Affinity Based Scheduling Using Bayesian Model and Load Balancing in Multicore Systems

Problems in the shared caches in multicore systems arise due to the non-affinity scheduling. Tasks are scheduled without considering the possible dependencies they have on each other. It has a negative effect on the overall execution time of the tasks. In this paper, we have proposed affinity based scheduling using Bayesian analysis model and creating groups or clusters of dependent tasks. Clusters are then allocated fairly and equally among the multiple cores. Load balancing is performed on the homogeneous system by feeding all the cores in a multicore architecture from a queue-like pool of tasks. We have used another technique for load balancing by defining a chunk size for each core. Results showed an improvement in an overall execution time of a process by 5.57% and of an individual task by 9.06% on average in comparison with other traditional schedulers used by the operating system for a factorial program. For a quick sort program, overall execution time of a process has been reduced by 1.13% while for an individual task by 1.5%.