Impact of Qualitative and Quantitative errors of the job runtime estimation in backfilling based scheduling policies

Estimation or prediction accuracy in backfilling based policies it’s an important issue that has high impact on the system performance. However it’s not clear which is the required precision of such estimations, moreover it’s not clear which kind of errors are critical when scheduling the jobs that are queued in the local systems. In this paper we present a deeper analysis of the impact of the estimation errors in the scheduling. The study is based on several criteria. For instance the implications of carrying out accurate predictions of determined kind of jobs, having qualitative errors or quantitative errors and so on. As far as we know, all the works that have analyzed the impact of the estimation error in the backfilling policies based they results and conclusions in a set of well known metrics, such as the average slowdown or the wait time. We present a new parametrical metric that evaluates the index of satisfaction of the user. As shown in the paper, conclusions obtained when evaluating backfilling based policies with this metric may differ with other conclusions obtained, for example, with average slowdown.