P2PDisCo%20

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P2PDisCo Java Distributed Computing for
Workstations Using Chedar Peer-to-Peer Middleware
Presentation for 7th International Workshop on
Javatm for Parallel and Distributed Computing
(IWJPDC 2005)

• 4.4.2005
• Mikko Vapa, research student
• Department of Mathematical Information Technology
• University of Jyväskylä, Finland
• http//tisu.it.jyu.fi/cheesefactory
• With co-authors Niko Kotilainen, Matthieu Weber,
  Joni Töyrylä and Jarkko Vuori

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Overview

• This paper introduces Peer-to-Peer Distributed
  Computing (P2PDisCo) software providing interface
  for distributing the computation of Java programs
  to multiple workstations
• P2PDisCo has been built over Chedar peer-to-peer
  middleware
• Currently, P2PDisCo is being used for speeding up
  the training of neural networks with evolutionary
  algorithm

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Peer-to-Peer Networks

• Peer-to-Peer (P2P) networks allow sharing of
  resources (e.g., computing power, storage space,
  network bandwidth, printers) over the Internet
• In contrast to clusters, in P2P networks all the
  tasks and responsibilities for managing the
  network are shared between peers
• This means that there exists no single control
  entity responsible for providing the services
• Because P2P networks do not require a dedicated
  hardware, distributing computation among
  workstations is usually a cost-effective solution

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Related Work

• There are many alternatives for distributing
  computing using Java programming language
• Programming language independent distributed
  computing tools such as Globus Toolkit with Java
  Commodity Grid Kit
• Programming language dependent Java distributed
  computing software
• Java extensions requiring changes to Java
  compiler and/or Java Virtual Machine (JVM)
• JavaParty as an example
• Java libraries providing special class libraries
  without a need for modifications to the Java
  compiler or JVM
• JavaSymphony and P2PDisCo as examples

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Related Work

• Many of these distribution tools have some
  centralized components in them
• Globus uses centralized indexes for resource
  discovery whereas in P2PDisCo the resource
  discovery is decentralized and provided by the
  Chedar peer-to-peer network
• In JavaSymphony all the computing resources are
  centrally configured under JS-Shell whereas in
  P2PDisCo no central management exists
• There are also some implementations of Java
  distributed computing that use peer-to-peer
  network for locating resources
• An example of such system is GT-P2PRMI allowing
  Remote Method Invocation (RMI) bindings and
  lookups to be executed via a modified RMIRegistry
  called P2PRMIRegistry

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Chedar P2P Middleware

• Chedar (CHEap Distributed Architecture) is
  peer-to-peer middleware designed for the needs of
  peer-to-peer applications
• Chedar constructs a pure peer-to-peer network
  using topology management algorithms and provides
  functionalities for locating resources in the
  network
• Implementation of Chedar is based on Java
  Standard Edition, thus providing platform
  independency and easy adaptation to different
  hardware

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Chedar P2P Middleware

• Each Chedar node maintains a database of locally
  available resources for example information about
  which applications are running on the device or
  what files are located in the node
• Resources can contain meta-information about
  themselves for example the version number for
  applications and last modification date for files
• Resource database is stored as an XML document
  using a specific Document Type Definition (DTD)
• This organization of data allows making rich and
  complex queries to the database in the form of
  XPath expressions

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Chedar P2P Middleware

• Chedar node keeps a list of neighbors it is
  connected to through TCP sockets
• TCP provides reliable data delivery and the
  disappearance of a neighbor can be detected with
  TCP timeout
• The neighbor list is updated based on heuristics
  such as number of relayed query replies and the
  actual query replies provided by the neighbor to
  form an efficient topology for resource discovery
• As a search mechanism we currently use
  Breadth-First Search (BFS) algorithm, which
  scales to small network sizes and guarantees to
  locate all resources in the network
• In our experiments, the query traffic in the
  network of 200 workstations with 100 Mb/s
  Ethernet connections has not yet posed a
  significant problem and therefore a more
  efficient version of the query algorithm has not
  been implemented

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Chedar P2P Middleware

• Each query contains a Message-ID and a query
  XPath description
• Whenever a query enters a Chedar node, the node
  checks its resource database for matching
  resources to XPath expression and if resource is
  found, a reply message is sent back using the
  route, which the query came from
• To properly relay the reply message back to the
  query originator, the message needs to contain
  the same Message-ID as the query had
• For communicating between two peers, Chedar
  provides a point-to-point communication protocol
  allowing basic message passing primitives to be
  executed by P2P applications
• The protocol uses the same path as the reply
  message to deliver messages between peers

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Peer-to-Peer Distributed Computing

• Problem
• Evolving neural networks in a simulator needs a
  lot of computing power
• One computer is not enough for many research
  cases
• Solution
• Distribute computation across desktop computers
  all over the University of Jyväskylä
• It has to be as invisible as possible to the user
  of the network simulator
• The simulator should not interfere with the
  desktop use of the distributed computers
• As a solution Peer-to-Peer Distributed Computing
  (P2PDisCo) was developed on top of Chedar

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P2PDisCo - Architecture

• The node that wants to distribute its computation
  (denoted as master) needs to query resources,
  receive query replies and send data (parameters)
  for the computation
• The node that offers computation time has to
  implement Distributed interface to be able to
  receive start, stop and is application running
  signals
• Reading of parameters and writing of results are
  done for the streams offered by P2PDisCo

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P2PDisCo - Architecture
Chedar node
Chedar node
Chedar node
Master
Chedar node
Chedar node
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P2PDisCo - Architecture
Chedar node
Chedar node
Chedar node
Master
Chedar node
Chedar node
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P2PDisCo - Architecture
Chedar node
Chedar node
Query
Query
Chedar node
Master
Chedar node
Chedar node
Query who has the resource NetSimulator
available?
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P2PDisCo - Architecture
Reply
Chedar node
Chedar node
Reply
Reply
Chedar node
Master
Chedar node
Chedar node
Reply I do!
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P2PDisCo - Architecture
Task
Chedar node
Chedar node
Task
Task
Chedar node
Master
Chedar node
Chedar node
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P2PDisCo - Architecture
Chedar node
Chedar node
Chedar node
Master
Chedar node
Chedar node
Computation
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P2PDisCo - Architecture
Result
Chedar node
Chedar node
Result
Result
Chedar node
Master
Chedar node
Chedar node
Results are sent back to the master
node. Calculation ends and everybody is happy.
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What happens inside a Chedar node
Chedar node starts the distributed application,
hijacking its file operations.
Distributed program
Task
Chedar
Result
Any Java program that uses files to read input
and store output can be distributed
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Security Concerns

• Because of security concerns the distributed
  application has been beforehand installed to the
  computers and it is not automatically delivered
  during the task distribution
• In the task distribution only the execution
  parameters i.e. configuration files are
  transferred
• Also, currently the IP addresses of master nodes
  are restricted such that only certain IP
  addresses are allowed to start computations

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Future Work

• At this time P2PDisCo is just a tool for our
  research project to speed up the computations of
  NeuroSearch neural network resource discovery
  algorithm
• Possible improvements
• Checkpointing of computation such that if
  connection is lost the computation can be resumed
  from the same point
• Master could leave the network and gather results
  afterwards
• Extending API of P2PDisCo to allow direct
  communication between computing nodes, which
  makes it possible to parallelize the evolutionary
  algorithm for multiple computers with other
  architectures than master-slave, such as the
  panmictic model commonly used for parallelization
  of evolutionary algorithms

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Thank You!

• Any questions?