Comprehensive Scheduling Algorithm for Asymmetric Multi-Core Processors

Abstract

This research focuses on the problem of operating system (OS) scheduling on asymmetric multi-core processors (AMP). A task scheduling model based on linear programming is proposed. Several attributes of AMP factors are taken into account in this model.Scheduling principles of behavior matching, migration avoiding, and load balancing are adhered as well. A comprehensive scheduling algorithm is also proposed based on the model. The algorithm has two parts: an integrated workload characterization, which proposes integrated behavior to measure the global and local behaviors of tasks comprehensively, and an integrated behavior-based scheduling algorithm, which efficiently utilizes the asymmetric multi-core processors without frequent task migration. This guarantees the load balance between cores. In addition, the algorithm achieves universality with a flexible parameter adjustment mechanism. It is an algorithm to achieve universality as well as the first to handle the global and local behaviors of tasks comprehensively. The evaluation on real platform demonstrates that the algorithm is universal for different conditions, and it always outperforms other scheduling algorithms on asymmetric multi-core processors (by 6%~22%).

Design and theoretical analysis of virtual machine placement algorithm based on peak workload characteristics

“…The image of σ 2 = 2 is slightly gentle with large peak span ranging mainly in (Kim et al 2013;Chen et al 2014) and thus the maximum is less than 0.4. The image of σ 2 = 1 is in between them.…”

“…This overhead remains steady as the number of consolidating VMs grows. Our team conducted a lot of research in the areas of resource management about cloud computing and VM resource scheduling optimization (Lin et al 2011(Lin et al , 2012(Lin et al , 2013(Lin et al , 2015Chen et al 2014). In the paper (Lin et al 2013), we modeled the energy optimization problem of virtual machine resource scheduling in a heterogeneous cloud data center.…”