Run-time mapping algorithm for dynamic workloads using association rule mining

Abstract

Abstract Task mapping exploration plays an important role in the high performance achieved by heterogeneous multi-processor system-on-chip (MPSoC) platforms. The dynamic of application workloads in modern MPSoC-based embedded systems are consistently growing. Nowadays, the execution of different applications is done concurrently, and these applications compete for resources in such systems. To cope with the dynamism of application workloads at runtime and improve the efficiency of the underlying system architecture, this paper presents a hybrid task mapping algorithm for multimedia applications. That consists of two phases: design-time and run-time. During design-time, static mapping exploration is performed, and the applications are clustered based on their efficient mapping, then a set of rules for mapping is extracted by Association Rule Mining techniques. During run-time, when a new application enters to the system, this application is classified to one of the existing clusters using the rule sets extracted at design-time phase. The objective of application mapping is to minimize execution time in a predefined budget of energy consumption. A heterogeneous MPSoC system is used to evaluate the proposed algorithm. The experimental results revealed that during run-time by using the proposed algorithm, suitable resources regarding energy consumption and execution time are selected for mapping.