A Performance Comparison of Data-aware Heuristics for Scheduling Jobs in Mobile Grid [Qualis B4]

MATÍAS HIRSCH; CRISTIAN MATEOS; JUAN MANUEL RODRIGUEZ; ALEJANDRO ZUNINO; YISEL GARÍ; DAVID MONGE

Córdoba

Conferencia; Proceedings of the XLIII Conferencia Latinoamericana de Informática (CLEI 2017); 2017

SADIO y CLEI

Given mobile devices´ ubiquity and capabilites, some researchers consider them as resource providers of distributed environments for running resource intensive software. Therefore, job scheduling has to deal with device singularities, such as energy constraints, mobility and unstable connectivity. Many existing schedulers consider at least one of these aspects, but their applicability strongly depends on information that is unavailable or difficult to estimate accurately, like job execution time. Other efforts do not assume knowing job CPU requirements but ignore energy consumption due to data transfer operations, which is not realistic for data-intensive applications. This work, on the contrary, considers the last as non negligible and known by the scheduler. Under these assumptions, we conduct a performance study of several traditional scheduling heuristics adapted to this environment, which are applied with the known information of jobs but evaluated along with job information unknown to the scheduler. Experiments are performed via a simulation software that employs hardware profiles derived from real mobile devices. Our work contributes to better understand both the capabilities and limitations of this kind of schedulers in mobile Grids.