Software Rejuvenation in Cloud Systems

Abstract

Cloud computing is a promising paradigm able to rationalize the use of hardware resources by means of virtualization. Virtualization allows to instantiate one or more virtual machines (VMs) on top of a single physical machine managed by a virtual machine monitor (VMM). Similarly to any other software, a VMM experiences aging and failures. Software rejuvenation is a proactive fault management technique that involves terminating an application, cleaning up the system internal state, and restarting it to prevent the occurrence of future failures. Adopting Software rejuvenation techniques in Cloud computing is a new research frontier with challenging problems related to the complexity and the distributed nature of Cloud itself.In this talk, we investigate how Cloud systems may benefit from the adoption of rejuvenation principles and propose a technique to model and evaluate the VMM aging process and to investigate the optimal rejuvenation policy that maximizes the VMM availability under variable workload conditions. Starting from dynamic reliability theory and adopting symbolic algebraic techniques, we investigate and compare existing time-based VMM rejuvenation policies. We also propose a time-based policy that adapts the rejuvenation timer to the VMM workload condition improving the system availability. The effectiveness of the proposed modeling technique is demonstrated through a numerical example based on a case study taken from the literature.The talk also addresses the problem of data gathering related to resources usage that may indicate critical working conditions, requiring the rejuvenation of software components. A 3D model for analyzing cloud monitoring information is proposed to retrieve meaningful information and support the coordination of management actions between cloud infrastructure and application. This monitoring solution is applied to a testbed using Openstack and the WordPress application.Finally, rejuvenation is combined with green Cloud concepts, with the goal to minimize the effects of system downtime, while reducing its impact in terms of energy consumption and environmental pollution.