Hardware resource fragmentation is a common issue in data centers. Traditional solutions based on migration or overcommitment are unacceptably slow, and modern commercial or research solutions like Spot VM may reduce or evict VM's resources anytime. We propose an alternative solution that does not suffer from these drawbacks, the Aggregate VM. We introduce a new distributed hypervisor design, the resource-borrowing hypervisor, which creates Aggregate VMs: distributed VMs that temporarily aggregate fragmented resources belonging to different host machines, which require mobility of virtual CPUs, memory and IO devices. We implement a prototype, FragVisor, which runs guest software transparently. We also propose minimal modifications to the guest OS that can enable significant performance gains. We evaluate FragVisor over a set of microbenchmarks and IaaS-style real applications. Although Aggregate VMs are not a perfect fit for every type of applications, some workloads enjoy significant speedups compared to overcommitted scenarios (up to 3.9x with 4 distributed vCPUs). We further demonstrate that FragVisor is faster than a state-of-the-art competitor, GiantVM (up to 2.5x).

Cloud Computing have highlighted the importance of container orchestration to manage distributed applicationy's life-cycle. With the advent of Edge Computing, DevOps expect to find the features of containers in the cloud, also at the edge. However, orchestration systems have not been designed to deal with geo-distribution aspects such as latency, intermittent networks, etc. In other words, it is unclear whether they could be directly used on top of massively distributed edge infrastructures without revision. In this paper, we provide an evaluation of Kubernetes in a WANWide context. Precisely, we present and discuss results we obtained during an experimental campaign to analyze the impact of WAN links on its behaviour. While there exist initiatives investigating Kubernetes revisions to deal with distribution aspects, there is, to the best of our knowledge, no rigorous performance evaluations to disqualify the vanilla code.