Summary Nowadays, datacenters lean on a computer‐centric approach based on monolithic servers which include all necessary hardware resources (mainly CPU, RAM, network, and disks) to run applications. Such an architecture comes with two main limitations: (1) difficulty to achieve full resource utilization and (2) coarse granularity for hardware maintenance. Recently, many works investigated a resource‐centric approach called disaggregated architecture where the datacenter is composed of self‐content resource boards interconnected using fast interconnection technologies, each resource board including instances of one resource type. The resource‐centric architecture allows each resource to be managed (maintenance, allocation) independently. LegoOS is the first work which studied the implications of disaggregation on the operating system, proposing to disaggregate the operating system itself. They demonstrated the suitability of this approach, considering mainly CPU and RAM resources. However, they did not study the implication of disaggregation on network resources. We reproduced a LegoOS infrastructure and extended it to support disaggregated networking. We show that networking can be disaggregated following the same principles, and that classical networking optimizations such as DMA, DDIO, or loopback can be reproduced in such an environment. Our evaluations show the viability of the approach and the potential of future disaggregated infrastructures.

Resource management is a critical issue in today’s virtualized computing infrastructures. Consolidation is the main technique used to optimize such infrastructure. Regarding memory management, it allows gathering overloaded and underloaded VM on the same server so that memory can be mutualized. However, because of infrastructures constraints and complexity of managing multiple resources, consolidation can hardly optimize memory management. In this article, we propose to rely on a remote memory sharing for mutualizing memory. We implemented a system which monitors the working set of virtual machines, reclaims unused memory and makes it available (as a remote swap device) for virtual machines which need memory. Our evaluations with HPC and Big Data benchmarks demonstrate the effectiveness of this approach. We show that remote memory can improve the performance of a standard Spark benchmark by up the 17 percent with an average performance degradation of 1.5 percent (for the providing application).

Designing a cloud infrastructure for HPC applications requires to correlate design choices for virtualization solutions, resources management strategies, and their implementation on specific hardware platforms. Such investigations can hardly be conducted without accurate simulation tools. This paper introduces VMcSim, the first micro architecture and virtualized many core simulation framework. VMcSim models a x86-based asymmetric many core micro architecture, including a virtualization layer which allows to model a hyper visor, a set of virtual machines with their virtual resources (CPU and memory), their operating system, and their applications. By simulating all the involved hardware and software layers, VMcSim outperforms other simulators. Simulation accuracy is evaluated through several benchmark (SPLASH-2). The simulator is demonstrated with the evaluation of virtual resources placement policies in multicore systems.