1. 15-744: Computer Networking L-21: Caching and CDNs

2. L -21; 4-4-01© Srinivasan Seshan, 20012 Caching & CDN’s HTTP APIs Assigned reading [FCAB98] Summary Cache: A Scalable Wide- Area Cache Sharing Protocol [Cla00] Freenet: A Distributed Anonymous Information Storage and Retrieval System

3. L -21; 4-4-01© Srinivasan Seshan, 20013 Overview Web caches Content distribution networks Peer-to-peer networks

4. L -21; 4-4-01© Srinivasan Seshan, 20014 Web Caching Why cache HTTP objects? Reduce client response time Reduce network bandwidth usage Wide area vs. local area use These two objectives are often in conflict May do exhaustive local search to avoid using wide area bandwidth Prefetching uses extra bandwidth to reduce client response time

5. L -21; 4-4-01© Srinivasan Seshan, 20015 Web Proxies Also used for security Proxy is only host that can access Internet Administrators makes sure that it is secure Performance How many clients can a single proxy handle? Caching Provides a centralized coordination point to share information across clients How to index Early caches used file system to find file Metadata now kept in memory on most caches

6. L -21; 4-4-01© Srinivasan Seshan, 20016 Caching Proxies - Sources for misses Capacity How large a cache is necessary or equivalent to infinite On disk vs. in memory  typically on disk Compulsory First time access to document Non-cacheable documents CGI-scripts Personalized documents (cookies, etc) Encrypted data (SSL) Consistency Document has been updated/expired before reuse Conflict  no such issue

7. L -21; 4-4-01© Srinivasan Seshan, 20017 Cache Hierarchies Use hierarchy to scale a proxy to more than limited population Why? Larger population = higher hit rate Larger effective cache size Why is population for single proxy limited? Performance, administration, policy, etc. NLANR cache hierarchy Most popular 9 top level caches Internet Cache Protocol based (ICP) Squid/Harvest proxy

8. L -21; 4-4-01© Srinivasan Seshan, 20018 ICP Simple protocol to query another cache for content Uses UDP – why? ICP message contents Type – query, hit, hit_obj, miss Other – identifier, URL, version, sender address (is this needed?) Special message types used with UDP echo port Used to probe server or “dumb cache” Transfers between caches still done using HTTP

9. L -21; 4-4-01© Srinivasan Seshan, 20019 Squid Cache ICP Use Upon query that is not in cache Sends ICP_Query to each peer (or ICP_Decho to echo port of peer caches that do not speak ICP) May also send ICP_Secho to origin server’s echo port Sets time to short period (default 2 sec) Peer caches process queries and return either ICP_Hit or ICP_Miss Proxy begins transfer upon reception of ICP_Hit, ICP_Decho or ICP_Secho Upon timer expiration, proxy request object from closest (RTT) parent proxy Would be better to direct to parent that is towards origin server

10. L -21; 4-4-01© Srinivasan Seshan, 200110 Squid Client Parent Child Web page request ICP Query

11. L -21; 4-4-01© Srinivasan Seshan, 200111 Squid Client Parent Child ICP MISS

12. L -21; 4-4-01© Srinivasan Seshan, 200112 Squid Client Parent Child Web page request

13. L -21; 4-4-01© Srinivasan Seshan, 200113 Squid Client Parent Child Web page request ICP Query

14. L -21; 4-4-01© Srinivasan Seshan, 200114 Squid Client Parent Child Web page request ICP MISS ICP HIT

15. L -21; 4-4-01© Srinivasan Seshan, 200115 Squid Client Parent Child Web page request

16. L -21; 4-4-01© Srinivasan Seshan, 200116 ICP vs HTTP Why not just use HTTP to query other caches? ICP is lightweight – positive and negative Makes it easy to process quickly Caches may process many more ICP requests than HTTP requests HTTP has many functions that are not supported by ICP ICP does not evolve with HTTP changes Adds extra RTT to any proxy-proxy transfer

17. L -21; 4-4-01© Srinivasan Seshan, 200117 Optimal Cache Mesh Behavior Minimize number of hops through mesh Each hop add significant latency ICP hops can cost a 2 sec timeout each! Strict hierarchies cost disk lookup, etc. Especially painful for misses Share across many users and scale to many caches ICP does not scale to a large number of peers Cache and fetch data close to clients

18. L -21; 4-4-01© Srinivasan Seshan, 200118 Hinting Have proxies store content as well as metadata about contents of other proxies (hints) Minimizes number of hops through mesh Size of hint cache is a concern – size of key vs. size of document Having hints can help consistency Makes it possible to push updated documents or invalidations to other caches How to keep hints up-to-date? Not critical – incorrect hint results in extra lookups not incorrect behavior Can batch updates to peers

19. L -21; 4-4-01© Srinivasan Seshan, 200119 Summary Cache Primary innovation – use of compact representation of cache contents Typical cache has 8GB of space and 8KB objects  1M objects Using 16byte MD5  16MB per peer Solution: Bloom filters Delayed propagation of hints Waits until threshold %age of cached documents are not in summary Perhaps should have looked at %age of false hits?

