A hybrid heuristic for load-balanced scheduling of heterogeneous workload on heterogeneous systems

Abstract

Load-balanced scheduling deals with the uniform allocation of workload to a set of computational resources in order to optimize some characteristic metrics such as makespan, resource utilization and relative load imbalance. As such, the load balancing is an NP-hard problem which becomes more complex when heterogeneity in the workload and the computing resources are introduced. Workload heterogeneity is defined by the types of the workload whereas CPU resources can be heterogeneous in terms of memory/cache hierarchy, clock speed, etc. For load balancing in a truly heterogeneous multicore system, this work proposes a model by incorporating a related heuristic into Genetic Algorithm (GA) to generate the priorities of the workload and the computing resources by exploiting their heterogeneity characteristics. The priorities play a significant role in the effective mapping of the workload on the computing resources. A good number of simulation experiments are carried out to study the performance of the proposed model besides comparing it with some contemporary GA-based models. Results indicate that incorporating the relevant heuristic into GA makes a significant load-balanced scheduling, especially for heavy workload applications.