Abstract
Due to recent advances in the wide-area network technologies and low cost of computing resources, grid computing has become an active research area. The efficiency of a grid environment largely depends on the scheduling method it follows. This paper proposes a framework for grid scheduling using dynamic information and an ant colony optimization algorithm to improve the decision of scheduling. A notion of two types of ants -‘Red Ants’ and ‘Black Ants’ have been introduced. The purpose of red and Black Ants has been explained and algorithms have been developed for optimizing the resource utilization. The proposed method does optimization at two levels and it is found to be more efficient than existing methods.
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Kant, A., Sharma, A., Agarwal, S., Chandra, S. (2010). An ACO Approach to Job Scheduling in Grid Environment. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_35
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DOI: https://doi.org/10.1007/978-3-642-17563-3_35
Publisher Name: Springer, Berlin, Heidelberg
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