We live in the world of heterogeneous computing. With specialised elements reaching all aspects of our computer systems and their prevalence only growing, we must act to rein in their inherent complexity. One area that has seen significantly less investment in terms of development is heterogeneous-ISA systems, specifically because of complexity. To date, heterogeneous-ISA processors have required significant software overheads, workarounds, and coordination layers, making the development of more advanced software hard, and motivating little further development of more advanced hardware. In this paper, we take a fused approach to heterogeneity, and introduce a new operating system (OS) design, the fused-kernel OS, which goes beyond the multiple-kernel OS design, exploiting cache-coherent shared memory among heterogeneous-ISA CPUs as a first principle -- introducing a set of new OS kernel mechanisms. We built a prototype fused-kernel OS, Stramash-Linux, to demonstrate the applicability of our design to monolithic OS kernels. We profile Stramash OS components on real hardware but tested them on an architectural simulator -- Stramash-QEMU, which we design and build. Our evaluation begins by validating the accuracy of our simulator, achieving an average of less than 4% errors. We then perform a direct comparison between our fused-kernel OS and state-of-the-art multiple-kernel OS designs. Results demonstrate speedups of up to 2.1× on NPB benchmarks. Further, we provide an in-depth analysis of the differences and trade-offs between fused-kernel and multiple-kernel OS designs.

Cloud environments continue to experience substantial memory wastage due to inefficient resource sharing and workload variability. Emerging Compute Express Link (CXL) technology offers fabric-attached memory that can expand memory capacity despite added access latency, but existing VM allocation strategies in the Cloud have critical limitations. The static partitioning of local DRAM and CXL memory underutilize capacity, and cannot adapt to dynamic demands. Conversely, Host-managed tiering (software-managed placement) relies on page-table scans or sampling, which incur high CPU overhead.

We investigate the problem of getting hints on the effects of virtualization system (aka hypervisor) updates impact on virtual machines (VMs). System administrators can be reluctant to apply updates due to vague hints regarding the updates' impact on running applications. The problem is challenging since VMs are black boxes by design, reducing the scope of the data that can be retrieved and analyzed. Additionally, cloning VMs is only sometimes possible for obvious legal and privacy concerns.

To improve resource utilization and reduce costs many Cloud providers adopt virtual machines (VMs) overcommitment. While effective, this strategy may lead to adverse outcomes, significantly affecting a VM IO performance when one virtual CPU (vCPU) is preempted by another vCPU within the same runqueue of the VM scheduler -- i.e., same physical CPU (pCPU). Additionally, the responsiveness of a VM is reduced during the inactive time of the vCPU, and it necessitates an extra schedule timeslice to react to any IO event. While such problems have been studied in academia and industry, no previous solution has been deployed in production. This is because for example certain solutions require modifications of the guest VM, which is in contrast with industry requirements.

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).

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 that 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.

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.