One of the biggest advantages of cloud infrastructures is the elasticity. Cloud services are monitored and based on the resource utilization and performance load, they get scaled up or down, by provision or deprovision of cloud resources. The goal is to guarantee the customers an acceptable performance with a minimum of resources. Such Quality of Service (QoS) characteristics are stated in a contract, called Service Level Agreement (SLA) negotiated between customer and provider. The approach of this paper shows that with additional imprecise information (e.g. expected daytime/weektime performance) modeled with fuzzy logic and used in a behavior, load and performance prediction model, the up and down scaling mechanism of a cloud service can be optimized. Evaluation results confirm, that using this approach, SLA violation can be minimized.

Reducing IT costs by using Cloud Computing is tempting for start up companies. To attract companies to outsource their services to Clouds, Cloud provider need to offer Service Level Objectives specified in SLAs individually for their customers. Cloud provider like Amazon can not afford to negotiate individual SLAs manually. Therefore, it becomes important to develop a format for machine-readable SLAs which can easily adapt to the individual Service Level Objectives requested by the customer any time. Because of its adaptability at run time by each individual customer on demand, this comply with the characteristics of Cloud Computing and to satisfies the customer's requirements to be flexible. This paper describes an adaptable Service Level Objective Agreement (A-SLO-A) format being machine-processed to offer the possibility to integrate the SLA management into the highly automated processes of resource provisioning. Use cases show its applicability.