chapter 5 naming - løbner.dkkurser.lobner.dk/ddist/3. naming (1).pdf · tanenbaum & van steen,...

Tanenbaum & Van Steen, Distributed Systems: Principles and Paradigms, 2e, (c) 2007 Prentice-Hall, Inc. All rights reserved. 0-13-239227-5

DISTRIBUTED SYSTEMS Principles and Paradigms

Second Edition ANDREW S. TANENBAUM

MAARTEN VAN STEEN

Chapter 5 Naming


Plan

•  Definitions and uses •  Types of naming

– Flat naming – Structured naming – Attribute-based naming


Definitions •  Name

–  A string of bits or characters that is used to refer to an entity

•  Address –  A name that refers to an access point of an entity

•  Identifiers –  A name that uniquely identifies an entity

•  Human-friendly names –  A character string name that is understandable by a human


Naming

•  Naming is fundamental in distributed systems

•  Is it really?


What can we do without naming?

•  Implies finite capacity of nodes in distributed systems –  Otherwise we could assign an identifier (and a name)

•  We could do broadcast or anycast –  Broadcast traffic-heavy –  Anycast equivalent to mobile computing

•  A mobile computing example –  Population protocols


A Motivating Example: Birds •  Strap tiny, identical sensors

to many birds in a flock. •  Sensors on two birds can

interact when the birds are close together.

•  Want to detect when (at least) five birds have elevated body temperatures, indicating possible epidemic.

•  (Material from Eric Ruppert)


Population Protocols: System Model

•  Sophistication of mobile nodes –  Identically programmed –  Finite state machines

•  Infrastructure –  None; not even identities

•  Synchrony –  Totally asynchronous –  No limit on time it takes for a message to arrive

•  Communication range –  When nodes get next to each other they may

communicate –  Anything that is always possible happens eventually

(fairness)


Protocols •  A protocol consists of

–  a finite set of states –  transition rules, mapping pairs of states to pairs of

states –  input encoding

•  mapping from inputs to states –  output interpretation

•  mapping from states to outputs

•  Remark –  Protocol must be independent of size of population!


Simplest Example: Computing OR of Input Bits

•  States –  {0, 1}.

•  One transition rule –  0, 1 → 1, 1.

•  Input to a node is its state •  Output of a node is its state

•  Result –  If all inputs are 0, all nodes will remain in state 0 –  If some node has input 1, eventually all will have state 1


Example: Threshold Predicate •  Suppose each agent starts with input 0 or 1.

•  Want to determine whether at least five nodes have input 1.

•  Output convention: Each state has an associated output.

•  Eventually, all nodes reach states with the correct output.

•  What does a protocol for this look like? –  (This was the problem of detecting bird flu epidemic.)


Example: Threshold Predicate

•  States –  {0, 1, 2, 3, 4, 5}

•  Transition rules – x, y → 0, x+y (for x+y < 5) – x, y → 0, 5 (for x+y => 5) – x, 5 → 5, 5

•  Can we at any point in time be sure whether there is no bird flu?


Example: Majority

•  Every node is initially red or blue.

•  Determine whether # reds > # blues


Example: Majority •  States:

–  {red, blue, yes, no}.

•  Rules: –  red, blue → no, no –  red, no → red, yes –  blue, yes → blue, no –  yes, no → no, no

–  eliminates all blues or all reds –  red changes answers to yes –  blue changes answers to no –  takes care of a tie


Example: Majority

•  Is this execution a problem? – No, because of fairness we do not have an

infinite cycle


More Generally: Predicates •  A predicate has yes/no output. •  Assume every agent should eventually produce

correct output

•  Predicate must be symmetric (order of inputs is unimportant).

•  So, we can write predicate as P(x1, x2, . . . , xk) where –  k = number of possible initial states, –  xi = number of agents starting in ith state.


Limits of Anonymity

•  Theorem: A predicate is computable iff it is on the following list –  where a, ci’s are integer constants

–  where a, b and ci’s are constants

–  Boolean combinations of the above predicates

•  How do the two examples map to this? –  Bird flu? –  Majority of red?


Back to Names… •  How do we map names to addresses so that we

can refer to entities?

•  Naming system –  Maintains a name-to-address binding –  E.g., www.daimi.au.dk -> IP no. 130.225.16.54

•  Operation depends on naming type –  Flat –  Structured –  Attribute-based


Flat Names

•  Identifiers are often just “random” strings of bits

•  Implies no information on location embedded in identifier – E.g., identifiers in Chord on the application

layer – E.g., Media Access Control (MAC) adresses

on the data link layer


ARP

•  My IP address is currently 10.198.5.65 – My physical/MAC address for my wireless

network card is 00:1f:f3:ba:59:7c – How to map my IP address to my MAC

address? •  Address Resolution Protocol (ARP)

– A machine broadcasts a packet on the local network

– Receivers check whether they are listening to the IP address


An ARP Scenario


An ARP Scenario

•  (Caching not shown)


ARP

•  Broadcast is inefficient for large networks

•  Multicast may be used also in point-to-point networks – But rarely enabled


Mobility

•  Problem – Name/identifier/address constant, location

changes •  Solutions

– Multicast groups could be used with ARP-like protocol •  Node multicasts new location to group when it has

moved – Forwarding pointers – Home-based approaches


Forwarding Pointers

•  Figure 5-1. The principle of forwarding pointers using (client stub, server stub) pairs.


