GeoClouds Modcs: A perfomability evaluation tool for disaster tolerant IaaS clouds

Abstract

Performance and availability are key aspects to evaluate the quality of cloud computing systems. The assessment of these systems should consider the effects of queuing and failure/recovery behavior of data center subsystems and disaster occurrences. Additionally, penalties may be applied if the defined quality level of SLA contracts is not satisfied. Thus, IaaS providers need to evaluate the performability level of its environment, considering, also, the possibility of disasters. A possible approach to protect cloud systems from natural disasters corresponds to the utilization of redundant data centers located far enough apart. However, the time to back up the VM data increases with the distance. To accomplish these issues, we propose a user-friendly tool, namely GeoClouds Modcs, for evaluating distributed cloud computing systems deployed into multiple data centers considering disaster occurrence. The proposed environment adopts a hybrid heterogeneous modeling approach, which includes Reliability Block Diagrams (RBD), Stochastic Petri Nets (SPN) and Cloud System High-Level models to perform the system evaluation. For specialized users, the tool also provides specific features that enable edit and evaluate the result SPN and RBD models on external evaluation tools (i.e., Mercury and TimeNET). To illustrate the proposed tool's usability, we present a case study that evaluates a cloud computing distributed in different cities considering diverse user loads.