text: Abstract Maximizing resource utilization by performing an efficient resource provisioning is a key factor for any cloud provider: commercial actors can maximize their revenues, whereas scientific and non-commercial providers can maximize their infrastructure utilization. Traditionally, batch systems have allowed data centers to fill their resources as much as possible by using backfilling and similar techniques. However, in an IaaS cloud, where virtual machines are supposed to live indefinitely, or at least as long as the user is able to pay for them, these policies are not easily implementable. In this work we present a new scheduling algorithm for IaaS providers that is able to support preemptible instances, that can be stopped by higher priority requests without introducing large modifications in the current cloud schedulers. This scheduler enables the implementation of new cloud usage and payment models that allow more efficient usage of the resources and potential new revenue sources for commercial providers. We also study the correctness and the performance overhead of the proposed scheduler against existing solutions.
  @cite_31: An important feature of most cloud computing solutions is auto-scaling, an operation that enables dynamic changes on resource capacity. Auto-scaling algorithms generally take into account aspects such as system load and response time to determine when and by how much a resource pool capacity should be extended or shrunk. In this article, we propose a scheduling algorithm and auto-scaling triggering strategies that explore user patience, a metric that estimates the perception end-users have from the Quality of Service (QoS) delivered by a service provider based on the ratio between expected and actual response times for each request. The proposed strategies help reduce costs with resource allocation while maintaining perceived QoS at adequate levels. Results show reductions on resource-hour consumption by up to approximately 9 compared to traditional approaches. Mechanisms for resource auto-scaling in clouds considering users' patience.Methods for determining the step size of scaling operations under bound and unbounded maximum capacity.Users patience model inspired in prospect theory.
  @cite_10: Resource management in a cloud environment is a hard problem, due to: the scale of modern data centers; the heterogeneity of resource types and their interdependencies; the variability and unpredictability of the load; as well as the range of objectives of the different actors in a cloud ecosystem. Consequently, both academia and industry began significant research efforts in this area. In this paper, we survey the recent literature, covering 250+ publications, and highlighting key results. We outline a conceptual framework for cloud resource management and use it to structure the state-of-the-art review. Based on our analysis, we identify five challenges for future investigation. These relate to: providing predictable performance for cloud-hosted applications; achieving global manageability for cloud systems; engineering scalable resource management systems; understanding economic behavior and cloud pricing; and developing solutions for the mobile cloud paradigm .
summary: The resource provisioning from cloud computing infrastructures using Spot Instances or similar mechanisms has been addressed profusely in the scientific literature in the last years @cite_10 . However, the vast majority of this work has been done from the users' perspective when using and consuming Spot Instances @cite_31 and few works tackle the problem from the resource provider standpoint.