Dynamic scheduling for switched processing systems with substantial service-mode switching times

Switched Processing Systems (SPS) represent canonical models for many communication and computer systems. Over the years, much research has been devoted to developing the best scheduling policies to optimize the various performance metrics of interest. These policies have mostly originated from the well–known MaxWeight discipline, which at any point in time switches the system into the service mode possessing ‘maximal matching’ with the system state (e.g., queue–length, workload, etc.). However, for simplicity it is often assumed that the switching times between service modes are ‘negligible’ but this proves to be impractical in some applications. In this study, we propose a new scheduling strategy (called the Dynamic Cone Policy) for SPS, which includes substantial service–mode switching times. The goal is to maximize throughput and maintain system stability under fairly mild stochastic assumptions. For practical purposes, an extended scheduling strategy (called the Practical Dynamic Cone Policy) is developed to reduce the computational complexity of the Dynamic Cone Policy and at the same time mitigate job delay. A simulation study shows that the proposed practical policy clearly outperforms another throughput–maximizing policy called BatchAdapt, both in terms of the average and the 95th percentile of job delay for various types of input traffic.