The Internet Domain Name System
Hari Balakrishnan
6.829 Fall 2002
Goals
• DNS architecture
How DNS works
• DNS uses
Mail
Content Distribution Networks (CDNs)
• DNS Performance
How well does it work?
Why does it work?
Why naming?
• Level(s) of indirection between a resource
and its location
• Convenience
For apps
For humans
Autonomous organizational operation (real-world)
• Examples
DNS, search engines, intentional names,…
Virtual memory, DHTs,…
DNS architecture
• Two major components
Name servers: Information repositories
Resolvers: Interface to client programs
• Stub resolver as libraries
• Forwarding name servers that proxy for stubs
• DNS name space
• Resource records
• Database distribution
Zones
Caching
• Datagram-based protocol
DNS name space
• Organized as a variable-depth rooted tree
• Each node in tree has associated label
Label = variable-length string of octets
Case-insensitive
• DNS name of node = path from node to root
E.g., nms.lcs.mit.edu. (“.” separates labels)
Joe.Schmoe@lcs.mit.edu. (left of “@” is a single label, to the
right are four labels)
• No implicit semantics in tree structure in general
Except for IN-ADDR.ARPA domain used for reverse lookups
• Design tuned for administrative delegation of the
name space (more on this in a bit)
Resource Records (RRs)
• Data in DNS structured using RRs
• Idea is to help both apps and DNS itself
• Classes are orthogonal to each other
IN,ISO, CHAOS, XNS,… (pretty much only IN
today!)
• Each class has a set of types; new types can
be added, but require standardization
• Example IN types
A, NS, MX, PTR, CNAME, …
Example
• dig www.google.com
www.google.com. 162 IN A 216.239.53.100
google.com. 345579 IN NS ns3.google.com.
google.com. 345579 IN NS ns4.google.com.
google.com. 345579 IN NS ns1.google.com.
google.com. 345579 IN NS ns2.google.com.
• dig www.google.com –t MX
www.google.com. 86210 IN MX 20 smtp2.google.com.
• What are the #s in the second column?
• What’s the number next to the MX answer?
• Advantage of one RR per type, versus single RR with multiple
values?
Database distribution
• Two distribution mechanisms
Zones
Caching
• Separation invisible to user/application
• Zone = complete description of a contiguous section
of the DNS name space
Stores RRs for labels
And pointers to all other contiguous zones
Zone divisions can be made anywhere in the name space
Zone logistics
• Persuade parent organization to delegate a
subzone consisting of a single node
E.g.,persuade lcs.mit.edu. to delegate
nms.lcs.mit.edu (the delegated node is “nms”)
Persuade com. to delegate label “cnn” to me
• New zone can grow to arbitrary size and
further delegated autonomously
Zone owner’s responsibilities
• Authoritatively maintain the zone’s data
• Arrange for replicated name servers for the zone
Typically,zone data is maintained in a master file and
loaded into a primary (master) server
Replicated servers use TCP-based zone transfers specified
in DNS protocol to refresh their data
• A name server authoritative for a zone does not have
to be in that zone (great idea)
• A name server can handle any number of zones,
which don’t have to be contiguous
• Example: dig cnn.com.
cnn.com. 600 IN NS twdns-02.ns.aol.com
Caching
• Each name server aggressively caches
everything it can
• Only control on caching: TTL field
An expired TTL requires a fresh resolution
Each RR has its own TTL
• Low TTL values reduces inconsistencies,
allows for dynamic name-to-RR mappings
• Large TTL values reduce network and server
load
Example resolution
• Suppose you want to lookup A-record for
www.lcs.mit.edu. and nothing is cached
2 Root
Iterative server
resolution
3 .edu
Recursive Local DNS
server
resolution proxy
1 4
mit.edu
App 5 server
Stub resolver
lcs.mit.edu
server
Caching
• In reality, one almost never sees the chain of
request-response messages of previous slide
• NS records for labels higher up the tree usually have
long TTLs
• E.g., the google.com example from before
• But what about cnn.com?
cnn.com. 600 IN NS twdns-02.ns.aol.com
• Not a problem
twdns-02.ns.aol.com. 3600 IN A 152.163.239.216
ns.aol.com. 3553 IN NS dns-02.ns.aol.com.
