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.

Nowadays, the access to a cloud computing environment is provided on-demand, offering transparent services to clients. Although the cloud allows an abstraction of the behavior of the infrastructure in the service providers (involving logical and physical resources), the Service Level Agreements (SLAs) fulfilment remains a challenge, because depending on the service demand and the system configuration, the providers may not be able to meet the clients requirements. In this way, mechanisms that take account of load balancing and resource provisioning algorithms to provide an efficient load distribution in the available resources are necessary. However, the studies in the literature do not effectively address the problem of the resource provisioning to meet clients requirements using optimization techniques, restricting the analysis to a limited set of objectives. This paper proposes algorithms to address the computational resource provisioning problem using optimization techniques on-the-fly. The techniques optimize the use of the resources available in the cloud infrastructure, aiming to fulfill the clients requirements defined in the SLAs, and ensuring the efficient use of resources.

The cloud ecosystem provides transformative advantages that allow elastically offering on-demand services. However, it is not always possible to provide adequate services to all customers and thus to fulfill service level agreements (SLA). To enable compliance with these agreements, service providers leave the customer responsible for determining the service settings and expect that the client knows what to do. Some studies address SLA compliance, but the existing works do not adequately address the problem of resource allocation according to clients' needs since they consider a limited set of objectives to be analyzed and fulfilled. In previous work, we have already addressed the problem considering a single-objective approach. In that work, we identified that the problem has a multi-objective characteristic since several attributes simultaneously influence the SLA agreement, which can lead to conflicts. This paper proposes a multi-objective combinatorial optimization approach for computational resources provisioning, seeking to optimize the efficient use of the infrastructure and provide the client with greater flexibility in contract closure.INDEX TERMS Cloud computing ecosystem, metaheuristics, multi-objective optimized, SLA, QoS.

With the development of mobile communications and the Internet of Things (IoT), IoT devices have increased, allowing their application in numerous areas of Industry 4.0. Applications on IoT devices are time sensitive and require a low response time, making reducing latency in IoT networks an essential task. However, it needs to be emphasized that data production and consumption are interdependent, so when designing the implementation of a fog network, it is crucial to consider criteria other than latency. Defining the strategy to deploy these nodes based on different criteria and sub-criteria is a challenging optimization problem, as the amount of possibilities is immense. This work aims to simulate a hybrid network of sensors related to public transport in the city of São Carlos - SP using Contiki-NG to select the most suitable place to deploy an IoT sensor network. Performance tests were carried out on five analyzed scenarios, and we collected the transmitted data based on criteria corresponding to devices, applications, and network communication on which we applied Multiple Attribute Decision Making (MADM) algorithms to generate a multicriteria decision ranking. The results show that based on the TOPSIS and VIKOR decision-making algorithms, scenario four is the most viable among those analyzed. This approach makes it feasible to optimally select the best option among different possibilities.

Currently the access to a cloud computing environment is provided on demand, which allows providers to offer elasticity services to customers. Although the cloud allows an abstraction of the infrastructure behavior of the service providers (logical and physical), the fulfillment of the Service Level Agreements (SLAs) is challenging, because according with the demand and system configuration, the providers cannot ensure the customers requirements. There is a necessity of mechanisms that consider load balancing algorithms to provide an efficient load distribution in the available resources. However, the papers available in the literature do not efficiently address the problem of resource equation considering the customers requirements, because they consider a limited set of objectives to be analysed and fulfilled. Therefore, this master project aims to develop and evaluate algorithms available in the literature that address the combinatorial optimization and the multi-objective approach to handle the computational resources during the execution time, trying to optimize the efficient use of the resources available in the infrastructure and fulfill the service level agreements defined between the clients and providers.