COST: A Cluster-Oriented Scheduling Technique for Heterogeneous Multi-cores

Development of efficient resource allocation strategies for real-time tasks on heterogeneous platforms has traditionally proved to be a challenging as well as a computationally expensive problem. However, strategies which can efficiently schedule real-time task sets on generic heterogeneous platforms having an arbitrary number of processor types, are rare. Most of the existing strategies are oriented towards systems with restricted number of processing core types. Hence, this paper proposes an effective low-overhead heuristic approach called COST, for scheduling a set of periodic tasks executing on a heterogeneous multi-core system. The proposed strategy works in three-phases namely, Core Clustering, Task Partitioning, and Task Scheduling. The Core Clustering step attempts to combine the available processing cores into a group of clusters. Each cluster consists of two cores and a disjoint subset of the given task set is assigned to it. The tasks assigned to a cluster are then allocated to the processing cores of the cluster and scheduled in a fair manner. Experimental studies show that our proposed scheme provides high resource utilisation with satisfactory acceptance ratios for a wide-range of task sets.