Mbarka Soualhia

Mbarka Soualhia, Foutse Khomh, Sofiene Tahar

The Hadoop scheduler is a centerpiece of Hadoop, the leading processing framework for data-intensive applications in the cloud. Given the impact of failures on the performance of applications running on Hadoop, testing and verifying the performance of the Hadoop scheduler is critical. Existing approaches such as performance simulation and analytical modeling are inadequate because they are not able to ascertain a complete verification of a Hadoop scheduler. This is due to the wide range of constraints and aspects involved in Hadoop. In this paper, we propose a novel methodology that integrates and combines simulation and model checking techniques to perform a formal verification of Hadoop schedulers, focusing on the following properties: schedulability, fairness and resources-deadlock freeness. We use the CSP language to formally describe a Hadoop scheduler, and the PAT model checker to verify its properties. Next, we use the proposed formal model to analyze the scheduler of OpenCloud, a Hadoop-based cluster that simulates the Hadoop load, in order to illustrate the usability and benefits of our work. Results show that our proposed methodology can help identify several tasks failures (up to 78%) early on, i.e., before the tasks are executed on the cluster.

Mbarka Soualhia, Foutse Khomh, Sofiene Tahar

Cloud Computing has emerged as a key technology to deliver and manage computing, platform, and software services over the Internet. Task scheduling algorithms play an important role in the efficiency of cloud computing services as they aim to reduce the turnaround time of tasks and improve resource utilization. Several task scheduling algorithms have been proposed in the literature for cloud computing systems, the majority relying on the computational complexity of tasks and the distribution of resources. However, several tasks scheduled following these algorithms still fail because of unforeseen changes in the cloud environments. In this paper, using tasks execution and resource utilization data extracted from the execution traces of real world applications at Google, we explore the possibility of predicting the scheduling outcome of a task using statistical models. If we can successfully predict tasks failures, we may be able to reduce the execution time of jobs by rescheduling failed tasks earlier (i.e., before their actual failing time). Our results show that statistical models can predict task failures with a precision up to 97.4%, and a recall up to 96.2%. We simulate the potential benefits of such predictions using the tool kit GloudSim and found that they can improve the number of finished tasks by up to 40%. We also perform a case study using the Hadoop framework of Amazon Elastic MapReduce (EMR) and the jobs of a gene expression correlations analysis study from breast cancer research. We find that when extending the scheduler of Hadoop with our predictive models, the percentage of failed jobs can be reduced by up to 45%, with an overhead of less than 5 minutes.