug, and fix
    After this tutorial, hopefully we will all know: Many lessons should be applicable to any networked server, e.g., files, mail, news, DNS, LDAP, etc.
  • 6. Acknowledgements Many people contributed comments and suggestions to this tutorial, including: Abhishek Chandra Mark Crovella Suresh Chari Peter Druschel Jim Kurose Balachander Krishnamurthy Vivek Pai Jennifer Rexford Anees Shaikh Srinivasan Seshan Errors are all mine, of course.
  • 7. Chapter 1: Introduction to the World-Wide Web (WWW)
  • 8. Introduction to the WWW
    • HTTP: Hypertext Transfer Protocol
      • Communication protocol between clients and servers
      • Application layer protocol for WWW
    • Client/Server model:
      • Client: browser that requests, receives, displays object
      • Server: receives requests and responds to them
      • Proxy: intermediary that aggregates requests, responses
    • Protocol consists of various operations
      • Few for HTTP 1.0 (RFC 1945, 1996)
      • Many more in HTTP 1.1 (RFC 2616, 1999)
    Client Proxy Server http request http request http response http response
  • 9. How are Requests Generated?
    • User clicks on something
    • Uniform Resource Locator (URL):
      • http://www.nytimes.com
      • https://www.paymybills.com
      • ftp://ftp.kernel.org
      • news://news.deja.com
      • telnet://gaia.cs.umass.edu
      • mailto:nahum@us.ibm.com
    • Different URL schemes map to different services
    • Hostname is converted from a name to a 32-bit IP address (DNS resolve)
    • Connection is established to server
    Most browser requests are HTTP requests.
  • 10. How are DNS names resolved?
    • Clients have a well-known IP address for a local DNS name server
    • Clients ask local name server for IP address
    • Local name server may not know it, however!
    • Name server has, in turn, a parent to ask (the “DNS hierarchy”)
    • The local name server’s job is to iteratively query servers until name is found and return IP address to server
    • Each name server can cache names, but:
      • Each name:IP mapping has a time-to-live field
      • After time expires, name server must discard mapping
  • 11. DNS in Action myclient.watson.ibm.com ns.watson.ibm.com (name server) A.GTLD-SERVER.NET (name server for .edu) ns.ucla.edu (name server) 12.100.104.5 www.ipam.ucla.edu www.ipam.ucla.edu? www.ipam.ucla.edu? ns.ucla.edu (TTL = 1d) 12.100.104.5 (TTL = 10 min) 12.100.104.5 www.ipam.ucla.edu? GET /index.html 200 OK index.html
  • 12. What Happens Then?
    • Client downloads HTML document
      • Sometimes called “container page”
      • Typically in text format (ASCII)
      • Contains instructions for rendering
        • (e.g., background color, frames)
      • Links to other pages
    • Many have embedded objects:
      • Images: GIF, JPG (logos, banner ads)
      • Usually automatically retrieved
        • I.e., without user involvement
        • can control sometimes
        • (e.g. browser options, junkbuster)
    <html> <head> <meta name=“Author” content=“Erich Nahum”> <title> Linux Web Server Performance </title> </head> <body text=“#00000”> <img width=31 height=11 src=“ibmlogo.gif”> <img src=“images/new.gif> <h1>Hi There!</h1> Here’s lots of cool linux stuff! <a href=“more.html”> Click here</a> for more! </body> </html> sample html file
  • 13. So What’s a Web Server Do?
    • Respond to client requests, typically a browser
      • Can be a proxy , which aggregates client requests (e.g., AOL)
      • Could be search engine spider or custom (e.g., Keynote)
    • May have work to do on client’s behalf:
      • Is the client’s cached copy still good?
      • Is client authorized to get this document?
      • Is client a proxy on someone else’s behalf?
      • Run an arbitrary program (e.g., stock trade)
    • Hundreds or thousands of simultaneous clients
    • Hard to predict how many will show up on some day
    • Many requests are in progress concurrently
    Server capacity planning is non-trivial.
  • 14. What do HTTP Requests Look Like? GET /images/penguin.gif HTTP/1.0 User-Agent: Mozilla/0.9.4 (Linux 2.2.19) Host: www.kernel.org Accept: text/html, image/gif, image/jpeg Accept-Encoding: gzip Accept-Language: en Accept-Charset: iso-8859-1,*,utf-8 Cookie: B=xh203jfsf; Y=3sdkfjej <cr><lf>
    • Messages are in ASCII (human-readable)
    • Carriage-return and line-feed indicate end of headers
    • Headers may communicate private information
      • (browser, OS, cookie information, etc.)
  • 15. What Kind of Requests are there?
