Machine-Learning Based Memory Prediction Model for Data Parallel Workloads in Apache Spark

A lack of memory can lead to job failures or increase processing times for garbage collection. However, if too much memory is provided, the processing time is only marginally reduced, and most of the memory is wasted. Many big data processing tasks are executed in cloud environments. When renting virtual resources in a cloud environment, it is necessary to pay the cost according to the specifications of resources (i.e., the number of virtual cores and the size of memory), as well as rental time. In this paper, given the type of workload and volume of the input data, we analyze the memory usage pattern and derive the efficient memory size of data-parallel workloads in Apache Spark. Then, we propose a machine-learning-based prediction model that determines the efficient memory for a given workload and data. To determine the validity of the proposed model, we applied it to data-parallel workloads which include a deep learning model. The predicted memory values were in close agreement with the actual amount of required memory. Additionally, the whole building time for the proposed model requires a maximum of 44% of the total execution time of a data-parallel workload. The proposed model can improve memory efficiency up to 1.89 times compared with the vanilla Spark setting.

THE USE OF MEDIA TEXTS IN RUSSIAN LANGUAGE TEACHING

At present, we observe active development of new methods and strategies of teaching foreign languages, in which mass media materials play an important role because they provide authentic resources that offer natural language environment and enrich the curriculum and syllabus. These resources can be easily adapted to suit different levels of language proficiency, interests and needs of the students. The study attempts to substantiate the necessity and importance of using various types of authentic media materials in the classroom and analyzes the challenges teachers may face in the process of teaching. The aim of the study is to address both the theoretical background and the practical issues of application of media texts at the lessons of Russian as a foreign language. They enhance not only language but and socio-cultural proficiency of the students and their understanding of linguistic phenomena such as various stylistic figures of speech, phraseological units, linguistic means of manipulation, etc. Mass media resources can be used to develop media literacy, critical thinking and analytical skills. This study is based on the use of general scientific methods such as analysis, description and interpretation. The authors fall back on scholarly literature and their personal experience of teaching Russian. The article suggests using practical media-based approaches to develop language skills and psychological properties, such as memory, prediction skills, and speed of response to linguistic stimuli in undergraduate university students of Russian as a second language. The opportunities and methods regarded in the study can be used in any other classroom of Russian as a second language.

Performance Prediction by an SVM with a Firefly Optimization Method

Since software system is becoming more and more complex than before, performance degradation and even abrupt download, which are called software aging phenomena, bring about a great deal of economic loss. To counter these problems, some methods are used. Support vector machine is an effective method to tackle software aging problems, but its performance is influenced by the selection of hyper-parameters. A method is proposed to optimize the hyper-parameter selection of support vector machine in this work. The proposed method which is used as a training algorithm to optimize the parameter selection of support vector machine, utilizes the global exploration power of firefly method to achieve faster convergence and also a better accuracy. In the experiment, we use two metrics to test the effect of the proposed method. The results indicate that the presented method owns the highest accuracy in both the available memory prediction and heap memory prediction of Web server for software aging predictions.