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Title:
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AI GAME PLAYING APPROACH FOR FAST PROCESSOR ALLOCATION IN HYPERCUBE SYSTEMS USING VEITCH DIAGRAM |
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Author(s):
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Srinivasan T , Srikanth Pjs , Praveen K , Harish Subramaniam L |
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ISBN:
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972-99353-6-X |
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Editors:
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Nuno Guimarães and Pedro Isaías |
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Year:
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2005 |
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Edition:
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1 |
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Keywords:
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External / Internal Fragmentation, Graph Coloring, Hypercube, Incomplete subcube, Veitch diagram, Penalty Factor, Processor Allocation / Deallocation, AI G |
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Type:
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Full Paper |
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First Page:
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65 |
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Last Page:
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72 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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This paper proposes a method called AI Game Playing Approach for Fast Processor Allocation in Hypercube Systems using Veitch diagram (AIPA) which achieves a fast and complete subcube recognition with much less complexity than the other existing allocation policies such as Buddy, Gray Code (GC), Modified Buddy, Modified Gray Code, Free List, Heuristic Processor Allocation (HPA) and Tree Collapsing (TC). The basic idea of this strategy is to find out a free subcube that can fit the Veitch diagram also called as the Karnaugh map (K-map). This is aided with an AI Game playing approach to ensure optimality. The cells in the Veitch diagram attribute the processors. This strategy can be implemented efficiently by using a graph coloring approach with a resultant penalty factor computation. This algorithm deals with cubic as well as non-cubic allocation. It is not only statically optimal but also optimal in a dynamic environment. Extensive performance analyses were carried out and outcomes are discussed comparatively with other allocation strategies. It is shown that our approach proves to be better in allocation and deallocation costs. The algorithm also utilizes memory efficiently and minimizes the system fragmentation. Simulation results illustrate that the AIPA significantly improves the performance. |
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