Introduction

Systèmes et Applications
Reparties
Dr DIALLO Mohamed
UFRMI 2016
diallo.med@gmail.com
1

Objectifs du cours
• Comprendre les challenges dans un système repartis
• Se familiariser avec la mise en œuvre de systèmes repartis
• Découvrir l’algorithmique repartie
• Etudier des exemples de systèmes distribues
• Explorer la recherche dans les systèmes distribues
L'éducation est l'allumage d'une flamme,
et non pas le remplissage d'un navire.
(Socrate)
2

Présentation de l’UE
• Huit séances de 4h
• CM - 10h - TD 10h – TP 8h
• Expose - 4h
• Evaluation
• Projet: Présentation d’un papier
de recherche ou d’un système
distribue (DEMO) en binôme.
• Examen sur table
• Introduction
• Communication
• Socket et RMI
• Algorithmique distribuée
• Synchronisation
• Election
• Exclusion
• Tolérance aux pannes et P2P
• Services web
3

Définition
A distributed system is a collection of independent computers that
appears to its users as a single coherent system. (A. Tanenbaum)
Un système réparti :
• Des sites indépendants avec un but commun
• Un système de communication
A distributed system is one that stops you from getting any work done
when a machine you’re never heard of crashes (L. Lamport)
Crédit C. Rabat – Introduction aux systèmes repartis
4

Characteristics of distributed systems
• Each node executes a program concurrently
• Knowledge is local
• Nodes have fast access only to their local state, and any information about global
state is potentially out of date
• Nodes can fail and recover from failure independently
• Messages can be delayed or lost
• Independent of node failure;
• it is not easy to distinguish network failure and node failure
• Clocks are not synchronized across nodes
• local timestamps do not correspond to the global real time order, which cannot be
easily observed
Distributed Systems for fun and profit - book.mixu.net/distsys/ebook.html5

Fallacies of distributed computing
• The network is reliable.
• Redundancy / Reliable messaging
• Latency is zero.
• Strive to make as few as possible calls / Move
as much data in each call
• Bandwidth is infinite.
• Strive to limit the size of the information we
send over the wire
• The network is secure.
• Assess risks
• Be aware of security and implications
• Topology doesn't change.
• Do not depend on specific routes/addresses
• Location transparency (ESB, multicast) / Directory
services
• There is one administrator.
• Different agendas / rules that can constrain your
app
• Help them manage your app.
• Transport cost is zero.
• Overhead (Marshalling…)
• Costs for running the network
• The network is homogeneous
• Do not rely on proprietary protocols, rather
XML…
Arnon Rotem - Fallacies of Distributed Computing Explained 6

Sample distributed system :
The Google cluster architecture (2003)
• Scale
• Raw documents (tens of terabytes of
data)
• Inverted index (#terabyte)
• Approach
• Partitioning and replication (load
balancing)
Combining more than 15,000 commodity-class PCs with
fault-tolerant software creates a solution that is more
cost-effective than a comparable system built out of a
smaller number of high-end servers
7

Real Facts
Lots of Data out there
• NYSE generates 1TB/day
• Google processes 700PB/month
• Facebook hosts 10 billion photos
taking 1PB of storage
Google search workloads
• Google now processes over
40,000 search queries every
second on average.
• A single Google query uses 1,000
computers in 0.2 seconds to
retrieve an answer
Snia.org http://www.internetlivestats.com/google-search-statistics/
8

Objectifs des systèmes repartis
• Accès aux ressources
• Transparence
• Passage à l’échelle
(Scalability)
• Tolérance aux pannes
•Fiabilité (Reliability)
•Ouverture
(Interoperability)
• Sécurité
Crédit C. Rabat – Introduction aux systèmes repartis
9

Transparence
Transparency Description
Access Hide differences in data representation and how a resource is
accessed
Location Hide where a resource is located
Migration Hide that a resource may me moved to another location
Relocation Hide that a resource may me moved to another location while in
use
Replication Hide that a resource is replicated
Concurrency Hide that a resource may be shared by several competitive users
Failure Hide the failure and recovery of a resource
Credit A. Tanenbaum
10

Scalability
• Size scalability
• Adding more nodes should make the system linearly faster;
• Growing the dataset should not increase latency
• Geographic scalability
• Administrative scalability
• Adding more nodes should not increase the administrative costs of the
system
A scalable system is one that continues to meet the needs of its users
as scale increases

