SlideShare une entreprise Scribd logo
1  sur  34
What is Cluster?
Computer Clusters
Configuration
Applications of Clusters
Components
Classification
Single Sysytem Image
Cluster Middleware
Benefits of Cluster , SSI & Middle
A group of the same or similar elements gathered or
occurring closely together;
 a bunch
 a number of persons or things grouped together
 a number of things growing, fastened, or occurring
close together

 “A computer cluster is a single logical unit

consisting of multiple computers that are linked
through a LAN and they can be worked as a single
system.”
 The components of a cluster are usually connected
to each other through fast local area
networks ("LAN").
 The networked computers essentially act as a
single, much more powerful machine
Each system has its own CPU, memory, and I/O
facilities
• Each system is known as a node of the cluster
• generally 2 or more computers (nodes) connected
together
• in a single cabinet, or physically separated &
connected via a LAN
• appear as a single system to users and
applications
• provide a cost-effective way to gain features and
benefits
•



Shared nothing Model
Shared Disk Model

The detailed configuration models are:-

1. Shared nothing Model
 High speed link between nodes
 No sharing of resources
 Partitioning of work through division of data
 Advantage:
 Reduced communication between nodes



Disadvantage:

 Can result in inefficient division of work




High speed link between nodes
Disk drives are shared between nodes
Advantage:
 Better load balancing



Disadvantage:
 Complex software required for transactional

processing
Technicians working on a large Linux
cluster at University of Technology,
Germany.

Sun Microsystems
Cluster
Clusters are classified into many categories
based on various factors as indicated below.
 Application Target
 Node Ownership
 Node Hardware
 Node Operating System
 Node Configuration
 Application Target

 High Performance (HP) Clusters
▪ Grand Challenging Applications

 High Availability (HA) Clusters
▪ Mission Critical applications
 Node

Ownership

 Dedicated Clusters
 Non-dedicated clusters
▪ Adaptive parallel computing
 Node

Hardware

 Clusters of PCs (CoPs)
 Clusters of Workstations (COWs)
 Node Configuration

 Homogeneous Clusters
▪ All nodes will have similar architectures and run
the same OSs

 Heterogeneous Clusters
▪ All nodes will have different architectures and
run different OSs


Processors



Memory and Cache



Disk and I/O



System Bus
Aggregated speed with
which complex calculations
carried out by millions of neurons in
human brain is amazing! although
individual neurons response is slow
(milli sec.)
•

The Single System Image (SSI) represents the view of a distributed
system as a single unified computing resource

•

This provides better usability for the users as it hides the
complexities of the underlying distributed and heterogeneous
nature of clusters from them



A single system image is the illusion, created by software or
hardware, that presents a collection of resources as one, more
powerful resource.



SSI makes the cluster appear like a single machine to the user, to
applications, and to the network.

•

A cluster without a SSI is not a cluster









Single entry point
 telnet cluster.my_institute.edu
 telnet node1.cluster.my_institute.edu
Single file hierarchy
Single control point: manage from single GUI
Single virtual networking
Single memory space - DSM
Single job management
Single user interface
20


Problem
 each nodes has a certain amount of resources

that can only be used from that node
 This restriction limits the power of a cluster


Solution
 implementing a middle-ware layer that glues

all operating systems on all nodes
 offer a unified access to system resources
22








Usage of system resources transparently
Transparent process migration and load balancing
across nodes.
Improved reliability and higher availability
Improved system response time and performance
Simplified system management
Reduction in the risk of operator errors
User need not be aware of the underlying system
architecture to use these machines effectively
•

A cluster resource management system (RMS) acts
as a cluster middleware that implements the SSI for
a cluster of machines

•

It enables users to execute jobs on the cluster
without the need to understand the complexities of
the underlying cluster architecture

•

A RMS manages the cluster through four major
branches, namely: resource management, job
queuing, job scheduling, and job management
An interface between between use applications and cluster
hardware and OS platform.
Middleware packages support each other at the management,
programming, and implementation levels.









