Abhishek Mishra

Today’s data centres have thousands of servers that belong to multiple vendors, and they vary vividly in terms of technology, model, generation and numerous peripherals like firmware, software, and other hardware components. It is a challenge for the data centre administrators to track activities being executed across these servers and the resource consumption at various stages of deployment, delivery, installation, and upgrades. This leads to a subsequent ripple where the data centre administrators find it tough to foresee GPU availability and/or consumption in their data centres. This poses a risk when they need resources to be available for critical activity in the future, but because of an overburdened data centre, the resources are unavailable, and the critical tasks fail. On the other hand, if the data centre always remains underutilized, the unused servers in the data centre become a liability and bring the ROI down. This not only leads to unmitigable risks but also has a monetary impact on the profitability of the organization. This also leads to poor resource planning because of the lack of visibility of future needs. This paper focuses on designing a forecasting solution for data centre admins. The solution includes performing data explorations and developing enough understanding of the various metrics of the servers in the data centre like CPU, IO, Power etc., and developing univariate/multivariate forecasting solutions using the time series data sets of these metrics. This paper proposes to explore TDM as well as GAM models for predicting behaviors of various factors of data centres and CSPs. This solution would assist the data centre admins to have clearer visibility of the future load/availability of the GPU in their data centres. By having so, they would be in a better state to understand the utilization metrics for their data centres and better financial planning for future investments based on the clear visibility of under/over-utilized resources. This will also lead to better data centre resource optimization and rationalization.