Forwarding Pointers

•  Figure 5-2. Redirecting a forwarding pointer by storing a shortcut in a client stub.


Forwarding Pointers

•  Transparency as advantage – Specifically migration transparency

•  Disadvantages – Chain may grow very large –  Intermediary nodes may need to maintain

links for a long time – Multiple-points-of-failure


Home-Based Approaches •  Node may want to maintain address while it

moves –  Fits well with, e.g., high mobility and DNS (more later)

•  Mobile IP –  Home address

•  The address of the mobile node –  Home agent

•  Keeps current location information of the node •  Tunnels datagrams to mobile node

–  Care-of-address •  Termination point of the tunnel to a mobile node

•  Part of network/IP layer in IPv6


Home-Based Approaches

•  Figure 5-3. The principle of Mobile IP.


Home-Based Approaches

•  Disadvantages – Single-point-of-failure –  Increased communication latency for first

packets


Chord Revisited •  Distributed Hash Table

–  Use hash function to map nodes and keys to an m-bit identifier •  E.g., 160 bit from using SHA-1

–  Each node should store keys (and values) for which its identifier is closest

•  Store roughly K/N keys –  Store and lookup key/value pairs whose identifier is close to key

•  Lookup in O(log N) time

•  Consequences –  Load balancing –  Scalability –  Decentralization –  Availability


Definitions

•  Definitions of state for node n, using m-bit identifiers


A Chord Network


Simple Lookup

// forward the query around the circle


Scalable Lookup


Distributed Hash Tables General Mechanism

•  Figure 5-4. Resolving key 26 from node 1 and key 12 from node 28 in a Chord system.


Lookup Complexity

•  Time is O(log N) (with high probability) – 

•  Otherwise id would not be between finger[k] and finger[k+1]

– 

–  After log N forwardings, distance will be at most

•  1 node expected in that interval

n

id

finger[k]

finger[k+1]

€

id − finger[k] ≤ 2k−1

€

id − n = finger[k]− n + id − finger[k] ≥ 2k−1 + id − finger[k]> 2 ⋅ (id − finger[k])

€

2m /2logN = 2m /N


Dynamic Operations •  p joining a network from node

n –  n’ := n.lookup(p) –  p.predecessor := n’ –  p.successor := n’.successor

–  p.finger[1] := n’.finger[2] –  … –  p.finger[m] := n.lookup( )

–  Copy data as necessary from n’ •  Rest of the network does not

learn about p from joining

n

n’ p


Dynamic Operations

•  Run n.stabilize() occasionally – Check whether n.successor.predecessor == n – Update n and n.successor as necessary

•  Two cases of nodes leaving – p.leave() – Failure

•  Correctness depends on correctness of successor pointers

•  Maintain list of successors •  Modify stabilize() to handle this


Chord and Multicasting

•  Generate identifier for group –  mid

•  Find multicast root –  root := lookup(mid)

•  Nodes join group –  Find forwarders throughout

lookup(mid) –  Forwarder notes children

•  Multicast –  lookup(mid) + data


Chord and Multicasting •  Example

–  mid = 13 –  root = 11

•  4.join(13) –  ”4.lookup(13)” –  9 becomes forwarder

•  1.join(13) –  ”1.lookup(13)” –  No need to send join

request all the way to 11

•  n.mcast(13) –  n.lookup(13) –  Send data from 11 to

nodes in tree

Example multicast tree


Hierarchical Approaches •  Network is divided into domains

–  Single top-level/root domain –  Multiple non-overlapping subdomains –  Leaf-domains

•  Each domain has associated directory node


Hierarchical Approaches: Globe

•  Directory nodes have location records for their contents –  In leaf nodes this is an address –  In other nodes this is pointers



•  Look up the domain containing E using expanding ring search –  Follow pointers from directory of that domain until addresses for E are

found



•  Figure 5-8. (a) An insert request is forwarded to the first node that knows about entity E.



•  Figure 5-8. (b) A chain of forwarding pointers to the leaf node is created.


Summary

•  Naming is fundamental to distributed systems

•  Different types of names may be used – Flat naming

•  E.g., DHT

– Structured naming •  E.g, DNS

– Attribute-based naming •  E.g., LDAP

chapter 5 naming - løbner.dkkurser.lobner.dk/ddist/3. naming (1).pdf · tanenbaum & van steen,...

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