Natural Computing

, Volume 12, Issue 3, pp 339–351 | Cite as

Synchronous cellular automata-based scheduler initialized by heuristic and modeled by a pseudo-linear neighborhood



Cellular automata (CA) are able to produce a global behavior from local interactions between their units. They have been applied to the task scheduling problem in multiprocessor systems in a very distinguished way. As this problem is NP-Complete, heuristics and meta-heuristics are usually employed. However, these techniques must always start the scheduling process from scratch for each new parallel application given as input. On the other hand, the main advantage to use CA for scheduling is the discovery of rules while solving one application and their subsequent reuse in other instances. Recently studies related to CA-based scheduling have shown relevant approaches as the use of synchronous updating in CA evolution and good results in multiprocessor systems with two processors. However, some aspects, such as the low performance of CA-based schedulers in architectures with more than two processors and during the reuse of the discovered rules, need to be investigated. This paper presents two new models to improve CA-based scheduling to deal with such aspects. The first proposal refers to the employment of a construction heuristic to initialize CA evolution and the second one is a new neighborhood model able to capture the dependence and relations strength among the tasks in a very simple way. It was named pseudo-linear neighborhood. An extensive experimental evaluation was performed using graphs of parallel programs found in the literature and new ones randomly generated. Experimental analysis showed the combined application of both techniques makes the search for CA transition rules during learning stage more robust and leads to a significant gain when considering the reuse of them on real-world conditions.


Cellular automata Multiprocessor task scheduling Evolutionary search Synchronous updating Knowledge extraction Pseudo-linear neighborhood 


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© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Instituto de Ciências Matemáticas e de ComputaçãoUniversidade de São PauloSão CarlosBrazil
  2. 2.Faculdade de ComputaçãoUniversidade Federal de UberlândiaUberlândiaBrazil

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