    • Called Methods :
    • GET: retrieve a file (95% of requests)
    • HEAD: just get meta-data (e.g., mod time)
    • POST: submitting a form to a server
    • PUT: store enclosed document as URI
    • DELETE: removed named resource
    • LINK/UNLINK: in 1.0, gone in 1.1
    • TRACE: http “echo” for debugging (added in 1.1)
    • CONNECT: used by proxies for tunneling (1.1)
    • OPTIONS: request for server/proxy options (1.1)
  • 16. What Do Responses Look Like? HTTP/1.0 200 OK Server: Tux 2.0 Content-Type: image/gif Content-Length: 43 Last-Modified: Fri, 15 Apr 1994 02:36:21 GMT Expires: Wed, 20 Feb 2002 18:54:46 GMT Date: Mon, 12 Nov 2001 14:29:48 GMT Cache-Control: no-cache Pragma: no-cache Connection: close Set-Cookie: PA=wefj2we0-jfjf <cr><lf> <data follows…>
    • Similar format to requests (i.e., ASCII)
  • 17. What Responses are There?
    • 1XX: Informational (def’d in 1.0, used in 1.1)
      • 100 Continue , 101 Switching Protocols
    • 2XX: Success
      • 200 OK, 206 Partial Content
    • 3XX: Redirection
      • 301 Moved Permanently, 304 Not Modified
    • 4XX: Client error
      • 400 Bad Request, 403 Forbidden, 404 Not Found
    • 5XX: Server error
      • 500 Internal Server Error, 503 Service Unavailable, 505 HTTP Version Not Supported
  • 18. What are all these Headers?
    • General:
      • Connection, Date
    • Request:
      • Accept-Encoding, User-Agent
    • Response:
      • Location, Server type
    • Entity:
      • Content-Encoding, Last-Modified
    • Hop-by-hop:
      • Proxy-Authenticate, Transfer-Encoding
    Specify capabilities and properties: Server must pay attention to respond properly.
  • 19. The Role of Proxies clients servers proxy Internet
    • Clients send requests to local proxy
    • Proxy sends requests to remote servers
    • Proxy can cache responses and return them
  • 20. Why have a Proxy?
    • For performance:
      • Many of the same web documents are requested by many different clients (“locality of reference”)
      • A copy of the document can be cached for later requests (typical document hit rate: ~ 50%)
      • Since proxy is closer to client, responses times are smaller than from server
    • For cost savings:
      • Organizations pay by ISP bandwidth used
      • Cached responses don’t consume ISP bandwidth
    • For security/policy:
      • Typically located in “demilitarized zone” (DMZ)
      • Easier to protect a single point rather than all clients
      • Can enforce corporate/government policies (e.g., porn)
  • 21. Proxy Placement in the Web clients servers proxy Internet proxy proxy “ reverse” proxy
    • Proxies can be placed in arbitrary points in net:
      • Can be organized into hierarchies
      • Placed in front of a server: “ reverse ” proxy
      • Route requests to specific proxies: content distribution
  • 22. Content Distribution Networks origin servers proxy Internet proxy proxy
    • Push content out to proxies:
      • Route client requests to “closest” proxy
      • Reduce load on origin server
      • Reduce response time seen by client
  • 23. Mechanisms for CDN’s
    • IP Anycast:
      • Route an IP packet to one-of-many IP addresses
      • Some research but not deployed or supported by IPV4
    • TCP Redirection:
      • Client TCP packets go to one machine, but responses come from a different one
      • Clunky, not clear it reduces load or response time
    • HTTP Redirection:
      • When client connects, use 302 response (moved temp) to send client to proxy close to client
      • Server must be aware of CDN network
    • DNS Redirection:
      • When client asks for server IP address, tell them based on where they are in the network
      • Used by most CDN providers (e.g., Akamai)
  • 24. DNS Based Request-Routing local nameserver client request- routing DNS name server www.service.com cdn 2 cdn 4 cdn 3 cdn 1 cdn 5 service.com? service.com? cdn 3 cdn 3
  • 25. Summary: Introduction to WWW
    • The major application on the Internet
      • Majority of traffic is HTTP (or HTTP-related)
      • Messages mostly in ASCII text (helps debugging!)
