Resource Utilization Analysis of Early Scheduling in Parallel State Machine Replication Most approaches to scaling the throughput of state machine replication resort to concurrent execution of nonconflicting requests. While these approaches enhance throughput, conflict detection and handling introduce overhead. Early scheduling is a promising technique that trades concurrency for expeditious scheduling decisions. In this technique, requests are grouped in classes and a fixed subset of threads is assigned to each class, respecting request dependencies. Early scheduling has been shown to provide significant performance improvements in state machine replication. This paper evaluates the impact of the restrictions imposed by the early scheduling technique. In particular, it shows that threads may be idle while pending independent requests are available to be executed, leading to poor processor utilization. We characterize resource underutilization for workloads with different rates of conflicting requests, number of threads, and number of request classes. The paper opens up new opportunities to further enhance the early scheduling technique.

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.