Keep Hot or Go Cold: A Randomized Online Migration Algorithm for Cost Optimization in STaaS Clouds

Abstract

Storage-as-a-Service clouds generally offer both hot and cold storage tiers with different pricing options. Hot tiers provide a higher storage price but a lower access price, and vice versa for cold tiers. Many studies show that those user-generated data generally receive relatively high access frequency in the early period of their lifetimes while the overall trend of accesses is downward. Thus, when such kinds of data are hosted in clouds, they can be stored in hot tiers initially and then migrated to cold tiers for optimizing costs. However, the cost of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">migration</i> is non-negligible, and the number of accesses may then unexpectedly increase after migration, which indicates that a rash migration will incur more costs instead of cost-savings. For making optimal migration decisions, future data access curves are needed, but it is generally very hard to predict them precisely. To solve this problem, in this paper we propose a randomized online algorithm to optimize costs for those user-generated data stored in clouds, without requiring any future information. We show theoretically that the proposed algorithm can achieve a guaranteed competitive ratio of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1 + \frac {2(1-\lambda)}{e - 3 + 2\lambda + \lambda /\alpha }$ </tex-math></inline-formula> , and it can be easily extended with prediction windows when short-term predictions are reliable. Eventually, we validate the effectiveness of our proposed algorithms through simulations driven by real-world video-visiting traces collected from a well-known video-sharing website.