Enhancing Resource Provisioning Across Edge-based Environments

As more computing operations shift from the cloud to edge environments, the need for reliable and efficient resource allocation becomes inevitable. Unlike the cloud, edge computing environments often are equipped with limited computational capabilities which makes the task allocation process time consuming and challenging. When allocating resources, it is imperative to consider multi-criteria based on a number of factors including task requirements and the availability of existing edge-based computational capabilities. To this extent, we consider the resource allocation process across edge environments as an optimization problem that can be solved using multi-criteria decision analysis methods (MCDA). In this paper, we present an extension to our Edgify dynamic resource provisioning model that incorporates the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for enhancing the decision making when provisioning edge-based resources across distributed edge or fog environments. We evaluate our proposed Edgify solution through multiple experiments which demonstrate the effectiveness of our proposed decision-making optimization approach.