20. L -21; 4-4-01© Srinivasan Seshan, 200120 Bloom Filters Proxy contents summarize as a M bit value Each page stored contributes k hash values in range [1..M] Bits for k hashes set in summary Check for page = if all pages k hash bits are set in summary it is likely that proxy has summary Tradeoff  false positives Larger M reduces false positives What should M be? 8-16 * number of pages seems to work well What about k? Is related to (M/number of pages)  4 works for above M

21. L -21; 4-4-01© Srinivasan Seshan, 200121 Leases Only consistency mechanism in HTTP is for clients to poll server for updates Should HTTP also support invalidations? Problem: server would have to keep track of many, many clients who may have document Possible solution: leases Leases – server promises to provide invalidates for a particular lease duration Server can adapt time/duration of lease as needed To number of clients, frequency of page change, etc.

22. L -21; 4-4-01© Srinivasan Seshan, 200122 Problems Over 50% of all HTTP objects are uncacheable – why? Not easily solvable Dynamic data  stock prices, scores, web cams CGI scripts  results based on passed parameters Obvious fixes SSL  encrypted data is not cacheable Most web clients don’t handle mixed pages well  many generic objects transferred with SSL Cookies  results may be based on passed data Hit metering  owner wants to measure # of hits for revenue, etc. What will be the end result?

23. L -21; 4-4-01© Srinivasan Seshan, 200123 Proxy implementation problems Aborted transfers Many proxies transfer entire document even though client has stopped  eliminates saving of bandwidth Making objects cacheable Proxy’s apply heuristics  cookies don’t apply to some objects, guesswork on expiration May not match client behavior/desires Client misconfiguration Many clients have either absurdly small caches or no cache How much would hit rate drop if clients did the same things as proxies

24. L -21; 4-4-01© Srinivasan Seshan, 200124 Problems – Population Size How does population size affect hit rate? Critical to understand usefulness of hierarchy or placement of caches Issues: frequency of access vs. frequency of change (ignore working set size  infinite cache) UW/Msoft measurement  hit rate rises quickly to about 5000 people and very slowly beyond that Proxies/Hierarchies don’t make much sense for populations > 5000 Single proxies can easily handle such populations Hierarchies only make sense for policy/administrative reasons

25. L -21; 4-4-01© Srinivasan Seshan, 200125 Problems – Common Interests Do different communities have different interests? I.e. do CS and English majors access same pages? IBM and Pepsi workers? Has some impact  UW departments have about 5% higher hit rate than randomly chosen UW groups Many common interests remain Is this true in general? UW students have more in common than IBM & Pepsi workers Some related observations Geographic caching – server traces have shown that there is geographic locality to interest UW & MS hierarchy performance is bad – could be due to size or interests?

26. L -21; 4-4-01© Srinivasan Seshan, 200126 Overview Web caches Content distribution networks Peer-to-peer networks

27. L -21; 4-4-01© Srinivasan Seshan, 200127 CDN Replicate content on many servers Challenges How to replicate content Where to replicate content How to find replicated content How to choose among know replicas How to direct clients towards replica Discussed in DNS/server selection lecture DNS, HTTP 304 response, anycast, etc. Akamai

28. L -21; 4-4-01© Srinivasan Seshan, 200128 How Akamai Works Clients fetch html document from primary server E.g. fetch index.html from cnn.com URLs for replicated content are replaced in html E.g. replaced with Client is forced to resolve aXYZ.g.akamaitech.net hostname

29. L -21; 4-4-01© Srinivasan Seshan, 200129 How Akamai Works How is content replicated? Akamai only replicates static content Modified name contains original file Akamai server is asked for content First checks local cache If not in cache, requests file from primary server and caches file

30. L -21; 4-4-01© Srinivasan Seshan, 200130 How Akamai Works Root server gives NS record for akamai.net Akamai.net name server returns NS record for g.akamaitech.net Name server chosen to be in region of client’s name server TTL is large G.akamaitech.net nameserver choses server in region Should try to chose server that has file in cache - How to choose? Uses aXYZ name and consistent hash TTL is small

31. L -21; 4-4-01© Srinivasan Seshan, 200131 Consistent Hash “view” = subset of all hash buckets that are visible Desired features Smoothness – little impact on hash bucket contents when buckets are added/removed Spread – small set of hash buckets that may hold an object regardless of views Load – across all views # of objects assigned to hash bucket is small

32. L -21; 4-4-01© Srinivasan Seshan, 200132 Consistent Hash – Example Construction Assign each of C hash buckets to Klog(C) random points on unit interval Map object to random position on unit interval Hash of object = closest bucket Monotone  addition of bucket does not cause movement between existing buckets Spread & Load  small set of buckets that lie near object Balance  no bucket is responsible for large portion of unit interval

