A machine learning-based framework for predicting game server load


Ozer C., Cevik T., Gurhanli A.

MULTIMEDIA TOOLS AND APPLICATIONS, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume:
  • Publication Date: 2020
  • Doi Number: 10.1007/s11042-020-10067-5
  • Title of Journal : MULTIMEDIA TOOLS AND APPLICATIONS

Abstract

Server load prediction can be utilized for load-balancing and load-sharing in distributed systems. The use of machine learning (ML) algorithms for load estimation in distributed system applications can increase the availability and performance of servers. Hence, a number of machine learning algorithms have been applied thus far for server load estimation. This study focuses on increasing the performance of game servers by accurately predicting the workload of game servers in short, medium and long term prediction situations. While doing this, various machine learning techniques have been applied and the algorithms that give the best results are presented. In terms of implementation, companies using their servers and data centers can try to increase their level of satisfaction by using these algorithms. A prediction model is developed and the estimation performances of a number of fundamental ML methods i.e., Naive Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosted Trees (GBT), Support Vector Machine (SVM), Fast Large Margin (FLM), Convolutional Neural Network CNN are analyzed. The data used during the training stage is obtained by listening to the TCP/IP packet traffic and the real-data is extracted by performing an extensive analysis of the total transferred-data that includes also the payload. In the analysis phase, the goodput is considered in order to reveal exact resource requirements. Comprehensive simulations are performed under various conditions for high accuracy performance analysis. Experimental results indicate that the proposed ML-based prediction shows promising performance in terms of load prediction when compared to the common approaches present in the literature.