|Project Description: ||As computational collectives along the spectrum from loosely-coupled Grids to tightly-coupled clusters increase in size and complexity, traditional centralized methods for assigning tasks to hosts will become too time-consuming to be considered effective in these time-sensitive environments. Through the use of centralized heuristics, the overall time to solution can be reduced, albeit at the expense of solution quality. Meta-heuristics, such as artificial immune systems (AIS), have demonstrated themselves to be viable alternatives to more traditional heuristic approaches and capable of efficiently performing task-to-host assignment in both static and dynamic environments. However, it is as yet unclear whether solutions provided by more exotic meta-heuristic approaches outperform traditional heuristic techniques. This project compared three traditional, centralized heuristics: Smallest Job First (SJF), Largest Job First (LJF), and Best Fit First (BFF) to an AIS-based approach called ALARM: The Asynchronous Lymphocytic Agent-based Resource Manager. These comparisons help to demonstrate the feasibility of using the more exotic ALARM scheduling approach in massive scale computational collectives, specifically in tightly-coupled multicomputer “cluster” environments. A discrete, event-driven simulation program was written in C to perform comparisons of these four techniques based on five metrics: Throughput, turnaround time, wait time, load balance and utilization. Results of these simulations and rankings of the four techniques based on performance in each of the five metrics are provided.