    • Client/server model:
      • Clients make requests, servers respond to them
      • Proxies act as servers to clients, clients to servers
    • Content may be spread across network
      • Through either proxy caches or content distr. networks
      • DNS redirection is the common approach to CDNs
    • Various HTTP headers and commands
      • Too many to go into detail here
      • We’ll focus on common server ones
      • Many web books/tutorials exist
      • (e.g., Krishnamurthy & Rexford 2001)
  • 26. Chapter 2: Outline of a Typical Web Server Transaction
  • 27. Outline of an HTTP Transaction
    • In this section we go over the basics of servicing an HTTP GET request from user space
    • For this example, we'll assume a single process running in user space, similar to Apache 1.3
    • At each stage see what the costs/problems can be
    • Also try to think of where costs can be optimized
    • We’ll describe relevant socket operations as we go
    initialize; forever do { get request; process; send response; log request; } server in a nutshell
  • 28. Readying a Server
    • First thing a server does is notify the OS it is interested in WWW server requests; these are typically on TCP port 80. Other services use different ports (e.g., SSL is on 443)
    • Allocate a socket and bind() 's it to the address (port 80)
    • Server calls listen() on the socket to indicate willingness to receive requests
    • Calls accept() to wait for a request to come in (and blocks)
    • When the accept() returns, we have a new socket which represents a new connection to a client
    s = socket(); /* allocate listen socket */ bind(s, 80); /* bind to TCP port 80 */ listen(s); /* indicate willingness to accept */ while (1) { newconn = accept(s); /* accept new connection */b
  • 29. Processing a Request
    • getsockname() called to get the remote host name
      • for logging purposes (optional, but done by most)
    • gethostbyname() called to get name of other end
      • again for logging purposes
    • gettimeofday() is called to get time of request
      • both for Date header and for logging
    • read() is called on new socket to retrieve request
    • request is determined by parsing the data
      • “ GET /images/jul4/flag.gif”
    remoteIP = getsockname(newconn); remoteHost = gethostbyname(remoteIP); gettimeofday(currentTime); read(newconn, reqBuffer, sizeof(reqBuffer)); reqInfo = serverParse(reqBuffer);
  • 30. Processing a Request (cont)
    • stat() called to test file path
      • to see if file exists/is accessible
      • may not be there, may only be available to certain people
      • &quot;/microsoft/top-secret/plans-for-world-domination.html&quot;
    • stat() also used for file meta-data
      • e.g., size of file, last modified time
      • &quot;Have plans changed since last time I checked?“
    • might have to stat() multiple files just to get to end
      • e.g., 4 stats in bill g example above
    • assuming all is OK, open() called to open the file
    fileName = parseOutFileName(requestBuffer); fileAttr = stat(fileName); serverCheckFileStuff(fileName, fileAttr); open(fileName);
  • 31. Responding to a Request
    • read() called to read the file into user space
    • write() is called to send HTTP headers on socket
      • (early servers called write() for each header!)
    • write() is called to write the file on the socket
    • close() is called to close the socket
    • close() is called to close the open file descriptor
    • write() is called on the log file
    read(fileName, fileBuffer); headerBuffer = serverFigureHeaders(fileName, reqInfo); write(newSock, headerBuffer); write(newSock, fileBuffer); close(newSock); close(fileName); write(logFile, requestInfo);
  • 32. Optimizing the Basic Structure
    • As we will see, a great deal of locality exists in web requests and web traffic.
    • Much of the work described above doesn't really need to be performed each time.
    • Optimizations fall under 2 categories: caching and custom OS primitives.
  • 33. Optimizations: Caching
    • Again, cache HTTP header info on a per-url basis, rather than re-generating info over and over.
    fileDescriptor = lookInFDCache(fileName); metaInfo = lookInMetaInfoCache(fileName); headerBuffer = lookInHTTPHeaderCache(fileName); Idea is to exploit locality in client requests. Many files are requested over and over (e.g., index.html).
    • Why open and close files over and over again? Instead, cache open file FD’s, manage them LRU.
    • Why stat them again and again? Cache path name and access characteristics.
  • 34. Optimizations: Caching (cont)
    • Instead of reading and writing the data, cache data, as well as meta-data, in user space
    fileData = lookInFileDataCache(fileName); fileData = lookInMMapCache(fileName); remoteHostName = lookRemoteHostCache(fileName);
    • Since we see the same clients over and over, cache the reverse name lookups (or better yet, don't do resolves at all, log only IP addresses)
    • Even better, mmap() the file so that two copies don't exist in both user and kernel space
  • 35. Optimizations: OS Primitives
    • Rather than call accept() , getsockname() & read() , add a new primitive, acceptExtended(), which combines the 3 primitives
    acceptExtended(listenSock, &newSock, readBuffer, &remoteInfo); currentTime = *mappedTimePointer; buffer[0] = firstHTTPHeader; buffer[1] = secondHTTPHeader; buffer[2] = fileDataBuffer; writev(newSock, buffer, 3);
    • Instead of calling write() many times, use writev()
    • Instead of calling gettimeofday() , use a memory-mapped counter that is cheap to access (a few instructions rather than a system call)
  • 36. OS Primitives (cont)
    • Rather than calling read() & write() , or write() with an mmap() 'ed file, use a new primitive called sendfile() (or transmitfile() ). Bytes stay in the kernel.
    httpInfo = cacheLookup(reqBuffer); sendfile(newConn, httpInfo->headers, httpInfo->fileDescriptor, OPT_CLOSE_WHEN_DONE);
    • Also add an option to close the connection so that we don't have to call close() explicitly.
    • While we're at it, add a header option to sendfile() so that we don't have to call write() at all.
    All this assumes proper OS support. Most have it these days.
  • 37. An Accelerated Server Example
    • acceptex() is called
      • gets new socket, request, remote host IP address
    • string match in hash table is done to parse request
      • hash table entry contains relevant meta-data, including modification times, file descriptors, permissions, etc.
    • sendfile() is called
      • pre-computed header, file descriptor, and close option
    • log written back asynchronously (buffered write() ).
    That’s it! acceptex(socket, newConn, reqBuffer, remoteHostInfo); httpInfo = cacheLookup(reqBuffer); sendfile(newConn, httpInfo->headers, httpInfo->fileDescriptor, OPT_CLOSE_WHEN_DONE); write(logFile, requestInfo);
  • 38. Complications
    • Much of this assumes sharing is easy :
      • but, this is dependent on the server architectural model
      • if multiple processes are being used, as in Apache, it is difficult to share data structures.
