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A bio-inspired distributed algorithm to improve scheduling performance of multi-broker grids

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The scheduling in grids is known to be a NP-hard problem. The distributed deployment of nodes, their heterogeneity and their fluctuations in terms of workload and availability make the design of an effective scheduling algorithm a very complex issue. The scientific literature has proposed several heuristics able to tackle this kind of optimization problem using techniques and strategies inspired by nature. The algorithms belonging to ant colony optimization (ACO) paradigm represent an example of these techniques: each one of these algorithms uses strategies inspired by the self-organization ability of real ants for building effective grid schedulers. In this paper, the authors propose an on line, non-clairvoyant, distributed scheduling solution for multi-broker grid based on the alienated ant algorithm (AAA), a new ACO inspired technique exploiting a “non natural” behavior of ants and an inverse interpretation of pheromone trails. The paper introduces the proposed algorithm, explains the differences with other classical ACO approaches, and compares AAA with two different algorithms. The results of simulations show that the AAA guarantees good performance in terms of makespan, average queue waiting time and load balancing capability.

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  1. Usually, if it is not differently specified in JDL (Pacini) files, each UI refers to a specific “default” RB.

  2. In terms of previous submitted jobs.

  3. Inversely proportional to the quantity of pheromone.

  4. In these systems several RBs can submit their jobs to the same CEs.


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Correspondence to Giovanni Morana.

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Di Stefano, A., Morana, G. A bio-inspired distributed algorithm to improve scheduling performance of multi-broker grids. Nat Comput 11, 687–700 (2012).

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