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Applications that make use of the Internet of Things (IoT) capture an enormous amount of raw data from sensors and actuators, which is frequently transmitted towards the cloud data centres for processing and analysis. However, due to varying and unpredictable data generation rates and network latency, sending the data towards a cloud data centre can lead to a performance bottleneck. With the emergence of Fog and Edge computing hosted microservices, data processing could be moved towards the network edge. We propose a novel Pareto-based approach that makes use of a multi-criteria bin packing optimisation for the efficient and optimal distributed deployment of microservices -- along the edge, fog/cloudlet and cloud tiers. This optimisation takes account of non-functional requirements, such as operational cost, compute resource utilisation, service availability, response time, latency and similar. The results show that the present approach provides an optimal and sustainable consumption of compute resources and improves Quality of Service of the application during its runtime. The approach can also be integrated into software engineering workbenches for the creation and deployment of cloud-native applications, enabling partitioning of an application across the multiple infrastructure tiers outlined above. |
