As the most widely used parallel job scheduling strategy, EASY backfilling achieved great success, not only because it can balance fairness and performance, but also because it is universally applicable to most HPC systems. However, unfairness still exists in EASY. Our simulation shows that a blocked job can be delayed by later jobs for more than 90 hours on real workloads. Additionally, directly employing runtime prediction techniques in EASY would lead to a serious situation called reservation violation. In this paper, we aim at guaranteeing strict fairness (no job is delayed by any jobs of lower priority) while achieving attractive performance, and employing prediction without causing reservation violation in parallel job scheduling. We propose two novel strategies, namely, shadow load preemption (SLP) and venture backfilling (VB), which are integrated into EASY to construct preemptive venture EASY backfilling (PV-EASY). Experimental results on three real HPC workloads demonstrate that PV-EASY is more attractive than EASY in parallel job scheduling, from both academic and industry perspectives.

As the most widely used parallel job scheduling strategy in production schedulers, EASY has achieved great success, not only because it can balance fairness and performance, but also because it is universally applicable to most HPC systems. However, unfairness still exists in EASY. For real workloads used in this work, our simulation shows that a blocked job can be delayed by later jobs for more than 90 hours. In addition, EASY cannot directly employ parallel job runtime prediction techniques, because this would lead to a serious situation called reservation violation.

Predicting grid performance is a complex task because heterogeneous resource nodes are involved in a distributed environment. Long execution workload on a grid is even harder to predict due to heavy load fluctuations. In this paper, we use Kalman filter to minimize the prediction errors. We apply Savitzky-Golay filter to train a sequence of confidence windows. The purpose is to smooth the prediction process from being disturbed by load fluctuations. We present a new adaptive hybrid method (AHModel) for load prediction guided by trained confidence windows. We test the effectiveness of this new prediction scheme with real-life workload traces on the AuverGrid and Grid5000 in France. Both theoretical and experimental results are reported in this paper. As the lookahead span increases from 10 to 50 steps (5 minutes per step), the AHModel predicts the grid workload with a mean-square error (MSE) of 0.04-0.73 percent, compared with 2.54-30.2 percent in using the static point value autoregression (AR) prediction method. The significant gain in prediction accuracy makes the new model very attractive to predict Grid performance. The model was proved especially effective to predict large workload that demands very long execution time, such as exceeding 4 hours on the Grid5000 over 5,000 processors. With minor changes of some system parameters, the AHModel can apply to other computational grids as well. At the end, we discuss extended research issues and tool development for Grid performance prediction.

Long term load prediction can assist task scheduling and load balancing greatly in distributed environment such as computational grid. Due to the dynamic property of grid environment, fixed-parameter prediction model can not exert its forecast capability completely. In this paper we first observe and analyze parameters' impact on prediction accuracy for our previous long term load prediction hybrid model (HModel) in detail. And then, a parameter-level adaptive method based on previous analysis is proposed in order to make HModel adapt to the time-varying characteristics of load in computational grid. The results of the experiments demonstrate that our adaptive hybrid model (AHModel) outperforms the widely used autoregressive (AR) model in long term load prediction significantly, and it also achieves obvious reduction in prediction mean square error comparing with HModel which uses fixed parameter value.