• Cache not only positive answers, but also stuff that
does not exist
Communication protocol
• Normal request response uses a UDP-based
datagram protocol with retransmissions
• Retry timer is configurable, typically 4 or 8 seconds
• Often, retries are extremely persistent (many times)
• Use transaction ID field to disambiguate responses
• Key point: App using DNS is typically decoupled from
the DNS resolver making recursive queries!
• Zone transfers use TCP (bulk data, rather than RPC-
style comm.)
Definitions
• gethostbyname() is a lookup
• Local DNS server makes one or more queries
(recursive resolution)
• Each contacted server responds with a
response
• A response could be a referral, to go
someplace else
• A response that is not a referral is an answer
Performance study motivation
• How well does DNS work today?
Scalability
Robustness
Protocol
• Which of its mechanisms are actually useful?
Hierarchy
Caching
• DNS is being put to new uses: Is that likely to cause
problems?
Load-balancing
Content Distribution Networks
Suspicion
• DNS in WAN traffic traces
14% of all packets (estimate) in Danzig et al. 1990 8% in
1992
5% in NSFNET (1995)
3% in 1997 (MCI traces, 1997)
• But…
18% of all “flows” in 1997
1 out of 5 flows is a DNS flow???
• But yet, the DNS seems to work OK
Because of caching is traditional view
• Low-TTL bindings have important benefits
Load-balancing
Mobility
Analysis: Two Data Sets
• MIT: Jan 2000 (mit-jan00) & Dec 2000 (mit-dec00)
All DNS traffic at LCS/AI border and all TCP SYN/FIN/RST
Protocol analysis & cache simulations
• KAIST, Korea: May 2001 (kaist-may01)
All DNS traffic at border and some TCP SYN/FIN/RST
Protocol analysis & cache simulations
• Key insight: Joint analysis of DNS and its driving
workload (TCP connection) can help understand
what’s going on
MIT LCS/AI Topology
Subnet 1
Collection
machine
Subnet 2
External
network
Subnet 3
LCS/AI
Router
Subnet 24
KAIST Topology
Subnet 1
Collection
machine
Subnet 2
External
network
Subnet 3
ns1.kaist.ac.kr
ns2.kaist.ac.kr
External
Subnet N;
network
N > 100
Basic Trace Statistics
mit-jan00 mit-dec00 kaist-may01
Total lookups 2,530,430 4,160,954 4,339,473
Unanswered 23.5% 22.7% 20.1%
Answered with success 64.3% 63.6% 36.4%
Answered with failure 11.1% 13.1% 42.2%
Total query packets 6,039,582 10,617,796 5,326,527
TCP connections 3,619,173 4,623,761 6,337,269
#TCP:#valid “A” answers 7.3 4.9 7.8
Hit rate 86% 80% 87%
Why so many unanswered lookups?
Why so many failures?
Why so many query packets?
Why is hit rate not much higher than 80% and does it matter?
Unanswered lookups
• What’s the main reason for this large
fraction?
• Three syndromes
Zeroreferrals (5%-10%)
Non-zero referrals (13%-10%)
Loops (5%-3%)
Reason: Misconfigurations!
Many Lookups Elicit No Response
(MIT data)
• About 50% of the wide-area DNS packets are not necessary!
DNS Protocol
• 20-25% of all lookups are unresponded
• Of all answered requests, 99.9% had at most two
retransmissions
• Implementations retransmit every 4 or 8 secs
And they keep on going and going and going…
And becoming worse (more secondaries?)
• But about 20% of the unanswered lookups gave up
after ZERO retransmits!
More in the KAIST data
• This suggests schizophrenia!
