The management and operational cost of public street lighting systems have driven the development of control architectures based on Internet of Things (IoT) technologies. These systems incorporate remote and autonomous control capabilities, enabling more efficient and scalable lighting operation. Despite recent advances, formal methods for evaluating the performance and energy behavior of smart street lighting systems are often overlooked during the design phase. This paper proposes a modeling approach based on Stochastic Petri Nets (SPN) to analyze the performance and energy consumption of adaptive street lighting systems. The model captures system behavior through operational states, message exchanges, and transmission failures. Experimental data obtained from LoRa-based microcontrollers are used to parameterize and validate the model under different configurations. The response time of the system is estimated using stationary analysis, and a sensitivity analysis is conducted to quantify the influence of key operational parameters such as message size and transmission interval. Results indicate that the transmission interval is the most influential factor affecting system responsiveness, while message size has a limited impact. The proposed model provides a formal and reproducible method for analyzing smart lighting infrastructure prior to deployment.

Deduplication and compression are powerful techniques to reduce the ratio between the quantity of logical data stored and the physical amount of consumed storage. Deduplication can impose significant performance overheads, as duplicate detection for large systems induces random accesses to the backend storage. These random accesses have led to the concern that deduplication for primary storage and HDDs are not compatible. Most inline data reduction solutions are therefore optimized for SSDs and discourage their use for HDDs, even for sequential workloads. In this work, we show that these concerns are valid if and only if the lessons learned from deduplication research are not applied. We have therefore investigated data reduction solutions for primary storage based on the RedHat Virtual Disk Optimizer (VDO) and show that directly applying them can decrease sequential write performance for HDDs by 36×. We then show that slight modifications to VDO plus the integration of a very small SSD area significantly improve performance even beyond the performance without data reduction enabled, making HDDs more cost-efficient for a wide range of mostly sequential cloud workloads than SSDs. Additionally, these VDO optimizations do not require to maintain different code bases for HDDs and SSDs, and we therefore provide the first data reduction solution applicable to both storage media.

The ossification of Internet's architecture is widely recognized as an important problem and network virtualization is a remarkable approach to deal with it. In this context, the optimal allocation of substrate resources is a NP-hard problem and there are many works proposing efficient mapping of virtual networks (VN). Evaluation and analysis of throughput on VNs are also important, as it directly impacts the quality of service of virtualized networks. This paper proposes a technique based on stochastic Petri nets (SPN) and reliability block diagrams (RBD) to assess availability-oriented throughput in virtualized network environments, using the availability of each physical network component to estimate VN throughput. Experimental results demonstrate throughput degradation when the distance of physical nodes increases. This work also presents a fault tolerance technique that mitigates such a degradation.

The need for performance has motivated the increasing adoption of solid-state drives (SSD) both in personal computers and data centers. However, the high cost per gigabyte and reduced lifetime hinder the thorough replacement of hard disk drives (HDD) with SSDs. To mitigate these issues, several architectures have been conceived based on hybrid storage systems, but performance and dependability models have not been proposed to better assess such different architectures. This paper presents an approach based on stochastic models for performance and availability modeling of hybrid storage systems, using stochastic Petri nets (SPN) and reliability block diagrams (RBD). A case study based on OpenStack Swift platform is adopted to demonstrate the feasibility of the proposed approach.