Cloud computing is a model in which resources such as storage, applications, and networking infrastructures can be offered as services over the internet. Cloud applications are becoming even bigger and more complex with a high availability requirement. This paper presents a modeling strategy based on a hierarchical and heterogeneous modeling for cloud infrastructure planning. This modeling strategy allows the selection of cloud infrastructures according to dependability and cost requirements. Additionally, a stochastic model generator for cloud infrastructure planning provides automatic generation of dependability and cost models for representing cloud infrastructures. A case study based on Moodle hosted on a Eucalyptus platform is adopted to demonstrate the feasibility of the proposed solution (modeling strategy and tooling).

Hundreds of natural disasters occur in many parts of the world every year, causing billions of dollars in damages. This fact contrasts with the high availability requirement of cloud computing systems, and, to protect such systems from unforeseen catastrophe, a recovery plan requires the utilization of different data centers located far enough apart. However, the time to migrate a VM from a data center to another increases due to distance. This work presents dependability models for evaluating distributed cloud computing systems deployed into multiple data centers considering disaster occurrence. Additionally, we present a case study which evaluates several scenarios with different VM migration times and distances between data centers.

The main objective of this paper is to propose a solution for modeling, analysis and verification of embedded real-time systems with energy constraints. For that, we combine functionalities of the SysML models and annotation from MARTE with the advantages of using time Petri net. This formalism allows analysis and verification of functional, timing and energy requirements in early phases of the development lifecycle. In order to depict the practically usability of the proposed method, a real-world case study is presented, namely, pulse-oximeter. Experimental results have demonstrated an accuracy of 96% using the proposed formal method in comparison with the values obtained with the hardware platform.