Issue No. 08 - Aug. (2012 vol. 23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2011.297
Hyuck Han , Seoul National University, Seoul
Young Choon Lee , University of Sydney, Sydney
Woong Shin , Seoul National University, Seoul
Hyungsoo Jung , University of Sydney, Sydney
Heon Y. Yeom , Seoul National University, Seoul
Albert Y. Zomaya , University of Sydney, Sydney
Over the past decades, 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. In addition to technical challenges, providing cache services in clouds encounters a major practical issue (quality of service or service level agreement issue) of pricing. Currently, (public) cloud users are limited to a small set of uniform and coarse-grained service offerings, such as High-Memory and High-CPU in Amazon EC2. In this paper, 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 demonstrate that our CaaS model can be a promising cost-efficient solution for both users and providers.
Cloud computing, cache as a service, remote memory, cost efficiency.
W. Shin, Y. C. Lee, A. Y. Zomaya, H. Jung, H. Y. Yeom and H. Han, "Cashing in on the Cache in the Cloud," in IEEE Transactions on Parallel & Distributed Systems, vol. 23, no. , pp. 1387-1399, 2011.