Scalability: Performance
• Short response time/low latency for a given piece of work
• High throughput (rate of processing work)
• Low utilization of computing resource(s)

Scalability: Availability (and Fault tolerance)
Distributed systems can take a bunch of unreliable components, and
build a reliable system on top of them (Design for fault tolerance)
Because the probability of a failure occurring increases with the
number of components, the system should be able to compensate so
as to not become less reliable as the number of components increases.
Fault tolerance
Ability of a system to behave in a well-defined manner once faults
occur

Scale out vs Scale up ?
Distributed Systems for fun and profit - book.mixu.net/distsys/ebook.html
High-end (128 core) – low-end (4 core)
14

Service Level Agreement
• If I write data, how quickly can I access it elsewhere?
• After the data is written, what guarantees do I have of
durability?
• If I ask the system to run a computation, how quickly will it
return results?
• When components fail, or are taken out of operation, what
impact will this have on the system?

Consequences of distribution
• An increase in the number of independent nodes increases the
probability of failure in a system
• Reducing availability and increasing administrative costs
• An increase in the number of independent nodes may increase the
need for communication between nodes
• Reducing performance as scale increases
• An increase in geographic distance increases the minimum latency for
communication between distant nodes
• Reducing performance for certain operations

Théorie des systèmes repartis
• Efficient solutions to specific
problems .
• Guidance about what is possible.
• Minimum cost of a correct
implementation.
• What is impossible.
• Timestamping distributed
events. (Lamport)
• Leader election
• Consistent snapshoting
• Consensus is impossible to solve
in fewer than 2 rounds of
messages in general
• CAP theorem
• FLP impossibility
• Two Generals problem

FLP impossibility result
• Validity: the value agreed upon must have
been proposed by some process – safety
• Agreement: all deciding processes agree on
the same value - safety
• Termination: at least one non-faulty process
eventually decides - liveness
Consensus is the problem of having
a set of processes agree on a value
proposed by one of those
processes.
18

FLP impossibility result
In an asynchronous setting,
where only one processor
might crash, there is no
distributed algorithm that
solves the consensus problem
Fischer, M. J., Lynch, N. A., &
Paterson, M. S. (1985).
Impossibility of distributed
consensus with one faulty
process. Journal of the ACM
(JACM), 32(2), 374-382.
19

CAP Theorem (Brewer Theorem)
Partition tolerance
The system continues to operate despite
arbitrary partitioning due to network failures
Consistency
Every read receives the most recent write or
an error
Availability
Every request receives a response, without
guarantee that it contains the most recent
version of the information
http://book.mixu.net/distsys/abstractions.html 20

Beware !
C in ACID
• If the system has certain
invariants that must always hold,
if they held before the
transaction, they will hold
afterward too.
(Example: law of conservation of money)
• In distributed systems : when
transactions run concurrently,
the result is the same as if it
runs in serial.
C in CAP
• Relates to data updates
spreading accross all replicas in a
cluster.
• How operations on a single item
are ordered, and made visible to
all nodes of the database.
21

Technologies pour les systèmes repartis
• Intergiciels (Corba, ESB)
• RPC, RMI, Web services
• Amazon Dynamo / Apache Cassandra
• Apache Hadoop
22

Amazon Dynamo: Highly available NoSQL
• A highly available key-value
storage system that some of
Amazon’s core services use to
provide an “always-on”
experience.
• To achieve this level of availability,
Dynamo sacrifices consistency
under certain failure scenarios.
Giuseppe DeCandia, et al, “Dynamo: Amazon's
Highly Available Key-Value Store”, in the
Proceedings of the 21st ACM Symposium on
Operating Systems Principles, Stevenson, WA,
October 2007.
23

Hadoop: Distributed framework for Big Data.
• Apache top level project, open-
source implementation of
frameworks for reliable,
scalable, distributed computing
and data storage.
• It is a flexible and highly-
available architecture for large
scale computation and data
processing on a network of
commodity hardware.
• Hadoop fractionne les fichiers en
gros blocs et les distribue à
travers les nœuds du cluster.
• Pour traiter les données,
Hadoop transfère le code à
chaque nœud et chaque nœud
traite les données dont il dispose
24

Apache Hadoop
• Hadoop Usage scenarios
• Search through data looking
for particular patterns.
• Sort large amount of data
(#Terabytes)
25

