QuARAM Service Recommender: A Platform for IaaS Service Selection

Abstract

Cloud computing provides on-demand resources with no constraints of physical locations. It allows customers to save upfront infrastructure costs and focus on features that discriminate their core businesses. The increasing number of offered services makes manual selection of the most suitable service for an application deployment time-consuming and challenging. It also requires a high level of user expertise to make proper decisions. In this paper, we present QuARAM Service Recommender platform, a self-adaptive Infrastructure-as-a-Service (IaaS) service selection system that recommends a list of suitable services for cloud application deployment based on application requirements and customer preferences. The process begins with automatic extraction of the application's features, requirements and customer preferences and provides a list of potential services for the application deployment (i.e., resource allocation in our context). Initial experiments show promising results for up to 90% precision of recommended services.