Programming
 enable applications, reduce programming effort,
distributed object/component models?
Middleware Layers:
 SSI Layer
 Availability Layer: It enables the cluster services of
▪ Checkpointing, Automatic Failover, recovery from failure,
▪ fault-tolerant operating among all cluster nodes.


Complete Transparency (Manageability)
 Lets the see a single cluster system..
▪ Single entry point



Scalable Performance
 Easy growth of cluster
▪ no change of API & automatic load distribution.



Enhanced Availability
 Automatic Recovery from failures
▪ Employ checkpointing & fault tolerant technologies
 Handle consistency of data when replicated..
Sequential Applications
Sequential Applications
Sequential Applications

Parallel Applications
Parallel Applications
Parallel Applications
Parallel Programming Environment

Cluster Middleware
(Single System Image and Availability Infrastructure)
PC/Workstation

PC/Workstation

PC/Workstation

PC/Workstation

Communications

Communications

Communications

Communications

Software

Software

Software

Software

Network Interface
Hardware

Network Interface
Hardware

Cluster Interconnection Network/Switch

Network Interface
Hardware

Network Interface
Hardware


High performance: running cluster enabled
programs



Scalability: adding servers to the cluster or by adding
more clusters to the network as the need arises or CPU to
SMP(Symmetric Multiprocessors)




High throughput: (cycle)
System availability (HA): offer inherent high
system availability due to the redundancy of hardware,
operating systems, and applications



Cost-effectively
28





Numerous Scientific & engineering Apps.
Business Applications:
 Database Applications (Oracle on clusters).
Internet Applications
Mission Critical Applications:
 banks, nuclear reactor control
The conclusion of this chapter is that although
solutions to the resource management and
Meta computing problems do exist, none of the
solutions is directly applicable to the case in
which a user does not have a great deal of
knowledge about the system they are using.


Clusters are promising and fun
 Offer incremental growth and match with funding

pattern
 New trends in hardware and software
technologies are likely to make clusters more
promising
 Cluster-based HP and HA systems can be seen
everywhere!
Summary ?


Thank You ...

?


High Performance Clusters
 Linux Cluster; 1000 nodes; parallel programs; MPI



Load-leveling Clusters

 Move processes around to borrow cycles (eg. Mosix)



Web-Service Clusters

 LVS/Piranah; load-level tcp connections; replicate data



Storage Clusters

 GFS; parallel filesystems; same view of data from each node



Database Clusters

 Oracle Parallel Server;



High Availability Clusters
 ServiceGuard, Lifekeeper, Failsafe, heartbeat, failover clusters

Contenu connexe

Tendances

Distributed Systems
Distributed SystemsDistributed Systems
Distributed SystemsRupsee
 
Agreement Protocols, distributed File Systems, Distributed Shared Memory
Agreement Protocols, distributed File Systems, Distributed Shared MemoryAgreement Protocols, distributed File Systems, Distributed Shared Memory
Agreement Protocols, distributed File Systems, Distributed Shared MemorySHIKHA GAUTAM
 
Operating system architecture
Operating system architectureOperating system architecture
Operating system architectureSabin dumre
 
Aggrement protocols
Aggrement protocolsAggrement protocols
Aggrement protocolsMayank Jain
 
Congestion avoidance in TCP
Congestion avoidance in TCPCongestion avoidance in TCP
Congestion avoidance in TCPselvakumar_b1985
 
Query Decomposition and data localization
Query Decomposition and data localization Query Decomposition and data localization
Query Decomposition and data localization Hafiz faiz
 
Parallel computing
Parallel computingParallel computing
Parallel computingVinay Gupta
 
20. Parallel Databases in DBMS
20. Parallel Databases in DBMS20. Parallel Databases in DBMS
20. Parallel Databases in DBMSkoolkampus
 
