DECENTRALIZED AND SCALABLE RESOURCE MANAGEMENT FOR DESKTOP GRIDS

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2009

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Abstract

The recent growth of the Internet and the CPU power of personal

computers and workstations enables desktop grid computing to

achieve tremendous computing power with low cost, through

opportunistic sharing of resources. However, traditional

server-client Grid architectures have inherent problems in robustness,

reliability and scalability. Researchers have therefore recently

turned to Peer-to-Peer (P2P) algorithms in an attempt to address these

issues.

I have designed and evaluated a set of protocols that implement a

scalable P2P desktop grid computing system for executing Grid

applications on widely distributed sets of resources. Such

infrastructure must be decentralized, robust, highly available and

scalable, while effectively mapping application instances to available

resources throughout the system (called matchmaking).

First of all, I address the problem of efficient matchmaking of jobs

to available system resources by employing customized

Content-Addressable Network (CAN) where each resource type corresponds

to a distinct dimension. With this approach, incoming jobs are matched

with system nodes through proximity in an N-dimensional resource

space. Second, I provide comprehensive load balancing mechanisms that

can greatly improve overall system throughput and response time

without using any centralized control or information about the

system. Finally, to remove any hot spots in the system where a small

number of nodes are processing a lot of system maintenance work, I

have designed a set of optimizations to minimize overall system

overheads and distribute them fairly among available system nodes. My

ultimate goal is to ensure that no node in the system becomes much

more heavily loaded than others, either because of executing jobs or

from system maintenance tasks. This is because every node in our

system is a peer, so that no node is acting as a pure server or a pure

client.

Throughout extensive experimental results, I show that the resulting

P2P desktop grid computing system is scalable and effective so that it

can efficiently match any type of resource requirements for jobs

simultaneously, while balancing load among multiple candidate nodes.

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