Predictive service placement in cloud using deep learning and frequent subgraph mining

Over the last few years, service placement has become a strategic and fundamental management operation that allows cloud providers to deploy and arrange their services on the high-performance computation/storage servers, while taking various constraints (e.g., resource usage, security levels, data transfer time, SLA) into consideration. Despite the huge number of service placement schemes, most of them are static and do not take the cloud changes into account. To cope with this issue, predicting the cloud zones’ performance and availability should precede the placement task. For this purpose, we adopt gated recurrent neural network as a deep learning variant that allows forecasting the next short-term resource consumption on cloud servers and predicting the future service migration traffic between them. Also, to place cloud services’ application/data components on the optimum cloud zones, the frequently used high-performance servers are selected by mining the graph-like placement history, i.e. previous placement plans. To do so, we propose a Frequent Subgraph Mining algorithm that is reinforced with a tuning method to increase the probability of executing the past placement schemes. Experimental results have proved that our predictive approach outperforms state-of-the-art placement schemes in terms of performance and prediction quality.