Abstract
This work presents the grouping of dependent tasks into a cluster using the Bayesian analysis model to solve the affinity scheduling problem in heterogeneous multicore systems. The non-affinity scheduling of tasks has a negative impact as the overall execution time for the tasks increases. Furthermore, non-affinity-based scheduling also limits the potential for data reuse in the caches so it becomes necessary to bring the same data into the caches multiple times. In heterogeneous multicore systems, it is essential to address the load balancing problem as all cores are operating at varying frequencies. We propose two techniques to solve the load balancing issue, one being designated “chunk-based scheduler” (CBS) which is applied to the heterogeneous systems while the other system is “quantum-based intra-core task migration” (QBICTM) where each task is given a fair and equal chance to run on the fastest core. Results show 30–55% improvement in the average execution time of the tasks by applying our CBS or QBICTM scheduler compare to other traditional schedulers when compared using the same operating system.
Highlights
Multicore systems usually operate in a shared resource environment
We propose two task scheduling techniques, named chunk-based scheduler” (CBS) and quantum-based intra-core task migration (QBICTM) based on even load balancing by considering the processing speed of all the cores
The second scheduler we propose in our work is the quantum-based intra-core task migration (QBICTM)
Summary
Multicore systems usually operate in a shared resource environment. They must cooperate in order to process sharable resources, especially the cache. Caches in multicore systems are private as well as shared. Task scheduling in heterogeneous multicore systems is mainly dependent on how the various tasks are distributed among all available cores [1]. The processing power of each core in a heterogeneous system is different, so distributing the workload according to each core’s maximum capacity or efficiency is essential. The computing power of each core has to be considered. Shared caches play a vital role in increasing the throughput of the overall system, but dependent tasks need to be scheduled concurrently [2]
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