Cloud SLAs compensate customers with credits when average availability drops below certain levels. This is too inflexible because consumers lose non-measurable amounts of performance being only compensated later, in next charging cycles. We propose to schedule virtual machines (VMs), driven by range-based non-linear reductions of utility, different for classes of users and across different ranges of resource allocations: partial utility. This customer-defined metric, allows providers transferring resources between VMs in meaningful and economically efficient ways. We define a comprehensive cost model incorporating partial utility given by clients to a certain level of degradation, when VMs are allocated in overcommitted environments (Public, Private, Community Clouds). CloudSim was extended to support our scheduling model. Several simulation scenarios with synthetic and real workloads are presented, using datacenters with different dimensions regarding the number of servers and computational capacity. We show the partial utility-driven driven scheduling allows more VMs to be allocated. It brings benefits to providers, regarding revenue and resource utilization, allowing for more revenue per resource allocated and scaling well with the size of datacenters when comparing with an utility-oblivious redistribution of resources. Regarding clients, their workloads' execution time is also improved, by incorporating an SLA-based redistribution of their VM's computational power.

Current clouds SLAs include compensation for customers (i.e. resource renters) with credits when average availability drops below a certain point. However, this credit scheme is too inflexible because consumers lose a non measurable quantity of performance and are only compensated later (i.e. in the next charging cycle). We propose to schedule cloud isolation and execution units, i.e. virtual machines (VMs), driven by the partial utility of applying a certain amount of resources (CPU, memory or bandwidth) to a given VM. This partial utility metric, specified by the customer, allows the provider to transfer resources between VMs. This is particularly relevant for private clouds where resources are not so abundant. We have defined a cost model that incorporates the partial utility the client gives to a certain level of depreciation when VMs are allocated in an over commit environment. CloudSim, a state of the art cloud simulator, was extended to support our partial utility-driven scheduling model. Using simulation scenarios with synthetic and real workloads, we show that our proposed scheduling strategy brings benefits to providers (i.e. revenue, resource utilization) and clients (i.e. workloads' execution time) by incorporating a SLA-based depreciation of computational power, allowing for more VMs to be allocated.

Internet of Things (IoT) devices are ubiquitous in various applications, such as smart homes, asset and people tracking, and city management systems. However, their deployment in adverse conditions, including unstable internet connectivity and power sources, present new cybersecurity challenges through new attack vectors. The LoRaWAN protocol, with its open and distributed network architecture, has gained prominence as a leading LPWAN solution, presenting novel security challenges. This paper proposes the implementation of machine learning algorithms, specifically the K-Nearest Neighbours (KNN) algorithm, within an Intrusion Detection System (IDS) for LoRaWAN networks. Through behavioural analysis based on previously observed packet patterns, the system can detect potential intrusions that may disrupt critical tracking services. Initial simulated packet classification attained over 90% accuracy. By integrating the Suricata IDS and extending it through a custom toolset, sophisticated rule sets are incorporated to generate confidence metrics to classify packets as either presenting an abnormal or normal behaviour. The current work uses third-party multi-vendor sensor data obtained in the city of Lisbon for training and validating the models. The results show the efficacy of the proposed technique in evaluating received packets, logging relevant parameters in the database, and accurately identifying intrusions or expected device behaviours. We considered two use cases for evaluating our work: one with a more traditional approach where the devices and network are static, and another where we assume that both the devices and the network are mobile; for example, when we need to report data back from sensors on a rail infrastructure to a mobile LoRaWAN gateway onboard a train.