Transcript Cheese Factory

UNIVERSITY OF JYVÄSKYLÄ
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
UNIVERSITY OF JYVÄSKYLÄ
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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UNIVERSITY OF JYVÄSKYLÄ
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 costeffective solution
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UNIVERSITY OF JYVÄSKYLÄ
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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UNIVERSITY OF JYVÄSKYLÄ
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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UNIVERSITY OF JYVÄSKYLÄ
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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UNIVERSITY OF JYVÄSKYLÄ
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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UNIVERSITY OF JYVÄSKYLÄ
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 pointto-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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UNIVERSITY OF JYVÄSKYLÄ
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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UNIVERSITY OF JYVÄSKYLÄ
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
Master
queryResource
sendData
Application
Distributed
receivedData
startApplication
stopApplication
applicationRunning
readFile
writeFile
P2PDisCo
receivedData
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Chedar
queryResource
resourceReply
sendData
registerResource
sendData
Chedar
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P2PDisCo - Architecture
Chedar
node
Chedar
node
Master
Chedar
node
Chedar
node
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Chedar
node
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P2PDisCo - Architecture
Chedar
node
Chedar
node
Master
Chedar
node
Chedar
node
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Chedar
node
UNIVERSITY OF JYVÄSKYLÄ
P2PDisCo - Architecture
Chedar
node
Chedar
node
Query
Master
Chedar
node
Chedar
node
Chedar
node
Query: who has the resource ”NetSimulator” available?
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UNIVERSITY OF JYVÄSKYLÄ
P2PDisCo - Architecture
Chedar
node
Chedar
node
Reply
Master
Chedar
node
Chedar
node
Reply: I do!
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Chedar
node
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P2PDisCo - Architecture
Chedar
node
Chedar
node
Task
Master
Chedar
node
Chedar
node
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Chedar
node
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P2PDisCo - Architecture
Chedar
node
Chedar
node
Master
Chedar
node
Chedar
node
Computation…
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Chedar
node
UNIVERSITY OF JYVÄSKYLÄ
P2PDisCo - Architecture
Chedar
node
Chedar
node
Result
Master
Chedar
node
Chedar
node
Chedar
node
Results are sent back to the master node.
Calculation ends and everybody is happy.
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UNIVERSITY OF JYVÄSKYLÄ
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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UNIVERSITY OF JYVÄSKYLÄ
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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UNIVERSITY OF JYVÄSKYLÄ
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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UNIVERSITY OF JYVÄSKYLÄ
Thank You!
Any questions?