Ddb 1.6-design issues
Ddb 1.6-design issuesDdb 1.6-design issues
Ddb 1.6-design issuesEsar Qasmi
 
clock synchronization in Distributed System
clock synchronization in Distributed System clock synchronization in Distributed System
clock synchronization in Distributed System Harshita Ved
 
management of distributed transactions
management of distributed transactionsmanagement of distributed transactions
management of distributed transactionsNilu Desai
 
Intro (Distributed computing)
Intro (Distributed computing)Intro (Distributed computing)
Intro (Distributed computing)Sri Prasanna
 
Design issues of dos
Design issues of dosDesign issues of dos
Design issues of dosvanamali_vanu
 
Lecture 1 introduction to parallel and distributed computing
Lecture 1   introduction to parallel and distributed computingLecture 1   introduction to parallel and distributed computing
Lecture 1 introduction to parallel and distributed computingVajira Thambawita
 
Clock synchronization in distributed system
Clock synchronization in distributed systemClock synchronization in distributed system
Clock synchronization in distributed systemSunita Sahu
 

Tendances (20)

Distributed Systems
Distributed SystemsDistributed Systems
Distributed Systems
 
Cluster Computing
Cluster ComputingCluster Computing
Cluster Computing
 
Agreement Protocols, distributed File Systems, Distributed Shared Memory
Agreement Protocols, distributed File Systems, Distributed Shared MemoryAgreement Protocols, distributed File Systems, Distributed Shared Memory
Agreement Protocols, distributed File Systems, Distributed Shared Memory
 
Operating system architecture
Operating system architectureOperating system architecture
Operating system architecture
 
Aggrement protocols
Aggrement protocolsAggrement protocols
Aggrement protocols
 
Congestion avoidance in TCP
Congestion avoidance in TCPCongestion avoidance in TCP
Congestion avoidance in TCP
 
Query Decomposition and data localization
Query Decomposition and data localization Query Decomposition and data localization
Query Decomposition and data localization
 
Parallel computing
Parallel computingParallel computing
Parallel computing
 
kernels
 kernels kernels
kernels
 
Resource management
Resource managementResource management
Resource management
 
20. Parallel Databases in DBMS
20. Parallel Databases in DBMS20. Parallel Databases in DBMS
20. Parallel Databases in DBMS
 
Kernel (OS)
Kernel (OS)Kernel (OS)
Kernel (OS)
 
Ddb 1.6-design issues
Ddb 1.6-design issuesDdb 1.6-design issues
Ddb 1.6-design issues
 
clock synchronization in Distributed System
clock synchronization in Distributed System clock synchronization in Distributed System
clock synchronization in Distributed System
 
Parallel computing persentation
Parallel computing persentationParallel computing persentation
Parallel computing persentation
 
management of distributed transactions
management of distributed transactionsmanagement of distributed transactions
management of distributed transactions
 
Intro (Distributed computing)
Intro (Distributed computing)Intro (Distributed computing)
Intro (Distributed computing)
 
Design issues of dos
Design issues of dosDesign issues of dos
Design issues of dos
 
Lecture 1 introduction to parallel and distributed computing
Lecture 1   introduction to parallel and distributed computingLecture 1   introduction to parallel and distributed computing
Lecture 1 introduction to parallel and distributed computing
 
Clock synchronization in distributed system
Clock synchronization in distributed systemClock synchronization in distributed system
Clock synchronization in distributed system
 

Similaire à Clusters

Cluster computing ppt
Cluster computing pptCluster computing ppt
Cluster computing pptDC Graphics
 
Distributed computing ).ppt him
Distributed computing ).ppt himDistributed computing ).ppt him
Distributed computing ).ppt himHimanshu Saini
 
introduction to cloud computing for college.pdf
introduction to cloud computing for college.pdfintroduction to cloud computing for college.pdf
introduction to cloud computing for college.pdfsnehan789
 
Distributed systems and scalability rules
Distributed systems and scalability rulesDistributed systems and scalability rules
Distributed systems and scalability rulesOleg Tsal-Tsalko
 