Enterprise Service Bus
• Middleware oriente message
• Echange de message asynchrone
• Services web (SOA)
• Transformations
• Routage intelligent
• Découplage expéditeur et
destinataire
• Business activity monitoring (BAM)
• Business process modeling (BPM)
• Mule ESB
• Talend ESB
Wikipedia.fr27

Service Oriented Architecture
28

Modèles fonctionnels
Deux/Trois/N-tiers
29

Modèles d’échange
Client/serveur
Communication par message
Code mobile
Mémoire partagée
33

Modèle client/serveur (1/2)
34

Modèle client/serveur (2/2)
35

Communication par message
• Pas de réponse attendue
• Messages non sollicites
• Exemple: Message Oriented Middleware.
• Point-a-point
• Publish-Subscribe
(Apache ActiveMQ, IBM Websphere MQ, OpenJMS)
36

Mémoire virtuelle partagée
• Les différentes applications partagent une zone mémoire commune.
• Applications parallèles: thread
• Application distribuée: intergiciel
38

Configurations
Centralise
Totalement décentralise
Hybride
39

Centralise
40
! Un système peut être
centralise mais
distribue.

Totalement décentralisée
1. No machine has complete information
about the system state.
2. Machines make decisions based only
on local information,
3. Failure of one machine does not ruin
the algorithm/system.
4. There is no implicit assumption that a
global clock exists (no strong
coordination).
(Credit A. Tanenbaum)
41
• Symétrie
• Autonomie (administrative)
• Fédération

Hiérarchique
i.e. DNS
Exemple de système
décentralisé mais:
• Serveurs racines
• Serveurs TLD
• Serveurs autorités
42

Hybride
43
i.e. Kazaa
Système décentralisé
Mais Peers vs Super-peers

Cloud et Virtualisation
Cloud computing
Virtualisation
44

Environnement Cloud
45
Community: the members
of the community generally
share similar security,
privacy, performance and
compliance requirements.
Credit Bamba Gueye - UCAD

Modèles d’utilisation
SaaS : c’est la plateforme applicative mettant à disposition des applications
complètes fournies à la demande. On y trouve différents types d'application
allant du CRM, à la gestion des ressources humaines, comptabilité, outils
collaboratifs, messagerie et d'autres applications métiers.
46
PaaS : c’est la plate-forme d’exécution, de déploiement et de
développement des applications sur la plate-forme du Cloud Computing.
IaaS : permet d'externaliser les serveurs, le réseau, le stockage dans des
salles informatiques distantes. Les entreprises démarrent ou arrêtent des
serveurs virtuels hébergés sur la plate-forme de Cloud Computing.
Credit Bamba Gueye - UCAD

Exemple d’application (AWS)
47Credit C. Rabat - CNAM

Common virtualization uses today
48

Common virtualisation uses…
• Run legacy software on non-legacy hardware
• Run multiple operating systems on the same hardware
• Create a manageable upgrade path
• Reduce costs by consolidating services onto the fewest number of
physical machines
49
http://www.vmware.com/img/serverconsolidation.jpg

Non-virtualized data centers
50
• Too many servers for too little work
• High costs and infrastructure needs
Maintenance
Networking
Floor space
Cooling
Power
Disaster Recovery

Virtualisation Features
51
VM Isolation
Secure Multiplexing
• Processor HW isolates VMs
Strong guarantees
• Software bugs, crashes, viruses
within one VM cannot affect other
VMs
Performance Isolation
• Partition system resources
(Controls for reservation, limit, shares)
VM Encapsulation
Entire VM is a File
Snapshots and clones
Easy content distribution
• Pre-configured apps, demos
• Virtual appliances
VM Compatibility
Hardware-independent
Create Once, Run Anywhere
• Migrate VMs between hosts
Legacy VMs
• Run ancient OS on new platform

PlanetLab
Different organizations contribute machines, which they subsequently
share for various experiments.
52
Problem: We need to ensure that different distributed applications
do not get into each other’s way => VIRTUALISATION

Planetlab
53
Vserver: Independent and protected environment with its own libraries, server versions
and so on.
Distributed apps are assigned a collection of vservers distributed accross multiple machines
(slice).

Planetlab map
54
https://www.planet-lab.org/

Références et liens
• Cyril Rabat – Introduction aux systèmes repartis (CNAM)
• Distributed systems reading list
• https://dancres.github.io/Pages/
55

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