# A Novel Multi-Task Performance Prediction Model for Spark
## 2 Related Work  
In recent years, the research and practice of exploring Spark performance prediction have been deepened. For example, Spark can be used to run big data queries, so query-wise performance prediction is also important. [PERSON] et al. [14] used Spark and Hadoop to cluster query datasets of various sizes and evaluate query performance. [PERSON] et al. [15] analyzed the impact of data size on the query execution time for Spark, which is a popular big data query framework. There are also various fields and methods proposed for Spark performance prediction. [PERSON] et al. [16] predicted and compared the performance of Streaming applications running on different configurations of stream processing frameworks and validated them with the Spark Streaming framework. [PERSON] et al. [17] provided a comprehensive performance evaluation of Spark Streaming backpressure. [PERSON] et al. [18] proposed two different parallelization models for performance prediction. [PERSON] et al. in [19] proposed a model to capture the execution behavior of tasks, phases, and jobs, and based on the model, implemented a prototype system. The system was used to collect and analyze various performance and system metrics such as execution time and CPU utilization. [PERSON] et al. in [20] explored the performance of a streaming media application under various adjustable parameters in Spark. [PERSON] et al. in [21] used multiple linear regression, MLR, to derive a correlation formula between cluster load and performance metrics. With the rapid development of machine learning and neural networks, Spark performance prediction models based on machine learning or neural networks have been proposed. Machine learning uses machine learning algorithms and techniques to build predictive models. It predicts job performance by learning and analyzing factors such as the input data, job characteristics, and execution environment. [PERSON] et al. [6] proposed a performance prediction model for Spark applications based on the machine learning algorithm AdaBoost. [PERSON] et al. in [8] presented a black-box model that uses various forms of machine learning methods to predict the execution time of Spark applications. [PERSON] et al. [22] presented a supervised machine learning model for predicting the performance of Spark applications. They studied and evaluated the utility of using supervised machine learning models to predict the performance of Spark applications. [PERSON] et al. [23] used a hierarchical neural network model with stage-awareness and predicted the performance of Spark in big data computing systems. [PERSON] et al. [1] used GBDT to predict the execution time of Spark jobs. [PERSON] et al. in [24] used machine learning to predict upcoming data loads. [PERSON] et al. in [11] used regression-based machine learning models to predict the performance of Spark-HBase applications. Machine-learning-based methods are able to automatically learn patterns and trends in data without manually designing rules or assumptions. [PERSON] et al. showed in [25] that gradient-promoted ML regressors are more accurate than any existing analytical model as long as the prediction horizon follows the training horizon. An extensive comparison of performance prediction accuracy based on machine learning and existing analytical models was also presented. The miscellaneous framework proposed by [PERSON] et al. in [26] can use multiple machine learning algorithms to automatically select appropriate datasets for caching. Machine-learning-based methods are highly adaptable. The constructed machine learning models can be adapted to different types of jobs and data with some generalization ability. In addition, machine learning-based methods can provide high prediction accuracy due to the large amount of data trained and model parameters optimized. However, machine-learning-based methods usually require a large amount of labeled data for training, and collecting and labeling data are time-consuming and resource-intensive.