Overview of Distributed Systems
Overview of Distributed SystemsOverview of Distributed Systems
Overview of Distributed Systemsvampugani
 
Apos week 1 4
Apos week 1   4Apos week 1   4
Apos week 1 4alixafar
 
Cluster computing pptl (2)
Cluster computing pptl (2)Cluster computing pptl (2)
Cluster computing pptl (2)Rohit Jain
 
Clustercomputingpptl2 120204125126-phpapp01
Clustercomputingpptl2 120204125126-phpapp01Clustercomputingpptl2 120204125126-phpapp01
Clustercomputingpptl2 120204125126-phpapp01Ankit Soni
 
CS8603_Notes_003-1_edubuzz360.pdf
CS8603_Notes_003-1_edubuzz360.pdfCS8603_Notes_003-1_edubuzz360.pdf
CS8603_Notes_003-1_edubuzz360.pdfKishaKiddo
 
Cloud computing1
Cloud computing1Cloud computing1
Cloud computing1ali raza
 
00 - BigData-Chapter_01-PDC.pdf
00 - BigData-Chapter_01-PDC.pdf00 - BigData-Chapter_01-PDC.pdf
00 - BigData-Chapter_01-PDC.pdfaminnezarat
 

Similaire à Clusters (20)

Cluster computing
Cluster computingCluster computing
Cluster computing
 
Cluster computing
Cluster computingCluster computing
Cluster computing
 
Cluster computing ppt
Cluster computing pptCluster computing ppt
Cluster computing ppt
 
Cluster Computing
Cluster ComputingCluster Computing
Cluster Computing
 
Distributed computing ).ppt him
Distributed computing ).ppt himDistributed computing ).ppt him
Distributed computing ).ppt him
 
introduction to cloud computing for college.pdf
introduction to cloud computing for college.pdfintroduction to cloud computing for college.pdf
introduction to cloud computing for college.pdf
 
Distributed systems and scalability rules
Distributed systems and scalability rulesDistributed systems and scalability rules
Distributed systems and scalability rules
 
CLUSTER COMPUTING
CLUSTER COMPUTINGCLUSTER COMPUTING
CLUSTER COMPUTING
 
Cluster Computing
Cluster ComputingCluster Computing
Cluster Computing
 
Overview of Distributed Systems
Overview of Distributed SystemsOverview of Distributed Systems
Overview of Distributed Systems
 
CCUnit1.pdf
CCUnit1.pdfCCUnit1.pdf
CCUnit1.pdf
 
Distributed Systems.pptx
Distributed Systems.pptxDistributed Systems.pptx
Distributed Systems.pptx
 
Cluster computing
Cluster computingCluster computing
Cluster computing
 
Apos week 1 4
Apos week 1   4Apos week 1   4
Apos week 1 4
 
Cluster computing pptl (2)
Cluster computing pptl (2)Cluster computing pptl (2)
Cluster computing pptl (2)
 
Clustercomputingpptl2 120204125126-phpapp01
Clustercomputingpptl2 120204125126-phpapp01Clustercomputingpptl2 120204125126-phpapp01
Clustercomputingpptl2 120204125126-phpapp01
 
CS8603_Notes_003-1_edubuzz360.pdf
CS8603_Notes_003-1_edubuzz360.pdfCS8603_Notes_003-1_edubuzz360.pdf
CS8603_Notes_003-1_edubuzz360.pdf
 
Cluster cmputing
Cluster cmputingCluster cmputing
Cluster cmputing
 
Cloud computing1
Cloud computing1Cloud computing1
Cloud computing1
 
00 - BigData-Chapter_01-PDC.pdf
00 - BigData-Chapter_01-PDC.pdf00 - BigData-Chapter_01-PDC.pdf
00 - BigData-Chapter_01-PDC.pdf
 

Plus de Muhammad Ishaq

Plus de Muhammad Ishaq (20)

Causality in special relativity
Causality in special relativityCausality in special relativity
Causality in special relativity
 
Business proposal
Business proposalBusiness proposal
Business proposal
 
Artificial neural network model & hidden layers in multilayer artificial neur...
Artificial neural network model & hidden layers in multilayer artificial neur...Artificial neural network model & hidden layers in multilayer artificial neur...
Artificial neural network model & hidden layers in multilayer artificial neur...
 
