Feedback-Based Resource Allocation in MapReduce-Based Systems

Abstract

Containers are considered an optimized fine-grain alternative to virtual machines in cloud-based systems. Some of the approaches which have adopted the use of containers are the MapReduce frameworks. This paper makes an analysis of the use of containers in MapReduce-based systems, concluding that the resource utilization of these systems in terms of containers is suboptimal. In order to solve this, the paper describes AdaptCont, a proposal for optimizing the containers allocation in MapReduce systems. AdaptCont is based on the foundations of feedback systems. Two different selection approaches, Dynamic AdaptCont and Pool AdaptCont, are defined. Whereas Dynamic AdaptCont calculates the exact amount of resources per each container, Pool AdaptCont chooses a predefined container from a pool of available configurations. AdaptCont is evaluated for a particular case, the application master container of Hadoop YARN. As we can see in the evaluation, AdaptCont behaves much better than the default resource allocation mechanism of Hadoop YARN.

Human Resource Planning and Configuration Based on Machine Learning

“…Human resource demand forecasting generally needs to follow the principle of correlation and the principle of inertia. e principle of correlation is based on the correlation between the research objects and uses other objects to predict the targeted object [5][6][7][8][9][10][11][12]. For example, there is a clear correlation between A, B, and C. In the research process, rich data of A, B, and C can be obtained.…”