    • Take, for example, mmap() :
      • mmap() maps a file into the address space of a process .
      • a file mmap'ed in one address space can’t be re-used for a request for the same file served by another process.
      • Apache 1.3 does use mmap() instead of read() .
      • in this case, mmap() eliminates one data copy versus a separate read() & write() combination, but process will still need to open() and close() the file.
  • 39. Complications (cont)
    • Similarly, meta-data info needs to be shared:
      • e.g., file size, access permissions, last modified time, etc.
    • While locality is high, cache misses can and do happen sometimes:
      • if previously unseen file requested, process can block waiting for disk.
    • OS can impose other restrictions:
      • e.g., limits on number of open file descriptors.
      • e.g., sockets typically allow buffering about 64 KB of data. If a process tries to write() a 1 MB file, it will block until other end receives the data.
    • Need to be able to cope with the misses without slowing down the hits
  • 40. Summary: Outline of a Typical HTTP Transaction
    • A server can perform many steps in the process of servicing a request
    • Different actions depending on many factors:
      • e.g., 304 not modified if client's cached copy is good
      • e.g., 404 not found, 401 unauthorized
    • Most requests are for small subset of data:
      • we’ll see more about this in the Workload section
      • we can leverage that fact for performance
    • Architectural model affects possible optimizations
      • we’ll go into this in more detail in the next section
  • 41. Chapter 3: Server Architectural Models
  • 42. Server Architectural Models
    • Several approaches to server structure:
    • Process based: Apache, NCSA
    • Thread-based: JAWS, IIS
    • Event-based: Flash, Zeus
    • Kernel-based: Tux, AFPA, ExoKernel
    • We will describe the advantages and disadvantages of each.
    • Fundamental tradeoffs exist between performance, protection, sharing, robustness, extensibility, etc.
  • 43. Process Model (ex: Apache)
    • Process created to handle each new request:
      • Process can block on appropriate actions,
      • (e.g., socket read, file read, socket write)
      • Concurrency handled via multiple processes
    • Quickly becomes unwieldy:
      • Process creation is expensive.
      • Instead, pre-forked pool is created.
      • Upper limit on # of processes is enforced
        • First by the server, eventually by the operating system.
        • Concurrency is limited by upper bound
  • 44. Process Model: Pros and Cons
    • Advantages:
      • Most importantly, consistent with programmer's way of thinking. Most programmers think in terms of linear series of steps to accomplish task.
      • Processes are protected from one another; can't nuke data in some other address space. Similarly, if one crashes, others unaffected.
    • Disadvantages:
      • Slow. Forking is expensive, allocating stack, VM data structures for each process adds up and puts pressure on the memory system.
      • Difficulty in sharing info across processes.
      • Have to use locking.
      • No control over scheduling decisions.
  • 45. Thread Model (Ex: JAWS)
    • Use threads instead of processes. Threads consume fewer resources than processes (e.g., stack, VM allocation).
    • Forking and deleting threads is cheaper than processes.
    • Similarly, pre-forked thread pool is created. May be limits to numbers but hopefully less of an issue than with processes since fewer resources required.
  • 46. Thread Model: Pros and Cons
    • Advantages:
      • Faster than processes. Creating/destroying cheaper.
      • Maintains programmer's way of thinking.
      • Sharing is enabled by default.
    • Disadvantages:
      • Less robust. Threads not protected from each other.
      • Requires proper OS support, otherwise, if one thread blocks on a file read, will block all the address space.
      • Can still run out of threads if servicing many clients concurrently.
      • Can exhaust certain per-process limits not encountered with processes (e.g., number of open file descriptors).
      • Limited or no control over scheduling decisions.
  • 47. Event Model (Ex: Flash)
    • Use a single process and deal with requests in a event-driven manner, like a giant switchboard.
    • Use non-blocking option ( O_NDELAY ) on sockets, do everything asynchronously, never block on anything, and have OS notify us when something is ready.
    while (1) { accept new connections until none remaining; call select() on all active file descriptors; for each FD: if (fd ready for reading) call read(); if (fd ready for writing) call write(); }
  • 48. Event-Driven: Pros and Cons
    • Advantages:
      • Very fast.
      • Sharing is inherent, since there’s only one process.
      • Don't even need locks as in thread models.
      • Can maximize concurrency in request stream easily.
      • No context-switch costs or extra memory consumption.
      • Complete control over scheduling decisions.
    • Disadvantages:
      • Less robust. Failure can halt whole server.
      • Pushes per-process resource limits (like file descriptors).
      • Not every OS has full asynchronous I/O, so can still block on a file read. Flash uses helper processes to deal with this (AMPED architecture).
  • 49. In-Kernel Model (Ex: Tux)
    • Dedicated kernel thread for HTTP requests:
      • One option: put whole server in kernel.
      • More likely, just deal with static GET requests in kernel to capture majority of requests.
      • Punt dynamic requests to full-scale server in user space, such as Apache.
    user/ kernel boundary user-space server kernel-space server user/ kernel boundary TCP HTTP IP ETH SOCK TCP IP ETH HTTP
  • 50. In-Kernel Model: Pros and Cons
    • In-kernel event model:
      • Avoids transitions to user space, copies across u-k boundary, etc.
