An Efficient Fault Tolerant Cloud Market Mechanism for Profit Maximization

In support of effectively discovering the market value of resources and dynamic resource provisioning, auction design has recently been studied in the cloud. However, there are limitations due to the inability to accept time-varying user demands or offline settings. These limitations create a large gap between the real needs of users and the services available from cloud providers. In addition, existing auction mechanisms do not consider service interruption due to server failures caused by software or hardware problems. To address the limitations of existing auction mechanisms and to avoid service interruption, this paper targets a more general scenario of online cloud resource auction design where: 1) users can request multiple types of time-varying resources; and 2) at least one server is available for each accepted bid even when one or more servers fail; and 3) profit is maximized over the system execution span. Specifically, we model the profit maximization problem using an Integral Linear Programming (ILP) optimization framework, which offers an elastic model for time-varying user demands. In addition, we design an online, truthful, and time efficient auction mechanism consisting of a price-based allocation strategy and a pricing function. The online allocation strategy allocates multiple types of resource to each user while satisfying the time-varying demands and ensuring at least one server is available for each user in each allocated time slot. Lastly, the efficacy of online auctions is validated through careful theoretical analysis and trace-driven simulation studies.