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Chapter 1: Introduction
Ajay Kshemkalyani and Mukesh Singhal
Distributed Computing: Principles, Algorithms, and Systems
Cambridge University Press
A. Kshemkalyani and M. Singhal (Distributed Computing) Introduction CUP 2008 1 / 36
Distributed Computing: Principles, Algorithms, and Systems
Definition
Autonomous processors communicating over a communication network
Some characteristics
I No common physical clock
I No shared memory
I Geographical seperation
I Autonomy and heterogeneity
A. Kshemkalyani and M. Singhal (Distributed Computing) Introduction CUP 2008 2 / 36
Distributed Computing: Principles, Algorithms, and Systems
Distributed System Model
M memory bank(s)
P
P P P
P
PP
M M M
MM
M M
Communication network
(WAN/ LAN)
P processor(s)
Figure 1.1: A distributed system connects processors by a communication network.
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Distributed Computing: Principles, Algorithms, and Systems
Relation between Software Components
protocols
Operating
system
Distributed software
N
et
w
or
k 
pr
ot
oc
ol
 st
ac
k
Transport layer
Data link layer
Application layer
(middleware libraries)
Network layer
Distributed application Extent of
distributed
Figure 1.2: Interaction of the software components at each process.
A. Kshemkalyani and M. Singhal (Distributed Computing) Introduction CUP 2008 4 / 36
Distributed Computing: Principles, Algorithms, and Systems
Motivation for Distributed System
Inherently distributed computation
Resource sharing
Access to remote resources
Increased performance/cost ratio
Reliability
I availability, integrity, fault-tolerance
Scalability
Modularity and incremental expandability
A. Kshemkalyani and M. Singhal (Distributed Computing) Introduction CUP 2008 5 / 36
Distributed Computing: Principles, Algorithms, and Systems
Parallel Systems
Multiprocessor systems (direct access to shared memory, UMA model)
I Interconnection network - bus, multi-stage sweitch
I E.g., Omega, Butterfly, Clos, Shuffle-exchange networks
I Interconnection generation function, routing function
Multicomputer parallel systems (no direct access to shared memory, NUMA
model)
I bus, ring, mesh (w w/o wraparound), hypercube topologies
I E.g., NYU Ultracomputer, CM* Conneciton Machine, IBM Blue gene
Array processors (colocated, tightly coupled, common system clock)
I Niche market, e.g., DSP applications
A. Kshemkalyani and M. Singhal (Distributed Computing) Introduction CUP 2008 6 / 36
Distributed Computing: Principles, Algorithms, and Systems
UMA vs. NUMA Models
M memory
MP P P
PPP M
M M
MM
M M M M
PP P P
Interconnection network Interconnection network
(a) (b)
P processor
Figure 1.3: Two standard architectures for parallel systems. (a) Uniform memory
access (UMA) multiprocessor system. (b) Non-uniform memory access (NUMA)
multiprocessor. In both architectures, the processors may locally cache data from
memory.
A. Kshemkalyani and M. Singhal (Distributed Computing) Introduction CUP 2008 7 / 36
Distributed Computing: Principles, Algorithms, and Systems
Omega, Butterfly Interconnects
3−stage Butterfly network
101
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011
010
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P0
P1
P2
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(a) 3−stage Omega network (n=8, M=4) (b) (n=8, M=4)
Figure 1.4: Interconnection networks for shared memory multiprocessor systems.
(a) Omega network (b) Butterfly network.
A. Kshemkalyani and M. Singhal (Distributed Computing) Introduction CUP 2008 8 / 36
Distributed Computing: Principles, Algorithms, and Systems
Omega Network
n processors, n memory banks
log n stages: with n/2 switches of size 2x2 in each stage
Interconnection function: Output i of a stage connected to input j of next
stage:
j =
{
2i for 0 ≤ i ≤ n/2− 1
2i + 1− n for n/2 ≤ i ≤ n − 1
Routing function: in any stage s at any switch:
to route to dest. j ,
if s + 1th MSB of j = 0 then route on upper wire
else [s + 1th MSB of j = 1] then route on lower wire
A. Kshemkalyani and M. Singhal (Distributed Computing) Introduction CUP 2008 9 / 36
Distributed Computing: Principles, Algorithms, and Systems
Interconnection Topologies for Multiprocesors
1101
01100100
0000
0001
0010
0111
0011
0101
(b)(a)
processor + memory
1100
1000
1110
1010
1111
10111001
Figure 1.5: (a) 2-D Mesh with wraparound (a.k.a. torus) (b) 3-D hypercube
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Distributed Computing: Principles, Algorithms, and Systems
Flynn’s Taxonomy
(c) MISD
D D D
I I I
I II I
II
C C C C
P P P P P
D D
P
C C
I instruction stream
D
P
Control Unit
Processing Unit
data stream
(a) SIMD (b) MIMD
Figure 1.6: SIMD, MISD, and MIMD modes.
