Unikernels are famous for providing excellent performance in terms of boot times, throughput and memory consumption, to name a few metrics. However, they are infamous for making it hard and extremely time consuming to extract such performance, and for needing significant engineering effort in order to port applications to them. We introduce Unikraft, a novel micro-library OS that (1) fully modularizes OS primitives so that it is easy to customize the unikernel and include only relevant components and (2) exposes a set of composable, performance-oriented APIs in order to make it easy for developers to obtain high performance.

Increasingly, embedded devices are being equipped with ARM processors. Because of ease-of-use and widespread support for drivers and applications, Linux is often used as the OS of choice, even though it consumes a significant amount of the device's limited resources and its large attack surface presents opportunities for exploits. In this paper we propose Unikraft, a fully librarized operating system and build tool which allows for generating specialized OSes and software stacks targeting specific applications, while removing unneeded functionality. As a proof of concept, we port Unikraft to the Raspberry Pi 3 B+ and to a Xilinx Ultra96-V2. On these boards, Unikraft is able to boot in 88-158 milliseconds, consume only hundreds of KBs of memory when running real-world application such as NGINX, all the while providing visible reductions in power consumption compared to Linux distributions. Unikraft is an open source project and can be found at unikraft.org.

Recent research has shown that unikernels, lightweight virtual machines tailored to specific applications, have great potential in terms of performance, tiny boot times, small memory consumption, and a reduced trusted compute base. Creating and optimizing them, however, is currently a painful, time-consuming process that often needs redoing for every application. With Unikraft, we introduce a system for automatically building unikernels that drastically reduces this time without negatively impacting performance.

Containers are in great demand because they are lightweight when compared to virtual machines. On the downside, containers offer weaker isolation than VMs, to the point where people run containers in virtual machines to achieve proper isolation. In this paper, we examine whether there is indeed a strict tradeoff between isolation (VMs) and efficiency (containers). We find that VMs can be as nimble as containers, as long as they are small and the toolstack is fast enough.

Video streaming dominates the Internet's overall traffic mix, with reports stating that it will constitute 90% of all consumer traffic by 2019. Most of this video is delivered by Content Delivery Networks (CDNs), and, while they optimize QoE metrics such as buffering ratio and start-up time, no single CDN provides optimal performance. In this paper we make the case for elastic CDNs, the ability to build virtual CDNs on-the-fly on top of shared, third-party infrastructure at a scale. To bring this idea closer to reality we begin by large-scale simulations to quantify the effects that elastic CDNs would have if deployed, and build and evaluate MiniCache, a specialized, minimalistic virtualized content cache that runs on the Xen hypervisor. MiniCache is able to serve content at rates of up to 32 Gb/s and handle up to 600K reqs/sec on a single CPU core, as well as boot in about 90 milliseconds on x86 and around 370 milliseconds on ARM32.

We explore the opportunities and design options enabled by novel SDN and NFV technologies, by re-designing a dynamic content delivery network (CDN) service. Our system, named MOSTO, provides performance levels comparable to that of a regular CDN, but does not require the deployment of a large distributed infrastructure. In the process of designing the system, we identify relevant functions that could be integrated in the future Internet infrastructure. Such functions greatly simplify the design and effectiveness of services, such as MOSTO. We demonstrate our system using a mixture of simulation, emulation, testbed experiments, and by realizing a proof-of-concept deployment in a planet-wide commercial cloud system.