Artificial Neural Network
Artificial Neural NetworkArtificial Neural Network
Artificial Neural Network
 
Writting process
Writting processWritting process
Writting process
 
Business
Business Business
Business
 
Index
IndexIndex
Index
 
Brochures
BrochuresBrochures
Brochures
 
Dependencies
DependenciesDependencies
Dependencies
 
Input output
Input outputInput output
Input output
 
Multi core processor
Multi core processorMulti core processor
Multi core processor
 
Dram and its types
Dram and its typesDram and its types
Dram and its types
 
Micro operation control of processor
Micro operation control of processorMicro operation control of processor
Micro operation control of processor
 
Computer architecture overview
Computer architecture overviewComputer architecture overview
Computer architecture overview
 
Raid 1 3
Raid 1 3Raid 1 3
Raid 1 3
 
Multi processing
Multi processingMulti processing
Multi processing
 
Cache memory
Cache memoryCache memory
Cache memory
 
Cache memory
Cache memoryCache memory
Cache memory
 
Addressing
AddressingAddressing
Addressing
 
Raid level 4
Raid level 4Raid level 4
Raid level 4
 

Clusters

  • 1.
  • 2.
  • 3. What is Cluster? Computer Clusters Configuration Applications of Clusters Components Classification Single Sysytem Image Cluster Middleware Benefits of Cluster , SSI & Middle
  • 4. A group of the same or similar elements gathered or occurring closely together;  a bunch  a number of persons or things grouped together  a number of things growing, fastened, or occurring close together 
  • 5.  “A computer cluster is a single logical unit consisting of multiple computers that are linked through a LAN and they can be worked as a single system.”  The components of a cluster are usually connected to each other through fast local area networks ("LAN").  The networked computers essentially act as a single, much more powerful machine
  • 6. Each system has its own CPU, memory, and I/O facilities • Each system is known as a node of the cluster • generally 2 or more computers (nodes) connected together • in a single cabinet, or physically separated & connected via a LAN • appear as a single system to users and applications • provide a cost-effective way to gain features and benefits •
  • 7.   Shared nothing Model Shared Disk Model The detailed configuration models are:- 1. Shared nothing Model  High speed link between nodes  No sharing of resources  Partitioning of work through division of data  Advantage:  Reduced communication between nodes  Disadvantage:  Can result in inefficient division of work
  • 8.    High speed link between nodes Disk drives are shared between nodes Advantage:  Better load balancing  Disadvantage:  Complex software required for transactional processing
  • 9.
  • 10.
  • 11. Technicians working on a large Linux cluster at University of Technology, Germany. Sun Microsystems Cluster
  • 12. Clusters are classified into many categories based on various factors as indicated below.  Application Target  Node Ownership  Node Hardware  Node Operating System  Node Configuration
  • 13.  Application Target  High Performance (HP) Clusters ▪ Grand Challenging Applications  High Availability (HA) Clusters ▪ Mission Critical applications
  • 14.  Node Ownership  Dedicated Clusters  Non-dedicated clusters ▪ Adaptive parallel computing
  • 15.  Node Hardware  Clusters of PCs (CoPs)  Clusters of Workstations (COWs)
  • 16.  Node Configuration  Homogeneous Clusters ▪ All nodes will have similar architectures and run the same OSs  Heterogeneous Clusters ▪ All nodes will have different architectures and run different OSs
  • 18. Aggregated speed with which complex calculations carried out by millions of neurons in human brain is amazing! although individual neurons response is slow (milli sec.)
  • 19. • The Single System Image (SSI) represents the view of a distributed system as a single unified computing resource • This provides better usability for the users as it hides the complexities of the underlying distributed and heterogeneous nature of clusters from them  A single system image is the illusion, created by software or hardware, that presents a collection of resources as one, more powerful resource.  SSI makes the cluster appear like a single machine to the user, to applications, and to the network. • A cluster without a SSI is not a cluster