      • Leverages already existing asynchronous primitives in the kernel (kernel doesn't block on a file read, etc.)
    • Advantages:
      • Extremely fast. Tight integration with kernel.
      • Small component without full server optimizes common case.
    • Disadvantages:
      • Less robust. Bugs can crash whole machine, not just server.
      • Harder to debug and extend, since kernel programming required, which is not as well-known as sockets.
      • Similarly, harder to deploy. APIs are OS-specific (Linux, BSD, NT), whereas sockets & threads are (mostly) standardized.
      • HTTP evolving over time, have to modify kernel code in response.
  • 51. So What’s the Performance?
    • Graph shows server throughput for Tux, Flash, and Apache.
    • Experiments done on 400 MHz P/II, gigabit Ethernet, Linux 2.4.9-ac10, 8 client machines, WaspClient workload generator
    • Tux is fastest, but Flash close behind
  • 52. Summary: Server Architectures
    • Many ways to code up a server
      • Tradeoffs in speed, safety, robustness, ease of programming and extensibility, etc.
    • Multiple servers exist for each kind of model
      • Not clear that a consensus exists.
    • Better case for in-kernel servers as devices
      • e.g. reverse proxy accelerator, Akamai CDN node
    • User-space servers have a role:
      • OS should provides proper primitives for efficiency
      • Leave HTTP-protocol related actions in user-space
      • In this case, event-driven model is attractive
    • Key pieces to a fast event-driven server:
      • Minimize copying
      • Efficient event notification mechanism
  • 53. Chapter 5: Workload Characterization
  • 54. Workload Characterization
    • Why Characterize Workloads?
      • Gives an idea about traffic behavior
      • (&quot;Which documents are users interested in?&quot;)
      • Aids in capacity planning
      • (&quot;Is the number of clients increasing over time?&quot;)
      • Aids in implementation
      • (&quot;Does caching help?&quot;)
    • How do we capture them ?
      • Through server logs (typically enabled)
      • Through packet traces (harder to obtain and to process)
  • 55. Factors to Consider
    • Where do I get logs from?
      • Client logs give us an idea, but not necessarily the same
      • Same for proxy logs
      • What we care about is the workload at the server
    • Is trace representative?
      • Corporate POP vs. News vs. Shopping site
    • What kind of time resolution?
      • e.g., second, millisecond, microsecond
    • Does trace/log capture all the traffic?
      • e.g., incoming link only, or one node out of a cluster
    client? proxy? server?
  • 56. Probability Refresher
    • Lots of variability in workloads
      • Use probability distributions to express
      • Want to consider many factors
    • Some terminology/jargon:
      • Mean: average of samples
      • Median : half are bigger, half are smaller
      • Percentiles: dump samples into N bins
      • (median is 50th percentile number)
    • Heavy-tailed:
      • As x->infinity
  • 57. Important Distributions
    • Some Frequently-Seen Distributions:
    • Normal:
      • (avg. sigma, variance mu)
    • Lognormal:
      • (x >= 0; sigma > 0)
    • Exponential:
      • (x >= 0)
    • Pareto:
      • (x >= k, shape a, scale k)
  • 58. More Probability
    • Graph shows 3 distributions with average = 2.
    • Note average  median in some cases !
    • Different distributions have different “weight” in tail.
  • 59. What Info is Useful?
    • Request methods
      • GET, POST, HEAD, etc.
    • Response codes
      • success, failure, not-modified, etc.
    • Size of requested files
    • Size of transferred objects
    • Popularity of requested files
    • Numbers of embedded objects
    • Inter-arrival time between requests
    • Protocol support (1.0 vs. 1.1)
  • 60. Sample Logs for Illustration We’ll use statistics generated from these logs as examples. 12,445,739 11,485,600 5,800,000 1,586,667 Hits: 28,804,852 54,697,108 10,515,507 14,171,711 Bytes: 319,698 86,0211 80,921 256,382 Clients: Corporate Presence Corporate Presence Nagano 1998 Olympics Event Site Kasparov-Deep Blue Event Site Description: 1 day in Feb 2001 1 day in June 1998 2 days in Feb 1998 2 weeks in May 1997 Period: 42,874 15,788 30,465 2,293 URLS: IBM 2001 IBM 1998 Olympics 1998 Chess 1997 Name:
  • 61. Request Methods
    • KR01: &quot;overwhelming majority&quot; are GETs, few POSTs
    • IBM2001 trace starts seeing a few 1.1 methods (CONNECT, OPTIONS, LINK), but still very small (1/10^5 %)
    noise noise noise noise Others: 00.2% 00.02% 00.04 % 00.007% POST 02% 00.08% 00.3 % 04% HEAD 97% 99.3% 99.6% 96% GET IBM 2001 IBM 1998 Olympics 1998 Chess 1997
  • 62. Response Codes
    • Table shows percentage of responses.
    • Majority are OK and NOT_MODIFIED.
    • Consistent with numbers from AW96, KR01.
