Affinity AwareScheduling of Live Migration of Virtual Machines Under Maintenance Scenarios During maintenance and disaster recovery scenarios, Virtual Machine (VM) intersite migrations usually take place over limited bandwidthxe2x80x93typically Wide Area Network (WAN)xe2x80x93which is highly affected by the amount of interVM traffic that becomes separated during the migration process. This causes both a degradation of the Quality of Service (QoS) of intercommunicating VMs and an increase in the total migration time due to congestion of the migration link. We consider the problem of scheduling VM migration in those scenarios. In the first stage, we resort to graph partitioning theory in order to partition the VMs into groups with high intragroup communication. In the second stage, we devise an affinitybased scheduling algorithm for controlling the order of the migration groups by considering their intergroup traffic. Comprehensive reallife experimental results and simulations show that our approach in some cases is able to decrease the volume of separated traffic by a factor larger than 60%.

Advanced Validation of the DVMS Approach to Fully Distributed VM Scheduling The holy grail for Infrastructure as a Service (IaaS) providers is to maximize the utilization of their infrastructure while ensuring the quality of service (QoS) for the virtual machines they host. Although the frameworks in charge of managing virtual machines (VM) on pools of physical ones (PM) have been significantly improved, enabling to manage large scale infrastructures composed of hundreds of PMs, most of them do not efficiently handle the aforementioned objective. The main reason is that advanced scheduling policies are subject to important and hard scalability problems, that become even worse when VM image transfers have to be considered. In this article, we provide a new validation of the Distributed VM Scheduler approach (DVMS) in a twofold manner. First, we provide a formal proof of the algorithm based on temporal logic. Second, we discuss largescale evaluations involving up to 4.7K VMs distributed over 467 nodes of the Gridu00275000 testbed. As far as we know, these experiments constitute the largest in vivo validation that has been performed so far with decentralized VM schedulers. These results show that a cooperative approach such as ours permits to fix overload problems in a reactive and scalable way.