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In cloud computing, users scale their resources (computational) based ontheir need. There is massive literature dealing with such resource scalingalgorithms. These works ignore a fundamental constrain imposed by all CloudService Providers (CSP), i.e. one has to pay for a fixed minimum durationirrespective of their usage. Such quantization in billing cycles poses problemfor users with sporadic workload. In recent literature, Cloud Broker (CB) hasbeen introduced for the benefit of such users. A CB rents resources from CSPand in turn provides service to users to generate profit. Contract between CBand user is that of pay-what-you-use/pay-per-use. However CB faces thechallenge of Quantized Billing Cycles as it negotiates with CSP. We design twoalgorithms, one fully online and the other partially online, which maximizesthe profit of the CB. The key idea is to regulate users demand using dynamicpricing. Our algorithm is inspired by the Ski-Rental problem. We derivecompetitive ratio of these algorithms and also conduct simulations using realworld traces to prove the efficiency of our algorithm.

Maximizing Profit of Cloud Brokers under Quantized Billing Cycles: a Dynamic Pricing Strategy based on Ski-Rental Problem