In a cloud computing environment, companies have the ability to allocate resources according to demand. However, there is a delay that may take minutes between the request for a new resource and it is ready for using. This causes the reactive techniques, which request a new resource only when the system reaches a certain load threshold, are not suitable for the resource allocation process. To address this problem, it is necessary to predict requests that arrive at the system in the next period of time to allocate the necessary resources, before the system becomes overloaded. There are several time-series forecasting models to calculate the workload predictions based on history of monitoring data. However, it is difficult to know which is the best time series forecasting model to be used in each case. The work becomes even more complicated when the user does not have much historical data to be analyzed. Most related work, considers only single methods to evaluate the results of the forecast. Other work propose an approach that selects suitable forecasting methods for a given context. But in this case, it is necessary to have a significant amount of data to train the classifier. Moreover, the best solution may not be a specific model, but rather a combination of models. In this paper we propose an adaptive prediction method using genetic algorithms to combine time-series forecasting models. Our method does not require a previous phase of training, because it constantly adapts the extent to which the data is coming. To evaluate our proposal we use three logs extracted from real web servers. The results show that our proposal often brings the best result, and is generic enough to adapt to various types of time series.

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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.

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