A Data Analytics Based Approach to Cloud Resource Auto-Scaling

Fang Hao, Murali S. Kodialam, Sarit Mukherjee, T. V. Lakshman

Abstract: Multiplexing resources is the core savings principle upon which the economic model of the Cloud is built. Cloud customers can flexibly purchase additional resources when needed, and trim these down when the need has past, while Cloud providers can direct resources when and where customers might require. One aspect which poses a challenge to this capability is the allocation process itself, which can be costly in terms of time and energy. Indeed, both provider and customer would prefer if resource allocation would be continuous, fast and with low energy overhead. Since this is not the case, there is an inherent tension between limiting the number of allocation events and efficient resource utilization.This paper considers this tension using several different models, and proposes a history-based dynamic allocation scheme that minimizes the number of resource allocation transition points for both average and adversarial use cases. We prove performance bounds and use extensive simulation to study the performance of our scheme.