Introduction to Distributed Systems and Distributed Programming

Distributed Programming

Lecture 01 - Introduction to Distributed Systems

and Distributed Programming

Edirlei Soares de Lima

<edirlei.lima@universidadeeuropeia.pt>

What is Distributed Programming?

•

Distributed computing is a field of

computer science that studies

distributed systems.

A distributed system is a system

whose components are located on

different networked computers,

which then communicate and

coordinate their actions by passing

messages to each other.

•

Distributed programming involves

the implementation of distributed

systems.

Distributed Programming – Concurrency

•

Process vs. Thread:

–

A process runs in its own address space (managed

by the OS), while a thread runs within the address

space of a single process and its managed by the

process.

Parallel vs. Concurrent:

–

Parallel refers usually to cases where two

computation are taking place physically

independently of each other (e.g.: in two different

machines, or in two different processors) while

concurrent is an abstraction that allows us to have

apparent parallelism even in the cases of one

processor.

Concurrent Programming

•

Main challenge: synchronize the execution of different

processes and enable them to communicate with each other.

Type of systems:

–

Multitasking System: concurrent execution of multiple processes in a

single CPU.

Multiprocessor Systems: parallel execution of multiple processes in

multiple CPUs.

Distributed Systems: parallel execution of multiple processes in

multiple computers connected through a network.

Multitasking Systems

•

Concurrency is based on interleaving

instructions from different processes

on a single CPU.

Multitasking does not require parallel

execution of multiple tasks at exactly

the same time; instead, it allows more

than one task to advance over a given

period of time.

Multiprocessor Systems

•

Parallelism is achieved by executing processes in multiple

CPUs on the same computer.

Each processor has access to both local memory and global

memory.

Distributed Systems

•

Parallelism is achieved by executing processes in multiple

computers that are connected through a network.

Distributed Systems – Examples

•

Web Services: WEB (HTTP), E-Mail (SMTP), VoIP, Instant

Messaging, …

Distributed Systems – Examples

•

Cloud Computing: Google Docs, Google Drive, Dropbox,

Amazon Web Services, …

Distributed Systems – Examples

•

MMORPGs: require fast response times and real-time

propagation of events.

Multiplayer Games – History

•

Empire (1973): turn-based strategy game

with support for networked multiplayer.

Maze War (1973): networked multiplayer

first-person shooter.

Both games were designed run on small

networks composed of mainframe

computers (PLATO system).

Multiplayer Games – History

•

Doom (1993) was the progenitor of the modern networked

games.

–

The first-person shooter supported up to four players in a single

game session (in a local area network – LAN), with the option to play

cooperatively or competitively.

Multiplayer Games – History

•

Quake (1996) allowed players to

connect to a server (which may be a

dedicated machine or on one of the

player's computers), where they

could either play cooperatively or

competitively.

Unreal (1998) followed the same

model of Quake with a multiplayer

mode that allowed up to 16 players

to play over the Internet.

Multiplayer Games – History

•

Ultima Online (1997) was one of

the first persistent MMORPGs.

EverQuest (1999) was the first

commercially successful MMORPG

to employ a three-dimensional

game engine.

•

World of Warcraft (2004) is one of

the most successful MMORPGs,

with a peak of 12 million

subscriptions in 2010.

Why are Distributed Systems Needed?

•

General access without location restrictions (e.g.: banking

network, games);

•

Sharing resources across many users (hardware and

software);

Load balancing (e.g.: distributing game players across many

servers instead of overloading a single server);

Fault tolerance (when a fraction of the processors fail, the

remaining processes can take over the tasks and keep the

application running);

•

Flexibility and adaptability by decomposing a global system

into smaller (and simpler) systems;

Distributed Systems – Challenges

•

Heterogeneity: hardware, operating systems, programming

languages, …

The knowledge of a process is local: no process is expected to

have global knowledge about either the network topology or

the global state.

•

Communication, cooperation and synchronization between

processes is done through message exchange.

The handling of failures is an important and complex part of a

distributed system.

Scalability: a distributed system is considered scalable when

its performance is not influenced by the final scale of the

system or the number of users.

Distributed Computing – Common Problems

•

Leader election: when a number of processes cooperate with

one another for solving a problem, many implementations

prefer to elect one of them as the leader and the remaining

processes as followers. If the leader crashes, then one of the

followers is elected the leader.

Distributed Computing – Common Problems

•

Mutual exclusion: when the access to a resource or shared

data is critical, it is necessary to guarantee that only one

process will acquire the resource or perform critical

operations on a shared data at any time.

Distributed Computing – Common Problems

•

Multicasting: sending of a given data to multiple processes in

a distributed system is a common subtask in many

applications. As an example, in group communication, one

may want to send some breaking news to millions of

members as quickly as possible.

Replica management: to support fault tolerance and improve

system availability, the use of process replicas is quite

common. When the main server is down, one of the replica

servers replaces the main server.