Intent-Based Service Graph Selection for Cost-Effective Cloud Deployment

Cloud computing offers a diverse range of virtualized services for users. However, selecting and composing these services to generate optimal service graphs that meet user requirements is complex because it requires an expert with extensive knowledge of the cloud applications and their service composition, to manually generate service graphs. This, however, cannot often guarantee that all the requirements are met. A potentially promising approach to address this complexity is to develop a system that enables cloud users to specify their intent and then to automatically select a service graph that meets all their requirements. In this paper, we address the problem of service graph selection while considering both functional and non-functional requirements derived from user intent. Our objective is to minimize the total deployment cost of the service graph in a data center network and to determine its placement within the distributed data center network. We formulate the problem as an Integer Linear Programming (ILP), taking into account the bandwidth and latency requirements of user intent. To solve this, we propose our Service Graph Selection (SGS) solution, which aims to achieve a near-optimal solution in a computationally efficient manner. Our results demonstrate that the proposed solution achieves a deployment cost that is only 4–6% larger than the lower bound of the optimal deployment cost.