Research Area:  Cloud Computing

Abstract:

Heterogeneous computers require a well-distributed workload to operate efficiently. When possible, this load balancing procedure should redistribute the workload with minimal knowledge of the system architecture, to reduce overhead. We propose a generic dynamic load balancing technique for iterative problems, independent from the resource to optimize. Proof of this generalization is given through formalization of the designed technique. A heuristic algorithm is defined based upon this formalization, with a structure that facilitates different objective functions. As a result, swapping the objective function can be done with relatively low effort. This heuristic is implemented to minimize energy consumption in an application. We use this application to solve three different dynamic programming problems with multiple GPUs. The implementation is described and then compared against two different workloads, the homogeneous distribution and another dynamic load balancing technique. Our experimentation shows good results in minimizing the overall energy consumption with low overhead.

Keywords:  

Author(s) Name:   Alberto Cabrera, Alejandro Acosta, Francisco Almeida & Vicente Blanco

Publisher name:  Springer

DOI:  10.1007/s11227-018-2718-6

Volume Information:  volume 75, pages 1610–1624 (2019)

Paper Link:   https://link.springer.com/article/10.1007/s11227-018-2718-6