Cost aware service selection in a mobile edge marketplace

A marketplace plays an important role that bridges between Mobile Edge Infrastructure Services (EISs) providers and their customers, also manages relations between actors in a mobile edge ecosystem. One of the key services of the marketplace is service selection where not only a list of EISs matching the customers' demands is provided but also enables service selection based on the customers' requirements to fully automate the process. In this paper, we first investigate important attributes of EISs (such as coverage area, latency models, pricing models, etc.), and requirements of edge-based applications (such as latency and reliability) for EIS selection in a marketplace. We then formulate an optimization problem to choose the right set of EISs among available services in the marketplace. We propose two service selection algorithms, i.e., Best Fit (BF) and an Improved version of BF (IBF) to minimize the monetary cost of selected services subject to latency, reliability constraints and customer requirements. The evaluation shows that IBF has 4% improvement in monetary cost as compared to the BF. IBF has only 1% deviation from the optimal solution generated by a brute force algorithm, while it is 189 times faster than the brute force. Accordingly, IBF not only outperforms BF in terms of monetary costs but also achieves the optimal solution as compared to the brute force algorithm in significantly lower execution time. Furthermore, IBM CPLEX Optimizer is also implemented to solve the considered problem to have more concrete evaluation. The results indicate that although CPLEX can also solve the problem with the optimal result, its computing time is still dramatically worse than IBF.