Abstract: Urgent computing capabilities for early warning systems and decision support systems are vital in situations that require execution be completed before a specified deadline. The cost of missing the deadline in such situations can be unacceptable, while providing insufficient results can mean an ineffective solution that may come at a very high cost. In order to provide a solution that is appropriate under the current conditions (i.e. available volume of computational resources, workload, and time available), a new approach is required. In this paper, we present a schema and algorithm of regulating the volume of computations within an urgent workflow to deliver a solution that is as sufficient as possible given the current conditions and deadline. To achieve these goals, we develop an approach that modifies an urgent workflow by changing its structure and the parameters of its individual tasks. Such modifications are based on introducing a notion of quality and applying quality-based models to estimate the sufficiency of solutions generated by the resulting workflow structures. Finally, a special extension of the genetic algorithm that performs quality-based scheduling of urgent workflows is described along with an experimental study to demonstrate its efficacy.

Abstract: Information spreading simulation is an important problem in scientific community and is widely studied nowadays using different techniques. Efficient users’ activity simulation for urgent scenarios is even more important, because fast and accurate reaction in such situations can save human lives. In this paper we present multi-layer agent-based network model for information spreading simulation in urgent scenarios, which allows to investigate agents’ behavior in a variety of situations. This model can be used for live city simulation in integration with other agent-based human interaction models. Experimental results demonstrate logical consistency of the proposed approach and show different cases of information spreading in the network with different social aspect.

Abstract: Cloud computational platforms today are very promising for execution of scientific applications since they provide ready to go infrastructure for almost any task. However, complex tasks, which contain a large number of interconnected applications, which are usually called workflows, require efficient tasks scheduling in order to satisfy user defined QoS, like cost or execution time (makespan). When QoS has some restrictions – limited cost or deadline – scheduling becomes even more complicated. In this paper we propose heuristic algorithm for scheduling workflows in hard-deadline constrained clouds – Levelwise Deadline Distributed Linewise Scheduling (LDD-LS) – which, in combination with implementation of IC-PCP algorithm, is used for initialization of proposed metaheuristic algorithm – Cloud Deadline Coevolutional Genetic Algorithm (CDCGA). Experiments show high efficiency of CDCGA, which makes it potentially applicable for scheduling in cloud environments.

Abstract: Public healthcare can be cast as a complex systems and network analysis is one of the methodological approaches that are commonly used to study these types of systems. In this paper we describe a multi-scale and multi-level interpretation of complex networks in public healthcare. Our contribution is to provide a toolbox for visualization and visual data-driven analysis of complex multiscale temporal contact networks that allows to simulate various dynamic processes using user-defined models. An example of explorative analysis of a dataset from real clinical data obtained from the Federal Almazov North-West Medical Research Centre in Saint Petersburg is described.