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|Title: ||Dynamic peer-to-peer construction of clusters|
|Authors: ||Kadaru, Pranith Reddy|
|Supervisor: ||Graham, Peter (Computer Science)|
|Examining Committee: ||Card, Paul (Electrical and Computer Engineering) Eskicioglu, Rasit (Computer Science)|
|Graduation Date: ||February 2010|
|Issue Date: ||13-Jan-2010|
|Abstract: ||The use of parallel computing is increasing with the need to solve ever more complex problems. Unfortunately, while the cost of parallel systems (including clusters and small-scale shared memory machines) has decreased, such machines are still not within the reach of many users. This is particularly true if large numbers of processors are needed. A largely untapped resource for doing some, simpler, types of parallel computing
are temporarily idle machines in distributed environments. Such environments range from the simple (identical machines connected via a LAN) to the complex (heterogeneous machines connected via the Internet).
In this thesis I describe a system for dynamically clustering together similar machines distributed across the Internet. This is done in a peer-to-peer (P2P) fashion with the goal of ultimately forming useful compute clusters without the need for a heavily centralized software system overseeing the process. In this sense my work builds on so-called
"volunteer computing" efforts, such as SETI@Home but with the goal of supporting a different class of compute problems.
I first consider the characteristics that are necessary to form good clusters of shared machines that can be used together effectively. Second, I exploit simple clustering algorithms to group together appropriate machines using the identifieded characteristics. My system assembles workstations into clusters which are, in some sense, "close" in terms
of bandwidth, latency and/or number of network hops and that are also computationally similar in terms of processor speed, memory capacity and available hard disk space. Finally, I assess the conditions under which my proposed system might be effective via simulation using generated network topologies that are intended to reflect real-world characteristics. The results of these simulations suggest that my system is tunable to different conditions and that the algorithms presented can effectively group together appropriate machines to form clusters and can also manage those clusters effectively as the constituent machines join and leave the system.|
|Appears in Collection(s):||FGS - Electronic Theses & Dissertations (Public)|
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