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Infrastructure-as-a-Service (IaaS) clouds offer diverse instance purchasingoptions. A user can either run instances on demand and pay only for what ituses, or it can prepay to reserve instances for a long period, during which ausage discount is entitled. An important problem facing a user is how these twoinstance options can be dynamically combined to serve time-varying demands atminimum cost. Existing strategies in the literature, however, require eitherexact knowledge or the distribution of demands in the long-term future, whichsignificantly limits their use in practice. Unlike existing works, we proposetwo practical online algorithms, one deterministic and another randomized, thatdynamically combine the two instance options online without any knowledge ofthe future. We show that the proposed deterministic (resp., randomized)algorithm incurs no more than 2-alpha (resp., e/(e-1+alpha)) times the minimumcost obtained by an optimal offline algorithm that knows the exact future apriori, where alpha is the entitled discount after reservation. Our onlinealgorithms achieve the best possible competitive ratios in both thedeterministic and randomized cases, and can be easily extended to cases whenshort-term predictions are reliable. Simulations driven by a large volume ofreal-world traces show that significant cost savings can be achieved withprevalent IaaS prices.

To Reserve or Not to Reserve: Optimal Online Multi-Instance Acquisition in IaaS Clouds