Cloud workflow scheduling with on-demand and spot block instances
Long Chen, Xiaoping Li, Rubén Ruíz · 2017
Cloud computing enables users to access different resources conveniently based on the `pay-as-you-go' model. However, the unit cost of these on-demand instances are usually high. The spot instances provide a dynamic and cheaper manner for renting resources from the cloud. However, failures are often occurred due to the fluctuations of the price of the spot instance. It is a big challenge to determine the appropriate amounts of spot and on-demand resources in terms of users' requirements. In this paper, the workflow scheduling problem with both spot and on-demand instances is considered. The objective is to minimize the total renting cost under deadline constrains. An idle time block-based method is proposed to construct schedules for workflow applications. Schedules are improved by a forward and backward moving mechanism. Experimental and statistical results demonstrate the effectiveness of the proposed algorithm over a lot of tests with different sizes.