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Cloud computing is a computational model in which resource providers can offer on-demand services to clients in a transparent way. However, to be able to guarantee quality of service without limiting the number of accepted requests, providers must be able to dynamically manage the available resources so that they can be optimized. This dynamic resource management is not a trivial task, since it involves meeting several challenges related to workload modeling, virtualization, performance modeling, deployment and monitoring of applications on virtualized resources. This paper carries out a performance evaluation of a module for resource management in a cloud environment that includes handling available resources during execution time and ensuring the quality of service defined in the service level agreement. An analysis was conducted of different resource configurations to define which dimension of resource scaling has a real influence on client requests. The results were used to model and implement a simulated cloud system, in which the allocated resource can be changed on-the-fly, with a corresponding change in price. In this way, the proposed module seeks to satisfy both the client by ensuring quality of service, and the provider by ensuring the best use of resources at a fair price.

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Abstract-Nowadays the access to a cloud computing environment is provided on-demand offering transparent services to customers. Although the cloud allows an abstraction of the behavior of the service providers in the infrastructure (involving logical and physical resources), it remains a challenge to fully comply with the Service Level Agreements (SLAs), because, depending on the service demand and system configuration, the providers may not be able to meet the requirements of the customers. There is a need for mechanisms that take account of load balancing algorithms to provide an efficient load distribution with the available resources. However, the studies in the literature do not effectively address the problem of the availability of resources to meet customers' requirements with analysis restricted to a limited set of objectives. This paper proposes algorithms to address the need for optimization when handling computational resources during the execution time. The methods optimize the efficient use of the resources available in the infrastructure aiming to comply with the service level agreements defined between client and provider.

Abstract-Performing functionality testing in serviceoriented architectures is not a trivial task. The difficulty is especially the large number of components that may be present in a SOA such as brokers, providers, service registries, clients, monitoring tools, data storage tools, etc. Thus, in order to facilitate the process of conducting functional testing and capacity planning in service-oriented systems, we present PEESOS. This first version is a functional prototype that offers facilities to assist researchers and industry to test their new applications, allowing collaborations that can be done between the participants to achieve an appropriate objective when developing a new application. The first results show that it is possible to make a planning environment easier to operate and to readily obtain results for performance evaluation of a target architecture. Since this is a first version of the prototype, it has interface and scalability limitations as well as needing improvements in performance of the logs repository and also in a core engine. We hope that such limitations can be corrected in the near future, including gathering information from the scientific community to make the prototype a useful and accessible tool. PEESOS is on-line and available at http://peesos.wsarch.lasdpc.icmc.usp.br.