A SOFTWARE DEFINED NETWORKING ARCHITECTURE FOR HIGH PERFORMANCE CLOUDS 1

ABSTRACT-Multi-tenant clouds with resource virtualization offer elasticity of resources and elimination of initial cluster setup cost and time for applications. However, poor network performance, performance variation and noisy neighbors are some of the challenges for execution of high performance applications on public clouds. Utilizing these virtualized resources for scientific applications, which have complex communication patterns, require low latency communication mechanisms and a rich set of communication constructs. To minimize the virtualization overhead, a novel approach for low latency networking for HPC Clouds is proposed and implemented over a multi-technology software defined network. The efficiency of the proposed low-latency SDN is analyzed and evaluated for high performance applications. The results of the experiments show that the latest Mellanox FDR InfiniBand interconnect and Mellanox OpenStack plugin gives the best performance for implementing virtual machine based high performance clouds with large message sizes

GNFC: Towards Network Function Cloudification

An increasing demand is seen from enterprises to host and dynamically manage middlebox services in public clouds in order to leverage the same benefits that network functions provide in traditional, in-house deployments. However, today's public clouds provide only a limited view and programmability for tenants that challenges flexible deployment of transparent, software-defined network functions. Moreover, current virtual network functions can't take full advantage of a virtualized cloud environment, limiting scalability and fault tolerance. In this paper we review and evaluate the current infrastructural limitations imposed by public cloud providers and present the design and implementation of GNFC, a cloud-based Network Function Virtualization (NFV) framework that gives tenants the ability to transparently attach stateless, container-based network functions to their services hosted in public clouds. We evaluate the proposed system over three public cloud providers (Amazon EC2, Microsoft Azure and Google Compute Engine) and show the effects on end-to-end latency and throughput using various instance types for NFV hosts

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

Evaluation of Docker Containers for Scientific Workloads in the Cloud

The HPC community is actively researching and evaluating tools to support execution of scientific applications in cloud-based environments. Among the various technologies, containers have recently gained importance as they have significantly better performance compared to full-scale virtualization, support for microservices and DevOps, and work seamlessly with workflow and orchestration tools. Docker is currently the leader in containerization technology because it offers low overhead, flexibility, portability of applications, and reproducibility. Singularity is another container solution that is of interest as it is designed specifically for scientific applications. It is important to conduct performance and feature analysis of the container technologies to understand their applicability for each application and target execution environment. This paper presents a (1) performance evaluation of Docker and Singularity on bare metal nodes in the Chameleon cloud (2) mechanism by which Docker containers can be mapped with InfiniBand hardware with RDMA communication and (3) analysis of mapping elements of parallel workloads to the containers for optimal resource management with container-ready orchestration tools. Our experiments are targeted toward application developers so that they can make informed decisions on choosing the container technologies and approaches that are suitable for their HPC workloads on cloud infrastructure. Our performance analysis shows that scientific workloads for both Docker and Singularity based containers can achieve near-native performance. Singularity is designed specifically for HPC workloads. However, Docker still has advantages over Singularity for use in clouds as it provides overlay networking and an intuitive way to run MPI applications with one container per rank for fine-grained resources allocation

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR