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Grid Computing 7700 Fall 2005 Lecture 2: About Grid Computing. Gabrielle Allen allen@bit.csc.lsu.edu http://www.cct.lsu.edu/~gallen/Teaching. Quick Test. What reason does Foster (2002) give that the Web is not a Grid?

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grid computing 7700 fall 2005 lecture 2 about grid computing

Grid Computing 7700Fall 2005Lecture 2: About Grid Computing

Gabrielle Allen

allen@bit.csc.lsu.edu

http://www.cct.lsu.edu/~gallen/Teaching

quick test
Quick Test
  • What reason does Foster (2002) give that the Web is not a Grid?
  • Advances in which area have changed the way we should think about collaboration: a) sensors, b) supercomputers, c) mass storage, d) networks, e) HDTV
  • What is GGF an acronym for?
  • What speed do gravitational waves travel at? a) speed of sound, b) speed of light, c) infinite speed, d) 103,457 km/s, e) they do not move
some history
Some History

1843 US Congress investigate telegraph technology

1866 Transatlantic telegraph cable laid

1901 Transatlantic radio transmission

1965 Multics developers envisage utility computing

1969 Unix is developed

1970 ARPANET: DoD exerimental WAN, precusor to internet

1972 C written by Ritchie

1975 Microsoft founded

1980s Parallel computing: algorithms, programs and architectures

1980s “Grand Challenge” applications

1985 NSFNET: Links SC centers at 56 kbps

1988 Condor project starts (LAN based)

1989 “Metacomputing” term (CASA project)

1990 HTML developed by Tim Berners-Lee, first browsers

1991 Linus Thorvalds works on Linux

1993 Mosaic browser released

1999 SETI@home

1999 Napster: Centralized file sharing

2000 Microsoft release .NET

2000 Gnutella released: P2P file sharing

2001 “Anatomy of the Grid”

2001 NSF announces TeraGrid

2001 First Global Grid Forum

2001 Cactus, Globus, MPICH-G2 win Gordon Bell prize

2002 Earth Simulator: 40TFlop NEC machine

2002 Globus 2.0 released

2002 “Physiology of the Grid”

2003 Globus 3.0 released

2003 10Gbps transatlantic optical network demonstrated

2005 Globus 4.0 released

2005 TeraGrid awarded $150M

1993 Legion project starts

1993 HPF specification released

1994 MPI-1 specification released

1994 Nimrod project starts (LAN based)

1994 First beowulf cluster

1995 Dot.com era starts …

1995 Netscape goes public

1995 FAFNER: Factoring via Network-Enabled Recursion

1995 I-WAY (Information Wide Area Year) at SC95

1995 Globus project (ANL,UC,ISI) starts

1995 Java released by Sun

1997 Legion released

1997 UNICORE project starts

1997 Entropia founded

1998 Globus 1.0 released

1998 Legion commercial via Applied Metacomputing (becomes Avaki in 2001)

1999 First Grid Forum

fernando corbato
Fernando Corbato
  • Designer of multics OS
    • Mainframe timesharing OS
    • Lead to UNIX
  • In 1965 envisaged a computer facility “like a power company or water company”
j c r licklider
J. C. R. Licklider
  • Experimental psychologist
  • Envisaged a “grid” for scientific research
  • Contributed to development of ARPANET
  • 1968: Developed a vision of networked computers that would provide fast, automated support for human decision making
len kleinrock
Len Kleinrock
  • Created the basic principles of packet switching, the technology underpinning the Internet, while a graduate student at MIT
  • His computer was the first node on the internet
  • Envisaged spread of computer utilities (1969)
grand challenges
“Grand Challenges”
  • Fundamental problems in science and engineering with broad economic and scientific impact. They are generally considered intractable without the use of state-of-the-art massively parallel computers
  • Used by funding agencies from the 80s onwards to motivate advances in science and high performance computing
  • Brought together distributed teams who started to collaborate around their machines, codes, data, etc
i way sc95
I-WAY: SC95
  • High speed experimental distributed computing project.
  • Set up ATM network connecting supercomputers, mass storage, advanced viz devices at 17 US sites.
  • 30 software engineers, 60 applications, 10 networks (most OC-3c/155Mbps)
  • Application focused (remote viz, metacomputing, collaboration)
  • Single interface to schedule and start runs
  • I-POP machines (17) coordinated I-WAY “virtual machines”, gateways to the I-WAY
  • I-Soft software for management/programming
aims of i way
Aims of I-WAY
  • Develop network enabled tools and build collaborative environments on existing networks with differing protocols and properties
  • Locating and accessing distributed resources
  • Security and reliability
  • Use of distributed resources for computation
  • Uniform access to distributed data
  • Coupling distributed resources
i way infrastructure
I-WAY Infrastructure
  • I-POP: gateways to I-WAY
    • Dedicated point of present machines at each site
    • Uniformly configured with standard software environment
    • Accessible from internet, inside firewall
    • ATM interface for monitoring/management of ATM switch

From Ian Taylor

  • I-Soft: management and application programming environment
    • Ran on I-POP machines
    • Provided uniform authentification, resource reservation, process creation, communication functions
    • CRB: Computational Resource Broker (central scheduler)
    • Security: Telnet client amended with Kerberos authentication and encryption
    • File system: AFS for shared repository
    • Communication: Nexus adapted (MPICH, CAVEcomm)
i way new concepts
I-WAY New Concepts
  • Point of presence machines at each site
  • Computational resource broker integrates different local schedulers
  • Uniform authentication environment and trust relationships between sites
  • Network-aware parallel programming tools to provide uniform view and optimize communications
  • Led to Globus from ISI/ANL
slide12

DARPA, NSF, and DOE begin funding Grid work

Globus Toolkit® History

Does not include downloads from:NMI, UK eScience, EU Datagrid,IBM, Platform, etc.

GT 2.0

Released

GT 2.2

Released

Physiology of the Grid

Paper Released

GT 2.0 beta

Released

NSF GRIDS CenterInitiated, DOE begins

SciDAC program

Anatomy of the Grid

Paper Released

Significant

Commercial

Interest in

Grids

GT 1.1.4 and

MPICH-G2 Released

The Grid: Blueprint for a New Computing

Infrastructure published

NSF & European Commission

Initiate Many New Grid Projects

First

EuroGlobus

Conference

Held in

Lecce

GT 1.1.3

Released

MPICH-G

released

Early Application

Successes Reported

GT 1.1.2

Released

Globus Project wins

Global Information Infrastructure

Award

GT 1.0.0

Released

GT 1.1.1

Released

NASA initiatesInformation Power Grid,DOE increases support

1998

2002

1997

1999

2000

2001

From Globus Team

From Globus Team

some application areas
Life sciences

Computational biology, bioinformatics, genomics

Access, collecting and mining data, imaging

Engineering

Aircraft design, modeling and monitoring

Data

High energy physics, astronomy

Physical sciences

Numerical relativity, material science, geoscience

Collaborations

Sharing, real time interactivity, visualization, communication

Commercial

Gaming, idle workstations, climate predication, disaster, cyber security, portals

Education and distance learning

Some Application Areas
some application types
Some Application Types
  • Minimal communication (embarrassingly parallel)
  • Staged/linked/workflow
  • Access to Resources
  • Fast throughput
  • Large scale
  • Adaptive
  • Real-time on demand
  • Speculative
  • We will read about these and new application scenarios later
what are grids
What are Grids?
  • Provide: “coordinated resource sharing and problem solving in dynamic, multi-institutional, virtual organizations”
  • Grids link together people, computers, data, sensors, experimental equipment, visualization systems and networks (Virtual Organizations)
  • For example, they can provide
    • Sharing of computer resources
    • Pooling of information
    • Access to specialized equipment
    • Increased efficiency and on-demand computing
    • Enable distributed collaborations
  • Need to think about hardware, software, applications and policies.
grid checklist
Grid Checklist

A Grid …

  • Coordinates resources that are not subject to centralized control
  • Uses standard, open, general purpose protocols and interfaces
  • Delivers non-trivial qualities of service

Ian Foster, “What is the Grid? A Three Point Checklist”, 2002

grid resources
Grid Resources

Networks

  • High speed optical networks (e.g. NLR)
  • Academic networks: Internet2
  • Commercial network providers
  • Wireless, bluetooth, 3G, etc.

Visualization

  • Servers
  • Renderers
  • Access Grid
  • Eg CCT Imaginarium

Computers

  • Any networked CPU
  • Supercomputers & Clusters
  • Workstations
  • Home PCs
  • PDAs
  • Telephones
  • Game machines
  • Very different properties: clock speed, memory, cache, FPUs, memory bandwidth, OS, software

Data

  • Belonging to a single user or shared across a VO
  • Global distributed databases (e.g. NVO, Genome)
  • Storage devices
  • Security, access considerations

Devices

  • Sensors
  • Telescopes
  • Gravitational wave detectors
  • Microscopes
  • Synchrotrons
  • Medical scanners
  • Etc
characteristics
Characteristics
  • Different heterogeneous resources from different organizations
  • Mutually distrustful organizations
  • Differing security requirements and policies
  • Dynamic quality of service (machines, networks etc)
  • Heterogeneous networks
  • Capabilities: Dynamic, adaptive, autonomic, discovery
who will use the grid
Who Will Use The Grid
  • Computational scientists and engineers
  • Experimental scientists
  • Collaborations
  • Educators
  • Enterprises
  • Governments
  • Health authorities
  • Use cases should be driving Grid developments, so important to understand needs and translate to requirements.
computational scientists and engineers
Computational Scientists and Engineers
  • Numerical simulation, access to more and larger computing resources
  • Easier, more efficient, access to supercomputers
  • Realtime visualization
  • Computational steering
  • Network enabled solvers
  • New scenarios
experimental scientists
Experimental Scientists
  • Hook up supercomputers with instruments (telescopes, microscopes, …)
  • Advanced visualization and GUI interfaces
  • Remote control of instruments
  • Access to remote data
  • Management and use of large distributed data repositories
governments
Governments
  • Disaster response
  • National defense
  • Long term research and planning
  • Collective power of nations fastest computers, data archives and intellect to solve problems
  • Strategic computing reserve (environmental disaster, earthquake, homeland security)
  • National collaboratory: complex scientific and engineering problems such as global environmental change, space station design
virtual organizations
Virtual Organizations
  • “A number of mutually distrustful participants with varying degrees of prior relationship (perhaps none at all) who want to share resources in order to perform some task.” (Anatomy of the Grid”
  • Sharing involves direct access to remote software, computers, data and other resources.
  • Sharing relationships can vary over time, resources involved, nature of allowed access, participants who get access
  • Span small corporate departments to large groups of people from different organizations around the world
  • For example:
    • This class
    • The LSU numerical relativity group and its collaborators
    • Astronomical community who have access to virtual observatories
virtual organizations24
Virtual Organizations

Three organizations and two VOs

From “The Anatomy of the Grid”

virtual organizations25
Virtual Organizations
  • Vary in purpose, scope, size, duration, structure, community and sociology
  • Common requirements:
    • Highly flexibly sharing relationships (both client-server and peer-to-peer)
    • Sophisticated and precise levels of control over sharing
    • Delegation
    • Application of local and global policies
  • Address QoS, scheduling, co-allocation, accounting, …
how will they use it
How Will They Use It?
  • Distributed supercomputing
    • Aggregate computational resources for problems which can be solved on a single machine (e.g. all workstations in a company, all supercomputers in the world)
    • Large problems needing extreme memory, CPU, or other resource
    • E.g. astrophysics/numerical relativity: accurate simulations need fine scale detail
    • Challenges: latency, coscheduling, scalability, algorithms, performance
how will they use it27
How Will They Use It?
  • High Throughput Computing
    • Large numbers of loosely coupled or independent tasks (e.g. leverage unused cycles)
  • On-Demand Computing
    • Short term requirements for jobs which cannot be effectively or conveniently run locally.
    • Often driven by cost-performance concerns
    • Challenges: dynamic requirements, large numbers of users and resources, security, payment
how will they use it28
How Will They Use It?
  • Data Intensive Computing
    • Focus on generating new information from data in geographically distributed repositories, digital libraries, databases
    • E.g. High energy physics experiments generate terabytes of data/day, widely distributed collabotators; digital sky surveys
    • Challenges: scheduling and configuration of complex, high volume data flows
how will they use it29
How Will They Use It?
  • Collaborative Computing
    • Enabling human-human interactions e.g. with shared resources such as data archives and simulations
    • Often in terms of a virtual shared space, e.g. a Cave environment
    • Challenges: realtime requirements
e science
E-Science
  • Global collaborations for scientific research
  • “large scale science that will increasingly be carried out through distributed global collaborations enabled by the Internet”

UK E-Science Program http://www.rcuk.ac.uk/escience/

cyberinfrastructure
Cyberinfrastructure
  • Software to support E-Science
  • “An infrastructure based on grids and on application-specific software, tools, and data repositories that support research in a particular discipline.”

Getting Up To Speed, The Future of Supercomputing (2001)

  • GridChem project at CCT is building a cyberinfrastructure for computational chemists
  • UCOMS project at CCT is building a cyberinfrastructure for geoscientists
  • SCOOP project at CCT is building a cyberinfrastructure for coastal modellers
  • Looking for generic tools and techniques, driving research
new scenarios enabling new science

SDSC

S

Brill Wave

RZG

SDSC

LRZ

S1

Calculate/Output

Invariants

S2

Archive data

P1

Found a horizon,

try out excision

P2

Calculate/Output

Grav. Waves

Look for

horizon

S2

S1

Archive to LIGO

public database

Find best

resources

P2

P1

NCSA

New Scenarios enabling new science

Add more

resources

Queue time over,

find new machine

Free CPUs!!

Clone job with

steered parameter

Physicist has new idea !

e science33
E-Science
  • Global collaborations for scientific research
  • “large scale science that will increasingly be carried out through distributed global collaborations enabled by the Internet”

UK E-Science Program http://www.rcuk.ac.uk/escience/

cyberinfrastructure34
Cyberinfrastructure
  • Software to support E-Science
  • “An infrastructure based on grids and on application-specific software, tools, and data repositories that support research in a particular discipline.”

Getting Up To Speed, The Future of Supercomputing (2001)

  • GridChem project at CCT is building a cyberinfrastructure for computational chemists
  • UCOMS project at CCT is building a cyberinfrastructure for geoscientists
  • SCOOP project at CCT is building a cyberinfrastructure for coastal modellers
  • Looking for generic tools and techniques, driving research
new scenarios enabling new science35

SDSC

S

Brill Wave

RZG

SDSC

LRZ

S1

Calculate/Output

Invariants

S2

Archive data

P1

Found a horizon,

try out excision

P2

Calculate/Output

Grav. Waves

Look for

horizon

S2

S1

Archive to LIGO

public database

Find best

resources

P2

P1

NCSA

New Scenarios enabling new science

Add more

resources

Queue time over,

find new machine

Free CPUs!!

Clone job with

steered parameter

Physicist has new idea !

high performance computing
High Performance Computing

A branch of computer science that concentrates on developing supercomputers and software to run on supercomputers. A main area of this discipline is developing parallel processing algorithms and software: programs that can be divided into little pieces so that each piece can be executed simultaneously by separate processors.

numerical relativity
Numerical Relativity
  • Black holes, neutron stars, supernovae, gravitational waves
  • Governed by Einsteins Equations: very complex, need to solve numerically
  • 10 coupled mixed elliptic-hyperbolic PDEs, thousands of terms
  • High fidelity solutions need more research in numerics/physics … but also larger computers, better infrastructure
  • Physics currently limited by information technology!
numerical relativity39
Numerical Relativity
  • Good motivating example for Grid computing:
    • Large varied distributed collaborations
    • Need lots of cycles, storage (currently using teraflops, terabytes)
    • Need to share results, codes, parameter files, …
    • Need advanced visualization, steering
parallelisation41
Split the data to be worked on across the processors you have available

Each processor can then work on a different piece of data at the same time

Parallelisation

Proc 1

Proc 0

parallelisation42
But there is a downside: data needs to be exchanged between processors most iterations: e.g. “synchronize”, “global reduction, output

MPI (PVM, OpenMP, …)

Parallelisation

Proc 1

Proc 0

parallel io
In this example just want to output fields from 2 processors, but it could be 2000

Each processor could write it’s own data to disk

Then the data usually is moved to one place and “recombined” to produce a single coherent file

Parallel IO

Proc 1

Proc 0

parallel io44
Alternatively processor 0 can gather data from the other processors and write it all to disk

Usually a combination of these works best … let every nth processor gather data and write to disk

Parallel IO

Proc 1

Proc 0

large scale computing
Large Scale Computing
  • PARALLEL: Typical runs they do now needs 45GB of memory:
    • 171 Grid Functions
    • 400x400x200 grid
  • OPTIMIZE: Typical run makes 3000 iterations with 6000 Flops per grid point: 600 TeraFlops !!
  • PARALLEL IO/VIZ/DATA: Output of just one Grid Function at just one time step
    • 256 MB
    • (320 GB for 10GF every 50 time steps)
  • CHECKPOINTING: One simulation takes longer than queue times: Need 10-50 hours
  • STEERING/MONITORING: Computing time is expensive
    • One simulation: 2500 to 12500 SUs
    • Need to make each simulation count
numerical relativity46
Numerical Relativity
  • Good motivating example for Grid computing:
    • Large varied distributed collaborations
    • Need lots of cycles, storage (currently using teraflops, terabytes)
    • Need to share results, codes, parameter files, …
    • Need advanced visualization, data management, steering
    • Connection to experimental equipment (LIGO Gravitational Wave Detector) and data.
numerical relativity47
Numerical Relativity
  • How do computational physicists work now?
  • Accounts on different machines: LSU, NCSA, NERSC, PSC, SDSC, LRZ, RZG, …
  • Learn how to use each machine
    • Compilers, filesystem, scheduler, MPI, policies, …
  • Ssh to machine, copy source code, compile, determine e.g. how much output can do in file system, how big a run should be, best queue to submit to, submit batch script
  • Wait till run starts, keep logging in to check if it is still running, what is happening …
  • Copy all data back to local machine for visualization and analysis
  • Email colleagues and explain what they saw.
  • Loose data, forget what they ran.
  • Publish paper
new scenarios

SDSC

S

Brill Wave

RZG

SDSC

LRZ

S1

Calculate/Output

Invariants

S2

Archive data

P1

Found a horizon,

try out excision

P2

Calculate/Output

Grav. Waves

Look for

horizon

S2

S1

Archive to LIGO

public database

Find best

resources

P2

P1

NCSA

New Scenarios

Add more

resources

Queue time over,

find new machine

Free CPUs!!

Clone job with

steered parameter

Physicist has new idea !

teragrid teragrid org
TeraGrid: teragrid.org

“Cyber-infrastructure” constructed through NSF TeraScale initiative

  • 2000: TeraScale Computing System (TCS-1) at PSC, resulting in a 6 TFLOPS computational resource.
  • 2001: $53M funding. Distributed Terascale Facility (DTF), 15 TFLOPS computational Grid composed of major resources at ANL, Caltech, NCSA, and SDSC. Exploits homogeneity at the microprocessor level, Intel Itanium architecture (Itanium2 and its successor) clusters to maximally leverage software and integration efforts. Homogeneity will offer the user community an initial set of large-scale resources with a high degree of compatibility, reducing effort required to move into the computational Grid environment.
  • 2002: $35M funding and PSC joins. Extensible TeraScale Facility (ETF), combines the TCS-1 and DTF resources into a single, 21+ TFLOPS Grid environment and supports extensibility to additional sites and heterogeneity.
  • 2003: $10M and four new sites: ORNL, Purdue, Indiana, TACC. 40 TFLOPS and 2 PBs.
  • 2005: $150M to enhance and operate TeraGrid: http://www.teragrid.org/news/news05/0817.html
teragrid52
TeraGrid
  • Production system (now part of NSF computer time allocations)
  • Each site has speciality
    • NCSA: compute-intensive codes
    • ANL: visualization
    • SDSC: data-oriented computing
    • Caltech: scientific collections
teragrid objectives
TeraGrid: Objectives
  • Provide an unprecedented increase in the computational capabilities available to the open research community, both in terms of capacity and functionality.
  • Deploy a distributed “system” using Grid technologies rather than a “distributed computer” with centralized control, allowing the user community to map applications across the computational, storage, visualization, and other resources as an integrated environment.
  • Create an “enabling cyberinfrastructure” for scientific research in such a way that additional resources (at additional sites) can be readily integrated as well as providing a model that can be reused to create additional Grid systems that may or may not interoperate with TeraGrid (but are technically interoperable nonetheless).
teragrid design
TeraGrid: Design
  • Resources at different sites automonously managed
    • E.g. different software locations, user names
    • Rationale: more scalable and workable
  • Consistent set of fundamental grid services (Globus based)
  • Now building higher level services
  • http://www.teragrid.org/about/TeraGrid-Primer-Sept-02.pdf
grid architecture
Grid Architecture
  • Read about in this last weekend in Anatomy of the Grid …
  • Based on interoperability, extensibility
  • => common (or standard) protocols which define the mechanisms by which VOs negotiate, establish, manage, use shared resources
  • From protocols define standard services, APIs and SDKs
grid architecture57
Grid Architecture