• Solution: tightly bound number of retransmissions
Failure Responses
mit-jan00 mit-dec00 kaist-may01
Failed 11.1% 13.1% 42.2%
lookups
• NXDOMAIN and SERVFAIL are most common reasons
• Most common NXDOMAIN reason: Reverse (PTR) lookups
for mappings that don’t exist
Happens, e.g., because of access control or logging
mechanisms in servers
• Other reasons
Inappropriate name search paths
(foobar.com.lcs.mit.edu)
• Invalid queries: ld
• Negative caching ought to take care of this
Two Hacks
1. Use dig option to find BIND version
Main result: flood of email from disgruntled administrators
Hint: set up reverse DNS with a txt message explaining
what you’re doing
2. Send back-to-back a.b.c.com to name servers
• First one with recursion-desired bit, second not
• With –ve caching, second query would respond with
NXDOMAIN and not a referral
• Result: 90% of name servers appear to implement
negative caching
• NXDOMAIN lookups are heavy-tailed too!
Many for non-existent TLDs: loopback, workgroup,
cow
DNS Scalability Reasons
• DNS scales because of good NS-record caching,
which partitions the database
Alleviates load on root/gTLD servers
• Hierarchy is NOT the reasons for DNS scalability
The namespace is essentially flat in practice
• A-record caching is, to first-order, a non-contributor to
scalability
Make ‘em all 5 minutes (or less!) and things will be just fine
Large-scale sharing doesn’t improve hit-rates
NS-record caching is critical
• Substantially reduces DNS lookup latency
• Reduces root load by about 4-5X
Effectiveness of A-record Caching
• Cache sharing amongst clients
How much aggregation is really needed?
• Impact of TTL on caching effectiveness?
Is the move to low TTLs bad for caching?
• What does the cache hit rate depend on?
Name popularity distribution
Name TTL distribution
Inter-arrival distribution
• Methodology
Trace-driven simulation
DNS Caching: Locality of
References
Name popularity TTL distribution
• The top 10% account for more than 68% of • Shorter TTL names are more frequently
total answers accessed
• A long tail: 9.0% unique names • The fraction of accesses to short TTLs
Root queries regardless of caching scheme has greatly increased
Indicating increased deployment of DNS-
based server selection
Trace-driven Simulation
• Key insight: correlate DNS traffic with driving
TCP workload
• Parse traces to get:
Outgoing TCP SYNs per client to external
addresses
Databases containing
• IP-to-Name bindings
• Name-to-TTL bindings per simulated cache
Algorithm
1. Randomly divide the TCP clients into groups of size
S. Give each group a shared cache.
2. For each new TCP connection in the trace,
determine the group G and look for a name N in the
cache of group G.
3. If N exists and the cached TTL has not expired,
record a hit. Otherwise record a miss.
4. On a miss, make an entry in G’s cache for N, and
copy the TTL from the TTL DB to N’s cache entry
• Same name may have many IPs (handled)
• Same IP may have many names (ignored)
Effect of Sharing on Hit Rate
• 64% (s = 1) vs. 91% (s → 1000)
• Small s (10 or 20 clients per cache) are enough
Small # of very popular names
Each remaining name is of interest to only a tiny fraction of clients
Impact of TTL on Hit Rate
mit-dec00 kaist-may01
• Peg TTL to some value T in each simulation run; vary
T
• TTL of even 200s gives most of the benefit of
caching, showing that long-TTL A-record caching is
not critical
Bottom line
• The importance of TTL-based caching may have
been greatly exaggerated
NS-record caching is critical: reduces root & WAN load
Large TTLs for A-records aren’t critical to hit rates
• 10-min TTLs don’t add extra root or WAN load
• 0 TTL with client caching would only increase load by 2X
• The importance of hierarchy may have been greatly
exaggerated
Most of the name space is flat; resolved within 2 referrals
• What matters is partitioning of the distributed
database
• The DNS protocol would work better without all that
retransmit persistence
Other issues
• How does reverse name lookup work?
Trie data structure of numeric IP addresses
treated as part of the in-addr.arpa zone
• Dynamic updates?
DNS update spec standard now, in BIND 9
• Secure updates?
DNS updates need authentication (also std now)
• Attacks on DNS?
PS 3 question!