  • 20.        Single entry point  telnet cluster.my_institute.edu  telnet node1.cluster.my_institute.edu Single file hierarchy Single control point: manage from single GUI Single virtual networking Single memory space - DSM Single job management Single user interface 20
  • 21.
  • 22.  Problem  each nodes has a certain amount of resources that can only be used from that node  This restriction limits the power of a cluster  Solution  implementing a middle-ware layer that glues all operating systems on all nodes  offer a unified access to system resources 22
  • 23.        Usage of system resources transparently Transparent process migration and load balancing across nodes. Improved reliability and higher availability Improved system response time and performance Simplified system management Reduction in the risk of operator errors User need not be aware of the underlying system architecture to use these machines effectively
  • 24. • A cluster resource management system (RMS) acts as a cluster middleware that implements the SSI for a cluster of machines • It enables users to execute jobs on the cluster without the need to understand the complexities of the underlying cluster architecture • A RMS manages the cluster through four major branches, namely: resource management, job queuing, job scheduling, and job management
  • 25. An interface between between use applications and cluster hardware and OS platform. Middleware packages support each other at the management, programming, and implementation levels.     Programming  enable applications, reduce programming effort, distributed object/component models? Middleware Layers:  SSI Layer  Availability Layer: It enables the cluster services of ▪ Checkpointing, Automatic Failover, recovery from failure, ▪ fault-tolerant operating among all cluster nodes.
  • 26.  Complete Transparency (Manageability)  Lets the see a single cluster system.. ▪ Single entry point  Scalable Performance  Easy growth of cluster ▪ no change of API & automatic load distribution.  Enhanced Availability  Automatic Recovery from failures ▪ Employ checkpointing & fault tolerant technologies  Handle consistency of data when replicated..
  • 27. Sequential Applications Sequential Applications Sequential Applications Parallel Applications Parallel Applications Parallel Applications Parallel Programming Environment Cluster Middleware (Single System Image and Availability Infrastructure) PC/Workstation PC/Workstation PC/Workstation PC/Workstation Communications Communications Communications Communications Software Software Software Software Network Interface Hardware Network Interface Hardware Cluster Interconnection Network/Switch Network Interface Hardware Network Interface Hardware
  • 28.  High performance: running cluster enabled programs  Scalability: adding servers to the cluster or by adding more clusters to the network as the need arises or CPU to SMP(Symmetric Multiprocessors)   High throughput: (cycle) System availability (HA): offer inherent high system availability due to the redundancy of hardware, operating systems, and applications  Cost-effectively 28
  • 29.     Numerous Scientific & engineering Apps. Business Applications:  Database Applications (Oracle on clusters). Internet Applications Mission Critical Applications:  banks, nuclear reactor control
  • 30. The conclusion of this chapter is that although solutions to the resource management and Meta computing problems do exist, none of the solutions is directly applicable to the case in which a user does not have a great deal of knowledge about the system they are using.
  • 31.  Clusters are promising and fun  Offer incremental growth and match with funding pattern  New trends in hardware and software technologies are likely to make clusters more promising  Cluster-based HP and HA systems can be seen everywhere!
  • 34.  High Performance Clusters  Linux Cluster; 1000 nodes; parallel programs; MPI  Load-leveling Clusters  Move processes around to borrow cycles (eg. Mosix)  Web-Service Clusters  LVS/Piranah; load-level tcp connections; replicate data  Storage Clusters  GFS; parallel filesystems; same view of data from each node  Database Clusters  Oracle Parallel Server;  High Availability Clusters  ServiceGuard, Lifekeeper, Failsafe, heartbeat, failover clusters

Notes de l'éditeur

  1. application programming interface