Notes on Cloud computing principles

This letter provides a review of fundamental distributed systems and economic Cloud computing principles. These principles are frequently deployed in their respective fields, but their inter-dependencies are often neglected. Given that Cloud Computing first and foremost is a new business model, a new model to sell computational resources, the understanding of these concepts is facilitated by treating them in unison. Here, we review some of the most important concepts and how they relate to each other

QoS-aware predictive workflow scheduling

This research places the basis of QoS-aware predictive workflow scheduling. This research novel contributions will open up prospects for future research in handling complex big workflow applications with high uncertainty and dynamism. The results from the proposed workflow scheduling algorithm shows significant improvement in terms of the performance and reliability of the workflow applications

DEPAS: A Decentralized Probabilistic Algorithm for Auto-Scaling

The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called auto-scaling and its main purpose is to automatically adjust the scale of the system that is running the application to satisfy the varying workload with minimum resource utilization. The need for auto-scaling is particularly important during workload peaks, in which applications may need to scale up to extremely large-scale systems. Both the research community and the main cloud providers have already developed auto-scaling solutions. However, most research solutions are centralized and not suitable for managing large-scale systems, moreover cloud providers' solutions are bound to the limitations of a specific provider in terms of resource prices, availability, reliability, and connectivity. In this paper we propose DEPAS, a decentralized probabilistic auto-scaling algorithm integrated into a P2P architecture that is cloud provider independent, thus allowing the auto-scaling of services over multiple cloud infrastructures at the same time. Our simulations, which are based on real service traces, show that our approach is capable of: (i) keeping the overall utilization of all the instantiated cloud resources in a target range, (ii) maintaining service response times close to the ones obtained using optimal centralized auto-scaling approaches.Comment: Submitted to Springer Computin

Flood lamination strategy based on a three-flood-diversion-area system management

The flood lamination has for principal objective to maintain a downstream flow at a fixed lamination level. For this goal, it is necessary to proceed to the dimensioning of the river system capacity and to make sure of its management by taking into account socio-economic and environmental constraints. The use of flood diversion areas on a river has for main interest to protect inhabited downstream areas. In this paper, a flood lamination strategy aiming at deforming the wave of flood at the entrance of the zone to be protected is presented. A transportation network modeling and a flow optimization method are proposed. The flow optimization method, is based on the modeling of a Min-Cost-Max-flow problem with a linear programming formulation. The optimization algorithm used in this method is the interior-point algorithm which allows a relaxation of the solution of the problem and avoids some non feasibility cases due to the use of constraints based on real data. For a forecast horizon corresponding to the flood episode, the management method of the flood volumes is evaluated on a 2D simulator of a river equipped with a three-flood-diversion- area system. Performances show the effectiveness of the method and its ability to manage flood lamination with efficient water storage