    67.72 --.-- --.-- --.-- 15.11 16.26 00.001 00.001 00.009 00.79 00.002 00.07 00.006 00.0003 00.0004 75.28 00.00001 --.-- --.-- 01.18 22.84 00.003 00.0001 00.01 00.65 --.-- 00.006 00.0005 00.0001 00.005 76.02 --.-- --.-- --.-- 00.05 23.24 00.0001 00.001 00.02 00.64 --.-- 00.003 00.0001 --.-- 00.00004 85.32 --.-- 00.25 00.05 00.05 13.73 00.001 --.—- 00.01 00.55 --.-- --.-- --.-- --.-- 00.0003 OK NO_CONTENT PARTIAL_CONTENT MOVED_PERMANENTLY MOVED_TEMPORARILY NOT_MODIFIED BAD_REQUEST UNAUTHORIZED FORBIDDEN NOT_FOUND PROXY_AUTH SERVER_ERROR NOT_IMPLEMENTED SERVICE_UNAVAIL UNKNOWN 200 204 206 301 302 304 400 401 403 404 407 500 501 503 ??? IBM 2001 IBM 1998 Olympics 1998 Chess 1997 Meaning Code
  • 63. Resource (File) Sizes
    • Shows file/memory usage (not weighted by frequency!)
    • Lognormal body, consistent with results from AW96, CB96, KR01.
    • AW96, CB96: sizes have Pareto tail; Downey01: Sizes are lognormal.
  • 64. Tails from the File Size
    • Shows the complementary CDF (CCDF) of file sizes.
    • Haven’t done the curve fitting but looks Pareto-ish.
  • 65. Response (Transfer) Sizes
    • Shows network usage (weighted by frequency of requests)
    • Lognormal body, pareto tail, consistent with CBC95, AW96, CB96, KR01
  • 66. Tails of Transfer Size
    • Shows the complementary CDF (CCDF) of file sizes.
    • Looks more Pareto-like; certainly some big transfers.
  • 67. Resource Popularity
    • Follows a Zipf model: p(r) = r^{-alpha}
      • (alpha = 1 true Zipf; others “Zipf-like&quot;)
    • Consistent with CBC95, AW96, CB96, PQ00, KR01
    • Shows that caching popular documents is very effective
  • 68. Number of Embedded Objects
    • Mah97: avg 3, 90% are 5 or less
    • BC98: pareto distr, median 0.8, mean 1.7
    • Arlitt98 World Cup study: median 15 objects, 90% are 20 or less
    • MW00: median 7-17, mean 11-18, 90% 40 or less
    • STA00: median 5,30 (2 traces), 90% 50 or less
    • Mah97, BC98, SCJO01: embedded objects tend to be smaller than container objects
    • KR01: median is 8-20, pareto distribution
    Trend seems to be that number is increasing over time.
  • 69. Session Inter-Arrivals
    • Inter-arrival time between successive requests
      • “ Think time&quot;
      • difference between user requests vs. ALL requests
      • partly depends on definition of boundary
    • CB96: variability across multiple timescales, &quot;self-similarity&quot;, average load very different from peak or heavy load
    • SCJO01: log-normal, 90% less than 1 minute.
    • AW96: independent and exponentially distributed
    • KR01: pareto with a=1.5, session arrivals follow poisson distribution, but requests follow pareto
  • 70. Protocol Support
    • IBM.com 2001 logs:
      • Show roughly 53% of client requests are 1.1
    • KA01 study:
      • 92% of servers claim to support 1.1 (as of Sep 00)
      • Only 31% actually do; most fail to comply with spec
    • SCJO01 show:
      • Avg 6.5 requests per persistent connection
      • 65% have 2 connections per page, rest more.
      • 40-50% of objects downloaded by persistent connections
    Appears that we are in the middle of a slow transition to 1.1
  • 71. Summary: Workload Characterization
    • Traffic is variable:
      • Responses vary across multiple orders of magnitude
    • Traffic is bursty:
      • Peak loads much larger than average loads
    • Certain files more popular than others
      • Zipf-like distribution captures this well
    • Two-sided aspect of transfers:
      • Most responses are small (zero pretty common)
      • Most of the bytes are from large transfers
    • Controversy over Pareto/log-normal distribution
    • Non-trivial for workload generators to replicate
  • 72. Chapter 6: Workload Generators
  • 73. Why Workload Generators?
    • Allows stress-testing and bug-finding
    • Gives us some idea of server capacity
    • Allows us a scientific process to compare approaches
      • e.g., server models, gigabit adaptors, OS implementations
    • Assumption is that difference in testbed translates to some difference in real-world
    • Allows the performance debugging cycle
    Measure Reproduce Find Problem Fix and/or improve The Performance Debugging Cycle
  • 74. Problems with Workload Generators
    • Only as good as our understanding of the traffic
    • Traffic may change over time
      • generators must too
    • May not be representative
      • e.g., are file size distributions from IBM.com similar to mine?
    • May be ignoring important factors
      • e.g., browser behavior, WAN conditions, modem connectivity
    • Still, useful for diagnosing and treating problems
  • 75. How does W. Generation Work?
