Most current infrastructures for cloud computing leverage static and greedy policies for the placement of virtual machines. Such policies impede the optimal allocation of resources from the infrastructure provider viewpoint. Over the last decade, more dynamic and often more efficient policies based, e.g., on consolidation and load balancing techniques, have been developed. Due to the underlying complexity of cloud infrastructures, these policies are evaluated either using limited scale testbeds/in-vivo experiments or ad-hoc simulators. These validation methodologies are unsatisfactory for two important reasons: they (i) do not model precisely enough real production platforms (size, workload variations, failure, etc.) and (ii) do not enable the fair comparison of different approaches. More generally, new placement algorithms are thus continuously being proposed without actually identifying their benefits with respect to the state of the art. In this article, we show how VMPlaceS, a dedicated simulation framework enables researchers (i) to study and compare VM placement algorithms from the infrastructure perspective, (ii) to detect possible limitations at large scale and (iii) to easily investigate different design choices. Built on top of the SimGrid simulation platform, VMPlaceS provides programming support to ease the implementation of placement algorithms and runtime support dedicated to load injection and execution trace analysis. To illustrate the relevance of VMPlaceS, we first discuss a few experiments that enabled us to study in details three well known VM placement strategies. Diving into details, we also identify several modifications that can significantly increase their performance in terms of reactivity. Second, we complete this overall presentation of VMPlaceS by focusing on the energy efficiency of the well-know FFD strategy. We believe that VMPlaceS will allow researchers to validate the benefits of new placement algorithms, thus accelerating placement research and favouring the transfer of results to IaaS production platforms.

Academic and industry experts are now advocating for going from large-centralized Cloud Computing infrastructures to smaller ones massively distributed at the edge of the network. Among the obstacles to the adoption of this model is the development of a convenient and powerful IaaS system capable of managing a significant number of remote data-centers in a unified way. In this paper, we introduce the premises of such a system by revising the OpenStack software, a leading IaaS manager in the industry. The novelty of our solution is to operate such an Internet-scale IaaS platform in a fully decentralized manner, using P2P mechanisms to achieve high flexibility and avoid single points of failure. More precisely, we describe how we revised the OpenStack Nova service by leveraging a distributed key/value store instead of the centralized SQL backend. We present experiments that validate the correct behavior and gives performance trends of our prototype through an emulation of several data-centers using Grid'5000 testbed. In addition to paving the way to the first large-scale and Internet-wide IaaS manager, we expect this work will attract a community of specialists from both distributed system and network areas to address the Fog/Edge Computing challenges within the OpenStack ecosystem.