Autonomy Requirements Engineering for Self-adaptive Science Clouds

Self-adaptive clouds extend upstream the regular cloud platforms with special autonomy features dedicated to handling increasing workload and service failures. The identification of such features is not necessarily an easy task. Sometimes those can be explicitly stated by QoS requirements or in preliminary material available to requirements engineers. Often though, they are implicit so that autonomy features capturing has to be undertaken. This paper elaborates on a methodology of capturing autonomy requirements for self-adaptive clouds with ARE, the Autonomy Requirements Engineering approach. In this approach, autonomy features are detected as special self-* objectives backed up by different capabilities and quality characteristics.