Sourcing Strategies for Virtualized Services: An Intuitionistic Fuzzy Risk-based Approach

An example for sourcing of virtualized services is “cloud bursting” as a deployment model in which applications run in a private cloud or data center and ‘burst’ into a non-private cloud when more computing capacity is needed [1],[2]. The availability of free burst capacity is not guaranteed, so ‘burst’ has a cheaper cost but comes with a higher risk that the Service Level Agreement (SLA) e.g. in terms of application response time are not met for peak-traffic events. To balance risk versus costs the impact of service levels (e.g. committed vs. burst capacity) of technical services on customers’ business processes need to be understood. But there are rarely accepted engineering methods available to recommend on costvs.-risk efficient service levels. In a SLA-aware service composition problem for minimizing cost without compromising the service quality, there is a great research interest in integrated management tools that automatically control the quality of multi-tier sourcing applications and autonomously warn for arising problem on frontend impacts to end-users or other business process implications. The proposed concept is providing a bridge between business impacts to distributed infrastructure systems to recommend for capacity and quality of technical components by defining fuzzy dependency couplings in a practical and feasible manner in order to satisfy aspects of the distributed nature of SLA dependencies.