Characterization of task allocation techniques in data centers based on information theory

We present a comprehensive characterization of task allocation techniques in data centers based on information theory, focusing specifically on Shannon Entropy and Statistical Complexity. This study investigates the potential benefits and limitations of using information theory-based integrated into different task allocation techniques. We conduct experiments using a realistic simulation environment. We evaluate the number of tasks allocated over time and the evolution of queue size over time. For this purpose, we used the Google Dataset. This trace represents 29 days' of information on a cluster of about 12.5k machines. It contains detailed information on job submissions, task execution, resource usage, and scheduling decisions. Our findings demonstrate the potential of information theory-based to identify the task allocation process using different strategies.