IoT special feature

Modelling IoT-fog-cloud systems with DISSECT-CF-Fog

Andras Markus and Attila Kertesz, University of Szeged

The number of interconnected internet of things (IoT) devices has been growing exponentially, due to technological advances and expanded user demand; in fact, Cisco has reported that the number of mobile devices across the world will reach 13.1 billion by 2023. The need to process and store the amount of data generated by these devices represents a significant burden on traditional clouds.

Fog computing complements cloud technology by bringing services closer to the user for latency- and / or time-sensitive IoT applications. However, operating and maintaining real IoT-fogcloud infrastructures are very costly and time-consuming tasks. Hence simulation tools – which allow systems to be tested in the absence of a real environment – have become popular in the research community and in industry. These tools provide a costeffective way to test concepts, try out new procedures, modify existing ones, and then change the real environment based on the conclusions drawn from the results measured.

The DISSECT-CF-Fog simulator is an open-source, event-driven simulator, which has two main parts: infrastructure and IoT modelling. For the physical layer of the infrastructure, detailed infrastructure-as-a-service (IaaS) simulation is offered, including physical and virtual machines, storage and datacentre network properties. Furthermore, both horizontal and vertical connections are represented among fog and cloud nodes. In the virtual layer, applications utilizing computing nodes are responsible for processing data.

DISSECT-CF-Fog is also capable of modelling smart devices, sensors and actuators. To be as realistic as possible, the mobility of smart devices is also considered. Since a node is typically responsible for serving IoT devices in its environment, and the coverage of computing nodes is limited, the movement of mobile devices may cause increased latency and unpredictable response time, which can degrade the quality of service. DISSECT-CF-Fog also offers a solution for large-scale experiments, especially if the number of active entities exceeds tens of thousands. With this simulator we focus on the following questions and topics:

• Connectivity: When many IoT devices are present in a certain area (due to movement, for example), the increased load can easily cause bottleneck effects, therefore it is important which

IoT device connects to which computing node. With different selection algorithms and handovers, the overlapping of computing nodes can be leveraged. • Offloading: The data to be processed can be moved depending on the load on the given compute node. By using different trade-off algorithms, the system can be optimized for energy load, utilization cost or resource usage. • Billing: Utilization cost is not only important from the provider's perspective, but also from that of the end user. DISSECT-CF-Fog takes into account both IoT and cloud-side costs and can measure energy consumption as well. • Resource management: The proper allocation of IoT services to computational resources is particularly important for device mobility. Metrics such as latency, storage and free computing power can change continuously according to the actual load of the moving devices. Since a device can move to a position that is not covered by any node, the data can be processed locally on that device. Several proactive and reactive service migration strategies can be applied in the simulator.

FURTHER INFORMATION: DISSECT-CF-Fog on GitHub github.com/sed-inf-u-szeged/DISSECT-CF-Fog Paper: ‘Actuator behaviour modelling in IoT-Fog-Cloud simulation’ peerj.com/articles/cs-651/