Towards Knowledge-Based Assisted IaaS Selection

Current PaaS platforms enable single or hybrid cloud deployments. However, such deployment types cannot best cover the user application requirements as they do not consider the great variety of services offered by different cloud providers and the effects of vendor lock-in. On the other hand, multi-cloud deployment enables selecting the best possible service among equivalent ones providing the best trade-off between performance and cost. In addition, it avoids cases of service level deterioration due to service underperformance as main effects of vendor lock-in. While many multi-cloud application deployment research prototypes have been proposed, such prototypes do not examine the effect that deployment decisions have on application performance. As such, they blindly attempt to satisfy low-level hardware requirements by neglecting the impact of allocation decisions on higher-level requirements at the component or application level. To this end, this paper proposes a new IaaS selection algorithm which, apart from being able to satisfy both low and high level requirements of different types, it also exploits deployment knowledge offered via reasoning over previous application execution histories to take the best possible allocation decisions. The experimental evaluation clearly shows that by considering this extra knowledge, more optimal deployment solutions are derived, able to maintain the service levels requested by users, in less solving time.