    • Many clients, one server
      • match asymmetry of Internet
    • Server is populated with some kind of synthetic content
    • Simulated clients produce requests for server
    • Master process to control clients, aggregate results
    • Goal is to measure server
      • not the client or network
    • Must be robust to conditions
      • e.g., if server keeps sending 404 not found, will clients notice?
    Responses Requests
  • 76. Evolution: WebStone
    • The original workload generator from SGI in 1995
    • Process based workload generator, implemented in C
    • Clients talk to master via sockets
    • Configurable: # client machines, # client processes, run time
    • Measured several metrics: avg + max connect time, response time, throughput rate (bits/sec), # pages, # files
    • 1.0 only does GETS, CGI support added in 2.0
    • Static requests, 5 different file sizes:
    www.mindcraft.com/webstone 5 MB 0.10 500 KB 0.90 50 KB 14.00 5 KB 50.00 500 B 35.00 Size Percentage
  • 77. Evolution: SPECWeb96
    • Developed by SPEC
      • Systems Performance Evaluation Consortium
      • Non-profit group with many benchmarks (CPU, FS)
    • Attempt to get more representative
      • Based on logs from NCSA, HP, Hal Computers
    • 4 classes of files:
    • Poisson distribution between each class
    100 KB – 1 MB 1.00 10-100 KB 14.00 1-10 KB 50.00 0-1 KB 35.00 Size Percentage
  • 78. SPECWeb96 (cont)
    • Notion of scaling versus load:
      • number of directories in data set size doubles as expected throughput quadruples (sqrt(throughput/5)*10)
      • requests spread evenly across all application directories
    • Process based WG
    • Clients talk to master via RPC's (less robust)
    • Still only does GETS, no keep-alive
    • www.spec.org/osg/web96
  • 79. Evolution: SURGE
    • S calable U RL R eference GE nerator
      • Barford & Crovella at Boston University CS Dept.
    • Much more worried about representativeness, captures:
      • server file size distributions,
      • request size distribution,
      • relative file popularity
      • embedded file references
      • temporal locality of reference
      • idle periods (&quot;think times&quot;) of users
    • Process/thread based WG
  • 80. SURGE (cont)
    • Notion of “user-equivalent”:
      • statistical model of a user
      • active “off” time (between URLS),
      • inactive “off” time (between pages)
    • Captures various levels of burstiness
    • Not validated, shows that load generated is different than SpecWeb96 and has more burstiness in terms of CPU and # active connections
    • www.cs.wisc.edu/~pb
  • 81. Evolution: S-client
    • Almost all workload generators are closed-loop :
      • client submits a request, waits for server, maybe thinks for some time, repeat as necessary
    • Problem with the closed-loop approach:
      • client can't generate requests faster than the server can respond
      • limits the generated load to the capacity of the server
      • in the real world, arrivals don’t depend on server state
        • i.e., real users have no idea about load on the server when they click on a site, although successive clicks may have this property
      • in particular, can't overload the server
    • s-client tries to be open-loop :
      • by generating connections at a particular rate
      • independent of server load/capacity
  • 82. S-Client (cont)
    • How is s-client open-loop?
      • connecting asynchronously at a particular rate
      • using non-blocking connect() socket call
    • Connect complete within a particular time?
      • if yes, continue normally.
      • if not, socket is closed and new connect initiated.
    • Other details:
      • uses single-address space event-driven model like Flash
      • calls select() on large numbers of file descriptors
      • can generate large loads
    • Problems:
      • client capacity is still limited by active FD's
      • “ arrival” is a TCP connect, not an HTTP request
    • www.cs.rice.edu/CS/Systems/Web-measurement
  • 83. Evolution: SPECWeb99
    • In response to people &quot; gaming &quot; benchmark, now includes rules:
      • IP maximum segment lifetime ( MSL ) must be at least 60 seconds (more on this later!)
      • Link-layer maximum transmission unit ( MTU ) must not be larger than 1460 bytes (Ethernet frame size)
      • Dynamic content may not be cached
        • not clear that this is followed
      • Servers must log requests.
        • W3C common log format is sufficient but not mandatory.
      • Resulting workload must be within 10% of target.
      • Error rate must be below 1%.
    • Metric has changed:
      • now &quot;number of simultaneous conforming connections“: rate of a connection must be greater than 320 Kbps
  • 84. SPECWeb99 (cont)
    • Directory size has changed:
      • (25 + (400000/122000)* simultaneous conns) / 5.0)
    • Improved HTTP 1.0/1.1 support:
      • Keep-alive requests (client closes after N requests)
      • Cookies
    • Back-end notion of user demographics
      • Used for ad rotation
      • Request includes user_id and last_ad
    • Request breakdown:
      • 70.00 % static GET
      • 12.45 % dynamic GET
      • 12.60 % dynamic GET with custom ad rotation
      • 04.80 % dynamic POST
      • 00.15 % dynamic GET calling CGI code
  • 85. SPECWeb99 (cont)
    • Other breakdowns:
      • 30 % HTTP 1.0 with no keep-alive or persistence
      • 70 % HTTP 1.0 with keep-alive to &quot;model&quot; persistence
      • still has 4 classes of file size with Poisson distribution
      • supports Zipf popularity
    • Client implementation details:
      • Master-client communication now uses sockets
      • Code includes sample Perl code for CGI
      • Client configurable to use threads or processes
    • Much more info on setup, debugging, tuning
    • All results posted to web page,
      • including configuration & back end code
    • www.spec.org/osg/web99
  • 86. SpecWeb99 vs. File Sizes
    • SpecWeb99: In the ballpark, but not very smooth
  • 87. SpecWeb99 vs. File Size Tail
    • SpecWeb99 tail isn’t as long as real logs (900 KB max)
  • 88. SpecWeb99 vs.Transfer Sizes
    • Doesn’t capture 304 (not modified) responses
    • Coarser distribution than real logs (i.e., not smooth)
  • 89. Spec99 vs.Transfer Size Tails
    • SpecWeb99 does OK, although tail drops off rapidly (and in fact, no file is greater than 1 MB in SpecWeb99!).
