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The advent of cloud computing has offered to developers a new appealing paradigm to deploy their applications without capital investments. Resources can now be acquired on-demand in a flexible, scalable and rapid way. However, cloud providers lack of native mechanisms to guarantee the Quality of Service required by specific application domains. High availability can be achieved by replication of critical components. Since outages could affect the entire cloud provider, replication can be effective only by using multiple providers. In this paper we tackle the above problem and present an approach to guarantee availability requirements of cloud-based applications by exploiting replication on multiple clouds to reduce unavailability, still limiting costs. More precisely, we propose: i) an approach to model, at design time, the application, its availability requirements and the characteristics of the used clouds, and ii) a self-adaptive technique responsible, at runtime, of both in-cloud scaling policies and traffic routing among different cloud providers, by means of a control-theoretical approach. We integrated the modeling approach in the Palladio Bench IDE and developed a runtime self-adaptation controller in Matlab. The controller has been evaluated against different workload conditions, costs variations and service failures in simulated scenarios. The controller has been able to provide the desired availability minimizing costs.

The large success of the Cloud computing, its strong impact on the ICT world and on everyday life testifies the maturity and effectiveness this paradigm achieved in the last few years. Presently, the Cloud market offers a multitude of heterogeneous solutions; however, despite the undeniable advantages, Cloud computing introduced new issues and challenges. In particular, the heterogeneity of the available Cloud services and their pricing models makes the identification of a configuration that minimizes the operating costs of a Cloud application, guaranteeing at the same time the Quality of Service (QoS), a challenging task. This situation requires new processes and models to design software architectures (SAs) and predict costs and performance considering together the large variability in price models and the intrinsic dynamism and multi-tenancy of the Cloud environments. This work aims at providing a novel mathematical approach to this problem presenting a queueing theory based Mixed Integer Linear Program (MILP) to find a promising multi-cloud configuration for a given software architecture. The effectiveness of the proposed model has been favorably evaluated against first principle heuristics currently adopted by practitioners. Furthermore, the configuration returned by the model has been also used as initial solution for a local-search based optimization engine, which exploits more accurate but time-consuming performance models. This combined approach has been shown to improve the quality of the returned solutions by a 37% on average and reducing the overall search time by 50% with respect to state of the art heuristics based on tiers utilization thresholds.

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Cloud Computing has assumed a relevant role in the ICT, profoundly influencing the life-cycle of modern applications in the manner they are designed, developed, and operated. The Cloud market offers highly diversified services upon which developers and operators can rely. Yet, its full adoption requires specific and rare expertise. Actually, such services are characterized by a steep learning curve and, among the other aspects, by significantly different Quality of Service (QoS). In this paper, we tackle the problem of supporting the design-time performance analysis of Cloud applications and the identification of the optimal strategy for allocating components onto Cloud resources. In particular, the final goal is to set the basis to overcome the limitations of current design-alternatives search tools proposing (i) a mathematical formulation for the underlying optimization problem, i.e., determine the Cloud configuration that minimizes the execution costs of the application over a daily time horizon, fulfilling at once QoS and service allocation constraints, and (ii) a software solution able to efficiently solve the resource allocation problem. The tool, codenamed SPACE4Cloud, embodies a hybrid two-level search meta-heuristic for the efficient exploration of the design-alternatives space. The benefits of this approach are demonstrated in the context of an industrial case study. Furthermore, this work also presents results where SPACE4Cloud leads to a cost reduction up to 60%, when compared to a first-principle technique based on utilization thresholds, like the ones typically used in practice. Finally, an extensive scalability analysis indicates that our solution is able to solve large problem instances within a time frame compatible with a fast-paced design process (less than half an hour in the worst case).