SISD: Single Instruction Stream Single Data Stream (traditional)
SIMD: Single Instruction Stream Multiple Data Stream
I scientific applicaitons, applications on large arrays
I vector processors, systolic arrays, Pentium/SSE, DSP chips
MISD: Multiple Instruciton Stream Single Data Stream
I E.g., visualization
MIMD: Multiple Instruction Stream Multiple Data Stream
I distributed systems, vast majority of parallel systems
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Distributed Computing: Principles, Algorithms, and Systems
Terminology
Coupling
I Interdependency/binding among modules, whether hardware or software (e.g.,
OS, middleware)
Parallelism: T (1)/T (n).
I Function of program and system
Concurrency of a program
I Measures productive CPU time vs. waiting for synchronization operations
Granularity of a program
I Amt. of computation vs. amt. of communication
I Fine-grained program suited for tightly-coupled system
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Distributed Computing: Principles, Algorithms, and Systems
Message-passing vs. Shared Memory
Emulating MP over SM:
I Partition shared address space
I Send/Receive emulated by writing/reading from special mailbox per pair of
processes
Emulating SM over MP:
I Model each shared object as a process
I Write to shared object emulated by sending message to owner process for the
object
I Read from shared object emulated by sending query to owner of shared object
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Distributed Computing: Principles, Algorithms, and Systems
Classification of Primitives (1)
Synchronous (send/receive)
I Handshake between sender and receiver
I Send completes when Receive completes
I Receive completes when data copied into buffer
Asynchronous (send)
I Control returns to process when data copied out of user-specified buffer
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Distributed Computing: Principles, Algorithms, and Systems
Classification of Primitives (2)
Blocking (send/receive)
I Control returns to invoking process after processing of primitive (whether sync
or async) completes
Nonblocking (send/receive)
I Control returns to process immediately after invocation
I Send: even before data copied out of user buffer
I Receive: even before data may have arrived from sender
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Distributed Computing: Principles, Algorithms,and Systems
Non-blocking Primitive
Send(X, destination, handlek) //handlek is a return parameter
...
...
Wait(handle1, handle2, . . . , handlek , . . . , handlem) //Wait always blocks
Figure 1.7: A nonblocking send primitive. When the Wait call returns, at least
one of its parameters is posted.
Return parameter returns a system-generated handle
I Use later to check for status of completion of call
I Keep checking (loop or periodically) if handle has been posted
I Issue Wait(handle1, handle2, . . .) call with list of handles
I Wait call blocks until one of the stipulated handles is posted
A. Kshemkalyani and M. Singhal (Distributed Computing) Introduction CUP 2008 16 / 36
Distributed Computing: Principles, Algorithms, and Systems
Blocking/nonblocking; Synchronous/asynchronous;
send/receive primities
S
Send
The completion of the previously initiated nonblocking operation
duration in which the process issuing send or receive primitive is blocked
 primitive issued
 
 primitive issuedReceive
Send
(c) blocking async. Send (d) nonblocking async. Send
S
W
R_C
P, S_C
R_CP,
(b) nonblocking sync. Send, nonblocking Receive (a) blocking sync. Send, blocking Receive
P,S_C
P
R R_C
S_C
processing for completesReceive
Process may issue to check completion of nonblocking operationWait
duration to copy data from or to user buffer
processing for completes
S S_Cprocess i
buffer_i
kernel_i
process j
buffer_j
kernel_j
S W W
W WRR
process i
buffer_i
kernel_i
S S_C WW
Figure 1.8:Illustration of 4 send and 2 receive primitives
A. Kshemkalyani and M. Singhal (Distributed Computing) Introduction CUP 2008 17 / 36
Distributed Computing: Principles, Algorithms, and Systems
Asynchronous Executions; Mesage-passing System
internal event send event receive event
P
P
P
P
0
1
2
3
m4
m1 m7
m3 m5
m6m2
Figure 1.9: Asynchronous execution in a message-passing system
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Distributed Computing: Principles, Algorithms, and Systems
Synchronous Executions: Message-passing System
round 3
P
P
P
P
3
2
0
1
round 1 round 2
Figure 1.10: Synchronous execution in a message-passing system
In any round/step/phase: (send | internal)∗(receive | internal)∗
(1) Sync Execution(int k, n) //k rounds, n processes.
