Fractional Rider Deep Long Short Term Memory Network for Workload Prediction-Based Distributed Resource Allocation Using Spark in Cloud Gaming

Abstract

The modern development in cloud technologies has turned the idea of cloud gaming into sensible behaviour. The cloud gaming provides an interactive gaming application, which remotely processed in a cloud system, and it streamed the scenes as video series to play through network. Therefore, cloud gaming is a capable approach, which quickly increases the cloud computing platform. Obtaining enhanced user experience in cloud gaming structure is not insignificant task because user anticipates less response delay and high quality videos. To achieve this, cloud providers need to be able to accurately predict irregular player workloads in order to schedule the necessary resources. In this paper, an effective technique, named as Fractional Rider Deep Long Short Term Memory (LSTM) network is developed for workload prediction in cloud gaming. The workload of each resource is computed based on developed Fractional Rider Deep LSTM network. Moreover, resource allocation is performed by fractional Rider-based Harmony Search Algorithm (Rider-based HSA). This Fractional Rider-based HSA is developed by combining Fractional calculus (FC), Rider optimization algorithm (ROA) and Harmony search algorithm (HSA). Moreover, the developed Fractional Rider Deep LSTM is developed by integrating FC and Rider Deep LSTM. In addition, the multi-objective parameters, namely gaming experience loss QE, Mean Opinion Score (MOS), Fairness, energy, network parameters, and predictive load are considered for efficient resource allocation and workload prediction. Additionally, the developed workload prediction model achieved better performance using various parameters, like fairness, MOS, QE, energy and delay. Hence, the developed Fractional Rider Deep LSTM model showed enhanced results with maximum fairness, MOS, QE of 0.999, 0.921, 0.999 and less energy and delay of 0.322 and 0.456.