An enhanced biomorphic model for cloud scaling

Multiple bio-inspired approaches have been developed aiming at achieving adaptive and autonomous behavior in the cloud. However, no such effort was invested in studying biomorphic-inspired methods. In this paper, biomorphism is presented as a central evolutionary processes where cellular differentiation take place. To the best of our knowledge, the only state-of-the-art biomorphic model applied to cloud computing was reported in our previous work [28]. The biomorphic model devised in [28] and called timing algorithm was simplistic and aimed to illustrate the concept of biomorphism for cloud scaling scenarios. In this respect, one of its major disadvantage is the timer randomness which could lead to long reaction time, possibly spawning additional unnecessary VMs. In this paper, we present an enhanced biomorphic algorithm which builds on the concept of fixed timer intervals and random seeds so that to coordinate the transformation procedure. The advantage of using a different method allowed us to achieve instant reactions and a more predictive nature of the system due to the fixed time spawning intervals.