(2) for r = 1 to k do
(3) proc i sends msg to (i + 1) mod n and (i − 1) mod n;
(4) each proc i receives msg from (i + 1) mod n and (i − 1) mod n;
(5) compute app-specific function on received values.
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Distributed Computing: Principles, Algorithms, and Systems
Synchronous vs. Asynchronous Executions (1)
Sync vs async processors; Sync vs async primitives
Sync vs async executions
Async execution
I No processor synchrony, no bound on drift rate of clocks
I Message delays finite but unbounded
I No bound on time for a step at a process
Sync execution
I Processors are synchronized; clock drift rate bounded
I Message delivery occurs in one logical step/round
I Known upper bound on time to execute a step at a process
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Distributed Computing: Principles, Algorithms, and Systems
Synchronous vs. Asynchronous Executions (2)
Difficult to build a truly synchronous system; can simulate this abstraction
Virtual synchrony:
I async execution, processes synchronize as per application requirement;
I execute in rounds/steps
Emulations:
I Async program on sync system: trivial (A is special case of S)
I Sync program on async system: tool called synchronizer
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Distributed Computing: Principles, Algorithms, and Systems
System Emulations
S−>A
Asynchronous 
message−passing (AMP)
Synchronous 
shared memory (ASM)
Asynchronous Synchronous
shared memory (SSM)
message−passing (SMP)
SM−>MPMP−>SMSM−>MPMP−>SM
A−>S
S−>A
A−>S
Figure 1.11: Sync ↔ async, and shared memory ↔ msg-passing emulations
Assumption: failure-free system
System A emulated by system B:
I If not solvable in B, not solvable in A
I If solvable in A, solvable in B
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Distributed Computing: Principles, Algorithms, and Systems
Challenges: System Perspective (1)
Communication mechanisms: E.g., Remote Procedure Call (RPC), remote
object invocation (ROI), message-oriented vs. stream-oriented
communication
Processes: Code migration, process/thread management at clients and
servers, design of software and mobile agents
Naming: Easy to use identifiers needed to locate resources and processes
transparently and scalably
Synchronization
Data storage and access
I Schemes for data storage, search, and lookup should be fast and scalable
across network
I Revisit file system design
Consistency and replication
I Replication for fast access, scalability, avoid bottlenecks
I Require consistency management among replicas
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Distributed Computing: Principles, Algorithms, and Systems
Challenges: System Perspective (2)
Fault-tolerance: correct and efficient operation despite link, node, process
failures
Distributed systems security
I Secure channels, access control, key management (key generation and key
distribution), authorization, secure group management
Scalability and modularity of algorithms, data, services
Some experimental systems: Globe, Globus, Grid
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Distributed Computing: Principles, Algorithms, and Systems
Challenges: System Perspective (3)
API for communications, services: ease of use
Transparency: hiding implementation policies from user
I Access: hide differences in data rep across systems, provide uniform operations
to access resources
I Location: locations of resources are transparent
I Migration: relocate resources without renaming
I Relocation: relocate resources as they are being accessed
I Replication: hide replication from the users
I Concurrency: mask the use of shared resources
I Failure: reliable and fault-tolerant operation
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Distributed Computing: Principles, Algorithms, and Systems
Challenges: Algorithm/Design (1)
Useful execution models and frameworks: to reason with and design correct
distributed programs
I Interleaving model
I Partial order model
I Input/Output automata
I Temporal Logic of Actions
Dynamic distributed graph algorithms and routing algorithms
I System topology: distributed graph, with only local neighborhood knowledge
I Graph algorithms: building blocks for group communication, data
dissemination, object location
I Algorithms need to deal with dynamically changing graphs
I Algorithm efficiency: also impacts resource consumption, latency, traffic,
congestion
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Distributed Computing: Principles, Algorithms, and Systems
Challenges: Algorithm/Design (2)
Time and global state
I 3D space, 1D time
I Physical time (clock) accuracy
I Logical time captures inter-process dependencies and tracks relative time
progression
I Global state observation: inherent distributed nature of system
I Concurrency measures: concurrency depends on program logic, execution
speeds within logical threads, communication speeds
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Distributed Computing: Principles, Algorithms, and Systems
Challenges: Algorithm/Design (3)
Synchronization/coordination mechanisms
I Physical clock synchronization: hardware drift needs correction
I Leader election: select a distinguished process, due to inherent symmetry
I Mutual exclusion: coordinate access to critical resources
I Distributed deadlock detection and resolution: need to observe global state;
avoid duplicate detection, unnecessary aborts
I Termination detection: global state of quiescence; no CPU processing and no
in-transit messages
