Achieving cost efficiency using CaaS model in the cloud

Caching has become the key technology used for bridging the performance gap across memory hierarchies via temporal or spatial localities; in particular, the effect is prominent in disk storage systems. Applications that involve heavy I/O activities, which are common in the cloud, probably benefit the most from caching. The use of local volatile memory as cache might be a natural alternative, but many well-known restrictions, such as capacity and the utilization of host machines, hinder its effective use. We present the cache as a service (CaaS) model as an optional service to typical infrastructure service offerings. Specifically, the cloud provider sets aside a large pool of memory that can be dynamically partitioned and allocated to standard infrastructure services as disk cache. We first investigate the feasibility of providing CaaS with the proof-of-concept elastic cache system (using dedicated remote memory servers) built and validated on the actual system, and practical benefits of CaaS for both users and providers (i.e., performance and profit, respectively) are thoroughly studied with a novel pricing scheme. Our CaaS model helps to leverage the cloud economy greatly in that 1) the extra user cost for I/O performance gain is minimal if ever exists, and 2) the provider's profit increases due to improvements in server consolidation resulting from that performance gain. Through extensive experiments with eight resource allocation strategies, we show that our CaaS model can be a promising cost-efficient solution for both users and providers.