  • 90. Spec99 vs. Resource Popularity
    • SpecWeb99 seems to do a good job, although tail isn’t long enough
  • 91. Evolution: TPC-W
    • Transaction Processing Council (TPC-W)
      • More known for database workloads like TPC-D
      • Metrics include dollars/transaction (unlike SPEC)
      • Provides specification , not source
      • Meant to capture a large e-commerce site
    • Models online bookstore
      • web serving, searching, browsing, shopping carts
      • online transaction processing (OLTP)
      • decision support (DSS)
      • secure purchasing (SSL), best sellers, new products
      • customer registration, administrative updates
    • Has notion of scaling per user
      • 5 MB of DB tables per user
      • 1 KB per shopping item, 25 KB per item in static images
  • 92. TPC-W (cont)
    • Remote browser emulator (RBE)
      • emulates a single user
      • send HTTP request, parse, wait for thinking, repeat
    • Metrics:
      • WIPS: shopping
      • WIPSb: browsing
      • WIPSo: ordering
    • Setups tend to be very large:
      • multiple image servers, application servers, load balancer
      • DB back end (typically SMP)
      • Example: IBM 12-way SMP w/DB2, 9 PCs w/IIS: 1M $
    • www.tpc.org/tpcw
  • 93. Summary: Workload Generators
    • Only the beginning. Many other workload generators:
      • httperf from HP
      • WAGON from IBM
      • WaspClient from IBM
      • Others?
    • Both workloads and generators change over time:
      • Both started simple, got more complex
      • As workload changes, so must generators
    • No one single &quot;good&quot; generator
      • SpecWeb99 seems the favorite (2002 rumored in the works)
    • Implementation issues similar to servers:
      • They are networked-based request producers
      • (i.e., produce GET's instead of 200 OK's).
      • Implementation affects capacity planning of clients!
      • (want to make sure clients are not bottleneck)
  • 94. End of this tutorial…
    • This is roughly half of a four-hour tutorial:
      • ACM SIGMETRICS 2002 (June, Marina Del Ray, CA)
    • Remainder gets into more detailed issues:
      • Event notification mechanisms in servers
      • Overview of the TCP protocol
      • TCP dynamics for servers
      • TCP implementation issues for servers
    • Talk to me if you’re still interested, or
    • Point your browser at:
    www.sigmetrics.org
  • 95. Chapter: Event Notification
    • Event notification:
      • Mechanism for kernel and application to notify each other of interesting/important events
      • E.g., connection arrivals, socket closes, data available to read, space available for writing
    • Idea is to exploit concurrency:
      • Concurrency in user workloads means host CPU can overlap multiple events to maximize parallelism
      • Keep network, disk busy; never block
    • Simultaneously, want to minimize costs:
      • user/kernel crossings and testing idle socket descriptors
    • Event notification changes applications:
      • state-based to event-based
      • requires a change in thinking
  • 96. Chapter: Introduction to TCP
    • Layering is a common principle in network protocol design
    • TCP is the major transport protocol in the Internet
    • Since HTTP runs on top of TCP, much interaction between the two
    • Asymmetry in client-server model puts strain on server-side TCP implementations
    • Thus, major issue in web servers is TCP implementation and behavior
    application transport network link physical
  • 97. Chapter: TCP Dynamics
    • In this section we'll describe some of the problems you can run into as a WWW server interacting with TCP.
    • Most of these affect the response as seen by the client, not the throughput generated by the server.
    • Ideally, a server developer shouldn't have to worry about this stuff, but in practice, we'll see that's not the case.
    • Examples we'll look at include:
      • The initial window size
      • The delayed ACK problem
      • Nagle and its interaction with delayed ack
      • Small receive windows interfering with loss recovery
  • 98. Chapter: Server TCP Implementation
    • In this section we look at ways in which the host TCP implementation is stressed under large web server workloads. Most of these techniques deal with large numbers of connections:
      • Looking up arriving TCP segments with large numbers of connections
      • Dealing with the TIME-WAIT state caused by closing large number of connections
      • Managing large numbers of timers to support connections
      • Dealing with memory consumption of connection state
    • Removing data-touching operations
      • byte copying and checksums

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