Optimizing Workload Category for Adaptive Workload Prediction in Service Clouds

It is important to predict the total workload for facilitating auto scaling resource management in service cloud platforms. Currently, most prediction methods use a single prediction model to predict workloads. However, they cannot get satisfactory prediction performance due to varying workload patterns in service clouds. In this paper, we propose a novel prediction approach, which categorizes the workloads and assigns different prediction models according to the workload features. The key idea is that we convert workload classification into a 0–1 programming problem. We formulate an optimization problem to maximize prediction precision, and then present an optimization algorithm. We use real traces of typical online services to evaluate prediction method accuracy. The experimental results indicate that the optimizing workload category is effective and proposed prediction method outperforms single ones especially in terms of the platform cumulative absolute prediction error. Further, the uniformity of prediction error is also improved.