The paper presents a new classification schema of IaaS cloud workloads types, based on the functional characteristics. We show the results of an experiment of automatic categorization performed with different benchmarks that represent particular workload types. Monitoring of resource utilization allowed us to construct workload models that can be processed with machine learning algorithms. The direct connection between the functional...

The paper presents an approach to automatic discovery of workloads types. We perform functional characteristics of the workloads executed in our cloud environment, that have been used to create model of the computations. To categorize the resources utilization we used K-means algorithm, that allow us automatically select six types of computations. We perform analysis of the discovered types against to typical computational benchmarks,...

A genetic programming paradigm is implemented for reliability optimization in the Comcute grid system design. Chromosomes are generated as the program functions and then genetic operators are applied for finding Pareto-suboptimal task assignment and scheduling. Results are compared with outcomes obtained by an adaptive evolutionary algorithm.

The goal of this paper is to present a model that predicts the real workload placed on a volunteer based system by an application, with incorporation of not only performance but also availability of volunteers. The application consists of multiple data packets that need to be processed. Knowing the computational workload demand of a single data packet we show how to estimate the application workload in a volunteer based system. Furthermore,...