Disparate deployment models of cloud computing pose varying requirements on cloud infrastructure components such as networking, storage, provisioning, and security. Infrastructure providers need to study these and often create custom infrastructure components to satisfy these requirements. A major challenge in the research and development of these cloud infrastructure solutions, however, is the availability of customizable platforms for experimentation and trade-off analysis of the various hardware and software components. Most platforms are either general purpose or bespoke solutions created to assist a particular task, too rigid to allow meaningful customization. In this work, we present a 100G reconfigurable smartNIC prototyping platform called Janus that enables cloud infrastructure research and hardware-software co-design of infrastructure components such as hypervisor, secure boot, software defined networking and distributed storage. The platform provides a path to optimize the stack by offloading the functionalities from the host x86 to the embedded processor on the smartNIC and optimize performance by moving pieces to hardware using P4. Further, our platform provides hardware-enforced isolation of cloud network control plane, thereby securing the control plane from the tenants even for bare-metal deployments.

Cloud networks support workloads with diverse characteristics. A key challenge facing cloud providers is how to meet the stringent performance and security needs of these diverse applications running over a shared infrastructure. To address this challenge, some providers build for peak capacity or even build dedicated clusters with specialized networks, which may be under-utilized at times. We propose a virtualization approach that customizes data center network resources to the needs of applications. Our approach is based on slicing data center network resources on-demand and customizing these slices to target workloads. Such slices can grow or shrink dynamically and programmatically based on workload demands. This elasticity provides a more efficient solution over building dedicated clusters. In our approach, a slice can be customized to a given set of workloads with similar security and performance requirements carved out of the underlying network. It leverages a software-defined underlay network controller and segment routing for fine-grained path control and service chaining. We have implemented a prototype of our fabric virtualization solution based on network slicing. In this paper, we first present the architecture of our prototype. Second, we present empirical results of slice provisioning times in networks of varying sizes and switch operating systems. Empirical results indicate that our prototype can support slice provisioning in the order of tens to hundreds of seconds and can meet the provisioning requirements of production networks.

With Moore's law progressively running out of steam, heterogeneous computing architectures have been powering the top supercomputers in the world for many years and are now finding broader adoption across the industry. The trend towards sustainable computing also requires domain-specific heterogeneous hardware architectures, which promise further gains in energy efficiency. At the same time, today's high performance computing applications have evolved from monolithic simulations in a single domain to multidisciplinary complex workflows. In this paper, we explore how these trends affect system design decisions and what this means for future computing system architectures.

Productivity from day one on supercomputers that leverage new technologies requires significant preparation. An institution that procures a novel system architecture often lacks sufficient institutional knowledge and skills to prepare for it. Thus, the "Center of Excellence" (CoE) concept has emerged to prepare for systems such as Summit and Sierra, currently the top two systems in the Top 500. This paper documents CoE experiences that prepared a workload of diverse applications and math libraries for a heterogeneous system. We describe our approach to this preparation, including our management and execution strategies, and detail our experiences with and reasons for using different programming approaches. Our early science and performance results show that the project enabled significant early seismic science with up to a l4X throughput increase over Cori. In addition to our successes, we discuss our challenges and failures so others may benefit from our experience.

Increasingly organizations are considering moving their workloads to clouds to take advantage of the anticipated benefits of a more cost effective and agile IT infrastructure. A key component of a cloud service, as it is exposed to the consumer, is the published selection of instance resource configurations (CPU, memory, and disk). The number of instance configurations, as well as the specific values that characterize them, form important decisions for the cloud service provider. This paper explores these resource configurations; examines how well a traditional data center fits into the cloud model from a resource allocation perspective; and proposes a framework, named CloudAffinity, aimed at selecting an optimal number of configurations based on customer requirements.

Task scheduling is essential for the proper functioning of parallel processor systems. Scheduling of tasks onto networks of parallel processors is an interesting problem that is well-defined and documented in the literature. However, most of the available techniques are based on heuristics that solve certain instances of the scheduling problem very efficiently and in reasonable amounts of time. This paper investigates an alternative paradigm, based on genetic algorithms, to efficiently solve the scheduling problem without the need to apply any restricted assumptions that are problem-specific, such is the case when using heuristics. Genetic algorithms are powerful search techniques based on the principles of evolution and natural selection. The performance of the genetic approach will be compared to the well-known list scheduling heuristics. The conditions under which a genetic algorithm performs best will also be highlighted. This will be accompanied by a number of examples and case studies.