A framework for availability, performance and survivability evaluation of disaster tolerant cloud computing systems

Abstract

Cloud Computing Systems (CCSs) allow the utilization of application services for users around the world. An important challenge for CCS providers is to supply a high-quality service even when there are failures, overloads, and disasters. A Service Level Agreement (SLA) is often established between providers and clients to define the availability, performance and security requirements of such services. Fines may be imposed on providers if SLA’s quality parameters are not met. A widely adopted strategy to increase CCS availability and mitigate the effects of disasters corresponds to the utilization of redundant subsystems and the adoption of geographically distributed data centers. Considering this approach, services of affected data centers can be transferred to operational data centers of the same CCS. However, the data center synchronization time increases with the distance, which may affect system performance. Additionally, resources over-provisioning may affect the service profitability, given the high costs of redundant subsystems. Therefore, an assessment that include performance, availability, possibility of disasters and data center allocation is of utmost importance for CCS projects. This work presents a framework for geographically distributed CCS evaluation that estimates metrics related to performance, availability and disaster recovery (man-made or natural disasters). The proposed framework is composed of an evaluation process, a set of models, evaluation tool, and fault injection tool. The evaluation process helps designers to represent CCS systems and obtain the desired metrics. This process adopts a formal hybrid modeling, which contemplates CCS high-level models, stochastic Petri nets (SPN) and reliability block diagrams (RBD) for representing and evaluating CCS subsystems. An evaluation tool is proposed (GeoClouds Modcs) to allow easy representation and evaluation of cloud computing systems. Finally, a fault injection tool for CCSs (Eucabomber 2.0) is presented to estimate availability metrics and validate the proposed models. Several case studies are presented and analyze survivability, performance and availability metrics considering multiple data center allocation scenarios for CCS systems.