# A Novel Multi-Task Performance Prediction Model for Spark
## 1 Introduction  
Spark [1] has emerged as one of the most popular big data computing frameworks in recent years. It finds significant applications in diverse scenarios such as social media [2], smart cities [3], and intelligent transportation [4], delivering robust and consistent support for big data tasks in these areas. However, the ever-growing scale and complexity of big data applications [5] in these domains leads to substantial resource consumption. Efficiently processing these data and optimizing resource utilization is not only a technical challenge but also vital to ensuring smooth computational tasks while meeting business demands. Against this backdrop, predicting Spark performance in advance becomes crucial for making accurate resource allocation and scheduling decisions in real-world applications. Yet, predicting Spark performance is intricate, involving various considerations such as parameter settings, application types, and data scales. Thus, delving into and enhancing the performance prediction methods [6] for Spark to meet the demands of practical applications is a central goal in current research. In recent years, Spark performance prediction has faced many challenges, such as complex computing environments, large-scale data processing, variable application scenarios, and dynamically changing cluster configurations. In addition, there are significant differences in standardized metrics for Spark performance prediction. These issues make Spark performance prediction challenging. The big data application computing environment is complex and contains numerous parameters such as data distribution, the logical structure of the application, and the configuration of hardware and software. These parameterscan have an impact on the performance of the application. Therefore, performance prediction models need to consider these parameters for more accurate prediction. To solve the above problems, rule-based methods, statistics-based methods, and analytical model-based methods have been proposed successively. [PERSON] et al. [7] proposed an analytical and simulation-based approach for predicting Spark performance. [PERSON] et al. [8] proposed a method based on a black-box model to forecast the execution time of Spark applications. [PERSON] et al. [9] presented an approach that combines model and log analysis to predict the execution time of Spark applications. [PERSON] et al. [10] introduced a gray-box modeling technique that integrates both white- and black-box models for predicting the runtime of Spark jobs. Based on the work of some of the researchers mentioned above, it is known that Spark application execution time prediction is a core element of Spark performance prediction. Therefore, the main research goal of this paper is to implement a method for predicting Spark application execution time. However, there are some problems and limitations of these methods mentioned above. Rule-based methods rely heavily on the experience and knowledge of domain experts. It requires constant adjustment and updating of rules to efficiently handle new or complex operations. Statistics-based methods rely on a large amount of historical data. It requires manual feature selection and model selection. This approach may affect the prediction accuracy when dealing with high-dimensional, nonlinear, and large-scale datasets. Recently, machine learning and deep learning have attracted much attention due to their significant advantages in image recognition, speech recognition, and natural language processing. In addition, deep learning is able to automatically learn high-level features from raw data, which has advantages when dealing with high-dimensional, nonlinear, and large-scale data. [PERSON] et al. [11] proposed a regression-based machine learning model to predict the performance of Spark-HBase applications on Hadoop. [PERSON] et al. [12] proposed a gray-box approach to estimate the execution time of an application on a Spark cluster. In addition, they discussed a machine-learning-based method to build the model.