Cloud computing providers offer multiple service classes to deal with workload heterogeneity. Classes are distinguished by their expected Quality of Service (QoS), which is defined in terms of Service Level Objectives (SLO). A priority-based scheduling policy is commonly used to guarantee that requests submitted to the different service classes achieve the desired QoS. However, the QoS delivered during resource contention periods may be unfair to certain users. In this paper, we present a SLO-driven scheduling policy which takes the SLOs and actual QoS delivered for each request into account when making decisions. We used simulation experiments fed with traces from a production system to compare the SLO-driven policy with a priority-based one. In general, the SLO-driven policy delivered a better service than the priority-based one.

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The increasing popularity of smartphones, associated with their capability to sense the environment, has allowed the creation of an increasing range of data-driven applications. In general, this type of application collects data from the environment using edge devices and sends them to a remote cloud to be processed. In this setting, the governance of the application and its data is, usually, unilaterally defined by the cloud-based application provider. We propose an architectural model which allows this kind of application to be governed solely by the community of users, instead. We consider members of a community who have some common problem to solve, and eliminate the dependence on an external cloud-based application provider by leveraging the capabilities of the devices sitting on the edge of the network. We combine the concepts of Participatory Sensing, Mobile Social Networks and Edge Computing, which allows data processing to be done closer to data sources. We define our model and then present a case study that aims to evaluate the feasibility of our proposal, and how its performance compares to that of other existing solutions (e.g. cloud-based architecture). The case study uses simulation experiments fed with real data from the public transport system of Curitiba city, in Brazil. The results show that the proposed approach is feasible, and can aggregate as much data as current approaches that use remote dedicated servers. Differently from the allor-nothing sharing policy of current approaches, the approach proposed allows users to autonomously configure the trade-off between the sharing of private data, and the performance that the application can achieve.