One participant described volunteers who would happily agree to every proposed task, but were too overextended to follow through on anything.
In addition, it is fairly easy to change the program so as to carry out a new or revised set of tasks should the nature of the process change or the previously proposed system prove to be inadequate for the proposed task.
Algorithm 1 shows the proposed task scheduling algorithm based on ACO with slave ants.
In this paper, we propose task allocation algorithms to deal with these issues.
The task scheduler, on which the proposed task scheduling algorithm resides, is responsible for deciding the task allocation to resources.
Finally, performance analysis is adopted to compare the proposed task migration schemes.
Experimental results on a Myrinet cluster system show that the proposed task scheduling approach outperforms other processor mapping methods.
Then, we present details of system model for SACO and the proposed task scheduling algorithm in "Slave ants based ant colony optimization algorithm for task scheduling".
This shows the efficiency of the proposed task scheduling algorithm and the effectiveness of the pheromone update scheme based on slave ants.
This study also compares set of machine-learning algorithms (Random Forest and Support Vector Machine) and protein descriptors (Structure-based and Sequence-based) for the proposed task.