33. L -21; 4-4-01© Srinivasan Seshan, 200133 How Akamai Works End-user cnn.com (content provider)DNS root serverAkamai server 123 4 Akamai high-level DNS server Akamai low-level DNS server Closest Akamai server 11 6 7 8 9 10 Get index. html Get /cnn.com/foo.jpg 12 Get foo.jpg 5

34. L -21; 4-4-01© Srinivasan Seshan, 200134 Akamai – Subsequent Requests End-user cnn.com (content provider)DNS root serverAkamai server 12 Akamai high-level DNS server Akamai low-level DNS server Closest Akamai server 7 8 9 10 Get index. html Get /cnn.com/foo.jpg

35. L -21; 4-4-01© Srinivasan Seshan, 200135 Overview Web caches Content distribution networks Peer-to-peer networks

36. L -21; 4-4-01© Srinivasan Seshan, 200136 Peer-to-Peer Networks Typically each member stores content that it desires Basically a replication system for files Always a tradeoff between possible location of files and searching difficulty Peer-to-peer allow files to be anywhere  searching is the challenge Dynamic member list makes it more difficult

37. L -21; 4-4-01© Srinivasan Seshan, 200137 Napster Simple centralized scheme  motivated by ability to sell/control On startup client contacts central server and reports list of files Upon query, central server returns list of possible clients that store data Transfer is done peer-to-peer

38. L -21; 4-4-01© Srinivasan Seshan, 200138 Gnutella On startup client contacts any servent (server + client) in network Basic message header Unique ID, TTL, Hops Transfers are done with HTTP between peers Servent interconnection used to forward control (queries, hits, etc)

39. L -21; 4-4-01© Srinivasan Seshan, 200139 Gnutella Details Message types Ping – probes network for other servents Pong – response to ping, contains IP addr, # of files, # of Kbytes shared Query – search criteria + speed requirement of servent QueryHit – successful response to Query, contains addr + port to transfer from, speed of servent, number of hits, hit results, servent ID Push – request to servent ID to initiate connection, used to traverse firewalls Ping, Queries are flooded QueryHit, Pong, Push reverse path of previous message

40. L -21; 4-4-01© Srinivasan Seshan, 200140 Freenet Anonymity a primary goal Files are stored according to associated key Core idea: try to cluster information about similar keys Messages Random 64bit ID used for loop detection TTL TTL 1 are forwarded with finite probablity Helps anonymity depth counter Opposite of TTL – incremented with each hop Depth counter initialized to small random value

41. L -21; 4-4-01© Srinivasan Seshan, 200141 Freenet Requests User requests key XYZ – not in local cache Looks up nearest key in routing table and forwards to corresponding node If request reaches node with data, it forwards data back to upstream requestor Requestor adds file to cache, adds entry in routing table Any node forwarding reply may change the source of the reply (to itself or any other node) Helps anonymity If data is not found, failure is reported back Requestor then tries next closest match in routing table

42. L -21; 4-4-01© Srinivasan Seshan, 200142 Freenet Request 1 AB C D E F Data Request Data Reply Request Failed 2 3 12 6 7 4 11 10 9 5 8

43. L -21; 4-4-01© Srinivasan Seshan, 200143 Freenet Search Features Nodes tend to specialize in searching for similar keys over time Gets queries from other nodes for similar keys Nodes store similar keys over time Caching of files as a result of successful queries Similarity of keys does not reflect similarity of files Routing does not reflect network topology

44. L -21; 4-4-01© Srinivasan Seshan, 200144 Freenet File Creation Key for file generated and searched  helps identify collision Not found (“All clear”) result indicates success Source of insert message can be change by any forwarding node Creation mechanism adds files/info to locations with similar keys New nodes are discovered through file creation Erroneous/malicious inserts propagate original file further

45. L -21; 4-4-01© Srinivasan Seshan, 200145 Cache Management LRU Cache of files Files are not guaranteed to live forever Files “fade away” as fewer requests are made for them File contents can be encrypted with original text names as key Cache owners do not know either original name or contents  cannot be held responsible

46. L -21; 4-4-01© Srinivasan Seshan, 200146 Freenet Naming Freenet deals with keys But humans need names Keys are flat  would like structure as well Could have files that store keys for other files File /text/philiosophy could store keys for files in that directory  how to update this file though? Search engine  undesirable centralized solution

47. L -21; 4-4-01© Srinivasan Seshan, 200147 Freenet Naming - Indirect files Normal files stored using content-hash key Prevents tampering, enables versioning, etc. Indirect files stored using name-based key Indirect files store keys for normal files Inserted at same time as normal file Has same update problems as directory files Updates handled by signing indirect file with public/private key Collisions for insert of new indirect file handled specially  check to ensure same key used for signing Allows for files to be split into multiple smaller parts

48. L -21; 4-4-01© Srinivasan Seshan, 200148 Next Lecture: QOS & IntServ QOS IntServ Architecture Assigned reading [She95] Fundamental Design Issues for the Future Internet [CSZ92] Supporting Real-Time Applications in an Integrated Services Packet Network: Architecture and Mechanisms