Edge computing is a recent computing paradigm that brings cloud services closer to the client. Among other features, edge computing offers extremely low client/server latencies. To consistently provide such low latencies, services need to run on edge nodes that are physically as close as possible to their clients. Thus, when a client changes its physical location, a service should migrate between edge nodes to maintain proximity. Differently from cloud nodes, edge nodes are built with CPUs of different Instruction Set Architectures (ISAs), hence a server program natively compiled for one ISA cannot migrate to another. This hinders migration to the closest node.We introduce H-Container, which migrates natively-compiled containerized applications across compute nodes featuring CPUs of different ISAs. H-Container advances over existing heterogeneous-ISA migration systems by being a) highly compatible -no source code nor compiler toolchain modifications are needed; b) easily deployable -fully implemented in user space, thus without any OS or hypervisor dependency, and c) largely Linux compliant -can migrate most Linux software, including server applications and dynamically linked binaries. H-Container targets Linux, adopts LLVM, extends CRIU, and integrates with Docker. Experiments demonstrate that H-Container adds no overhead on average during program execution, while between 10ms and 100ms are added during migration. Furthermore, we show the benefits of H-Container in real scenarios, proving for example up to 94% increase in Redis throughput when unlocking heterogeneity.CCS Concepts: • Computer systems organization → Heterogeneous (hybrid) systems; • Software and its engineering → Operating systems.

In this paper, we investigate the effectiveness of multiprocessor architectures with ISA-different cores for executing HPC workloads. Our envisioned design point in the heterogeneous architecture space is one with multiple cache-coherency domains, with each domain hosting cores of a different ISA and no coherency between domains. We prototype such an architecture using an Intel Xeon x86-64 server and a Cavium ThunderX ARMv8 server, interconnected using a high-speed network fabric. We design, implement, and evaluate policies for scheduling HPC applications with the goal of maximizing workload makespan. Our results reveal that such an architecture is most effective for workloads that exhibit diverse execution times on ISA-different CPUs, with gains exceeding 60% over ISA-homogeneous architectures. Furthermore, cross-ISA execution migration can yield gains up to 38%.

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National audienceCe papier présente GECOS (GEneration Counter Optimistic Synchronisation), un nouveau mécanisme de synchronisation permettant un accès lock-free (sans verrou) aux structures chaînées à plusieurs lecteurs et un écrivain. Ce mécanisme a été spécialement conçu pour prendre en considération les contraintes des architectures manycore en permettant non seulement des performances qui passent à l’échelle, mais aussi une utilisation optimale de la mémoire. Les premiers résultats, obtenus sur un système à 64 cœurs, montrent de très bonnes performances lors du passage à l’échelle, qui sont comparables, voire supérieures, à celles des mécanismes les plus performants tels que le mécanisme Read-Copy-Update ou le mécanisme Hazard Pointers

Recent advances in processor manufacturing has led to integrating tens of cores in a single chip and promise to integrate many more with the so-called manycore architectures. Manycore architectures usually integrate many small power efficient cores, which can be 32-bit cores in order to maximize the performance per Watt ratio. Providing large physical memory (e.g. 1 TB) to such architectures thus requires extending the physical address space (e.g. to 40 bits). This extended physical space has early been identified as a problem for 32-bit operating systems as they can normally support at maximum 4 GB of physical space, and up to 64 GB with memory extension techniques. This paper presents a scalable solution which efficiently manages large physical memory. The proposed solution decomposes the kernel into multiple units, each running in its own space, without the virtual memory mechanism (directly in physical mode). User applications, however, continue to run in the virtual mode. This solution allows the kernel to manage a large physical memory, while allowing user space applications to access nearly 4 GB of virtual space. It has been successfully implemented in ALMOS, a UNIX-like operating system, running on the TSAR manycore architecture, which is a 32-bit virtual 40-bit physical manycore architecture. Moreover, the first results show that this approach improves both the scalability and the performance of the system.

Edge computing is a recent computing paradigm that brings cloud services closer to the client. Among other features, edge computing offers extremely low client/server latencies. To consistently provide such low latencies, services should run on edge nodes that are physically as close as possible to their clients. Thus, when the physical location of a client changes, a service should migrate between edge nodes to maintain proximity. Differently from cloud nodes, edge nodes integrate CPUs of different Instruction Set Architectures (ISAs), hence a program natively compiled for a given ISA cannot migrate to a server equipped with a CPU of a different ISA. This hinders migration to the closest node. We introduce H-Container, a system which migrates natively-compiled containerized applications across compute nodes featuring CPUs of different ISAs. H-Container advances over existing heterogeneous-ISA migration systems by being a) highly compatible – no user’s source-code nor compiler toolchain modifications are needed; b) easily deployable – fully implemented in user space, thus without any OS or hypervisor dependency, and c) largely Linux compliant – it can migrate most Linux software, including server applications and dynamically linked binaries. H-Container targets Linux and its already-compiled executables, adopts LLVM, extends CRIU, and integrates with Docker. Experiments demonstrate that H-Container adds no overheads during program execution, while 10 − 100 ms are added during migration. Furthermore, we show the benefits of H-Container in real-world scenarios, demonstrating for example up to \(94\% \) increase in Redis throughput when client/server proximity is maintained through heterogeneous container migration.