Application

Collective

Resource

Connectivity

Fabric

layers
Layers
  • Applications: tools, applications, portals
  • Collective: resource scheduling, information providing, data management, systems such as MPICH-G, taskfarming, community authorization, accounting
  • Resource and Connectivity: Secure access to resources and services (communication, data transfer, security)
  • Fabric: Diverse resources (including local resource specific operations)
infrastructure
Infrastructure
  • Communication services
    • Transport and routing
    • Un/reliable point to point communications, multicast,…
    • Bulk-data transport, streaming data, …
    • Parameters: latency, bandwidth, reliability, fault tolerance, jitter
  • Information services
    • Location and type of services change dynamically
    • Mechanisms for registering and obtaining information about resources, services, status, applications, network …
infrastructure60
Infrastructure
  • Naming services
    • Names for computers, services, applications, data, job ids
    • Uniform namespace across complete environment
    • E.g. X.500 naming scheme (directory services), Domain Name Service (DNS)
  • Data Management and Replication
    • Access to files distributed across many servers (e.g. data mining)
    • Distributed filesystem must provide a uniform global namespace
    • Support range of file I/O protocols
    • Allow performance optimizations (e.g. caching)
infrastructure61
Infrastructure
  • Security and authorization
    • Single sign-on
    • Confidentiality
    • Authentication (determines a user's or server’s identity)
    • Authorization (what a user etc is allowed to do)
    • Delegation/restricted delegation (program can run on users behalf, maybe with less authorization)
    • Integration with diverse resources with different administrations/security solutions (e.g. kerberos, unix, …)
    • Trust relationships
    • Support communication/data protection
  • Monitoring resources and applications
infrastructure62
Infrastructure
  • Resource management and scheduling
    • Efficient scheduling and deployment of applications across distributed machines
    • Management of resources and applications running on them
    • User just wants application submission
    • Cost/efficiency/application constraints/throughput
    • Coscheduling, advanced reservation, network/data storage reservation
    • Accounting
  • User and administrative GUIs
    • Interfaces should be intuitive, easy to use, and heterogeneous.
    • Typically web based (accessible from anywhere)
reading for next lecture
Reading for Next Lecture
  • The following is expected to be read by the next lecture:
    • The Anatomy of the Grid
coursework 1
Coursework 1
  • Due Monday August 29th NEW! Wednesday 31st
  • Essay: “What is Grid Computing”
    • 5 pages (+ cover page)
    • Explain what Grid Computing is, and how it differs from distributed computing, internet technologies and high performance computing
    • Explain how Grid Computing could support and advance scientific research
    • Explain the potential economic benefit of Grid Computing to the US economy
cct eminent lecture managing information on the net the digital object architecture
CCT Eminent Lecture Managing Information on the Net: the Digital Object Architecture

Dr. Kahn will discuss an architectural approach to managing information on the net. In particular, he will focus on applications where the information may need to be persist over very long periods of time and where it may be moved many times from site to site and platform to platform over its lifetime. An open architecture approach to federated repositories will also be discussed along with applications of the technology.

Robert E. Kahn is Chairman, CEO and President of the Corporation for National Research Initiatives (CNRI), which he founded in 1986 after a thirteen year term at the U.S. Defense Advanced Research Projects Agency (DARPA). Dr. Kahn earned M.A. and Ph.D. degrees from Princeton University in 1962 and 1964 respectively. He worked on the Technical Staff at Bell Laboratories and then became an Assistant Professor of Electrical Engineering at MIT. He was responsible for the system design of the Arpanet, the first packet-switched network. In 1972 he moved to DARPA and subsequently became Director of DARPA's Information Processing Techniques Office (IPTO). He is a co-inventor of the TCP/IP protocols.