I Garbage collection: Reclaim objects no longer pointed to by any process
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Distributed Computing: Principles, Algorithms, and Systems
Challenges: Algorithm/Design (4)
Group communication, multicast, and ordered message delivery
I Group: processes sharing a context, collaborating
I Multiple joins, leaves, fails
I Concurrent sends: semantics of delivery order
Monitoring distributed events and predicates
I Predicate: condition on global system state
I Debugging, environmental sensing, industrial process control, analyzing event
streams
Distributed program design and verification tools
Debugging distributed programs
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Distributed Computing: Principles, Algorithms, and Systems
Challenges: Algorithm/Design (5)
Data replication, consistency models, and caching
I Fast, scalable access;
I coordinate replica updates;
I optimize replica placement
World Wide Web design: caching, searching, scheduling
I Global scale distributed system; end-users
I Read-intensive; prefetching over caching
I Object search and navigation are resource-intensive
I User-perceived latency
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Distributed Computing: Principles, Algorithms, and Systems
Challenges: Algorithm/Design (6)
Distributed shared memory abstraction
I Wait-free algorithm design: process completes execution, irrespective of
actions of other processes, i.e., n − 1 fault-resilience
I Mutual exclusion
F Bakery algorithm, semaphores, based on atomic hardware primitives, fast
algorithms when contention-free access
I Register constructions
F Revisit assumptions about memory access
F What behavior under concurrent unrestricted access to memory?
Foundation for future architectures, decoupled with technology (semiconductor,
biocomputing, quantum . . .)
I Consistency models:
F coherence versus access cost trade-off
F Weaker models than strict consistency of uniprocessors
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Distributed Computing: Principles, Algorithms, and Systems
Challenges: Algorithm/Design (7)
Reliable and fault-tolerant distributed systems
I Consensus algorithms: processes reach agreement in spite of faults (under
various fault models)
I Replication and replica management
I Voting and quorum systems
I Distributed databases, commit: ACID properties
I Self-stabilizing systems: ”illegal” system state changes to ”legal” state;
requires built-in redundancy
I Checkpointing and recovery algorithms: roll back and restart from earlier
”saved” state
I Failure detectors:
F Difficult to distinguish a ”slow” process/message from a failed process/ never
sent message
F algorithms that ”suspect” a process as having failed and converge on a
determination of its up/down status
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Distributed Computing: Principles, Algorithms, and Systems
Challenges: Algorithm/Design (8)
Load balancing: to reduce latency, increase throughput, dynamically. E.g.,
server farms
I Computation migration: relocate processes to redistribute workload
I Data migration: move data, based on access patterns
I Distributed scheduling: across processors
Real-time scheduling: difficult without global view, network delays make task
harder
Performance modeling and analysis: Network latency to access resources
must be reduced
I Metrics: theoretical measures for algorithms, practical measures for systems
I Measurement methodologies and tools
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Distributed Computing: Principles, Algorithms, and Systems
Applications and Emerging Challenges (1)
Mobile systems
I Wireless communication: unit disk model; broadcast medium (MAC), power
management etc.
I CS perspective: routing, location management, channel allocation, localization
and position estimation, mobility management
I Base station model (cellular model)
I Ad-hoc network model (rich in distributed graph theory problems)
Sensor networks: Processor with electro-mechanical interface
Ubiquitous or pervasive computing
I Processors embedded in and seamlessly pervading environment
I Wireless sensor and actuator mechanisms; self-organizing; network-centric,
resource-constrained
I E.g., intelligent home, smart workplace
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Distributed Computing: Principles, Algorithms, and Systems
Applications and Emerging Challenges (2)
Peer-to-peer computing
I No hierarchy; symmetric role; self-organizing; efficient object storage and
lookup;scalable; dynamic reconfig
Publish/subscribe, content distribution
I Filtering information to extract that of interest
Distributed agents
I Processes that move and cooperate to perform specific tasks; coordination,
controlling mobility, software design and interfaces
Distributed data mining
I Extract patterns/trends of interest
I Data not available in a single repository
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Distributed Computing: Principles, Algorithms, and Systems
Applications and Emerging Challenges (3)
Grid computing
I Grid of shared computing resources; use idle CPU cycles
I Issues: scheduling, QOS guarantees, security of machines and jobs
Security
I Confidentiality, authentication, availability in a distributed setting
I Manage wireless, peer-to-peer, grid environments
F Issues: e.g., Lack of trust, broadcast media, resource-constrained, lack of
structure
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	Distributed Computing: Principles, Algorithms, and Systems