CAFE and SOUP: Toward Adaptive VDI Workload Prediction

Abstract

For Virtual Desktop Infrastructure (VDI) system, effective resource management is rather important where turning off spare virtual machines would help save running cost while maintaining sufficient virtual machines is essential to secure satisfactory user experience. Current VDI resource management strategy works in a passive manner by either reactively driving available capacity based on user demands or following manually configured schedules, which may lead to unnecessary running costs or unsatisfactory user experience. In this article, we propose a first attempt toward proactive VDI resource management, where two adaptive learning approaches for VDI workload prediction are proposed by learning from multi-grained historical features. For non-persistent desktop pool, based on the aggregation session count of pool-sharing users, the CAFE approach induces a pool-level workload predictive model by utilizing coarse-to-fine historical features extracted from aggregation workload data. For persistent desktop pool, based on the session connection status of individual users within the same pool, the SOUP approach induces user-level workload predictive model by incorporating encoded multi-grained features extracted from the logon behavior of individual users into an aggregation pool-level model. Extensive experiments on datasets of real VDI customers and electricity load evidently verify the effectiveness of the proposed adaptive approaches for VDI workload prediction as well as other workload prediction tasks.