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

Virtual InfiniBand Clusters for HPC Clouds

High Performance Computing (HPC) employs fast interconnect technologies to provide low communication and synchronization latencies for tightly coupled parallel compute jobs. Contemporary HPC clusters have a xed capacity and static runtime environments; they cannot elastically adapt to dynamic workloads, and provide a limited selection of applications, libraries, and system software. In contrast, a cloud model for HPC clusters promises more exibility, as it provides elastic virtual clusters to be available on-demand. This is not possible with physically owned clusters. In this paper, we present an approach that makes it possible to use InfiniBand clusters for HPC cloud computing. We propose a performance-driven design of an HPC IaaS layer for In niBand, which provides throughput and latency-aware virtualization of nodes, networks, and network topologies, as well as an approach to an HPC-aware, multi-tenant cloud management system for elastic virtualized HPC compute clusters

Routing on the Channel Dependency Graph:: A New Approach to Deadlock-Free, Destination-Based, High-Performance Routing for Lossless Interconnection Networks

In the pursuit for ever-increasing compute power, and with Moore's law slowly coming to an end, high-performance computing started to scale-out to larger systems. Alongside the increasing system size, the interconnection network is growing to accommodate and connect tens of thousands of compute nodes. These networks have a large influence on total cost, application performance, energy consumption, and overall system efficiency of the supercomputer. Unfortunately, state-of-the-art routing algorithms, which define the packet paths through the network, do not utilize this important resource efficiently. Topology-aware routing algorithms become increasingly inapplicable, due to irregular topologies, which either are irregular by design, or most often a result of hardware failures. Exchanging faulty network components potentially requires whole system downtime further increasing the cost of the failure. This management approach becomes more and more impractical due to the scale of today's networks and the accompanying steady decrease of the mean time between failures. Alternative methods of operating and maintaining these high-performance interconnects, both in terms of hardware- and software-management, are necessary to mitigate negative effects experienced by scientific applications executed on the supercomputer. However, existing topology-agnostic routing algorithms either suffer from poor load balancing or are not bounded in the number of virtual channels needed to resolve deadlocks in the routing tables. Using the fail-in-place strategy, a well-established method for storage systems to repair only critical component failures, is a feasible solution for current and future HPC interconnects as well as other large-scale installations such as data center networks. Although, an appropriate combination of topology and routing algorithm is required to minimize the throughput degradation for the entire system. This thesis contributes a network simulation toolchain to facilitate the process of finding a suitable combination, either during system design or while it is in operation. On top of this foundation, a key contribution is a novel scheduling-aware routing, which reduces fault-induced throughput degradation while improving overall network utilization. The scheduling-aware routing performs frequent property preserving routing updates to optimize the path balancing for simultaneously running batch jobs. The increased deployment of lossless interconnection networks, in conjunction with fail-in-place modes of operation and topology-agnostic, scheduling-aware routing algorithms, necessitates new solutions to solve the routing-deadlock problem. Therefore, this thesis further advances the state-of-the-art by introducing a novel concept of routing on the channel dependency graph, which allows the design of an universally applicable destination-based routing capable of optimizing the path balancing without exceeding a given number of virtual channels, which are a common hardware limitation. This disruptive innovation enables implicit deadlock-avoidance during path calculation, instead of solving both problems separately as all previous solutions

Datacenter Traffic Control: Understanding Techniques and Trade-offs

Datacenters provide cost-effective and flexible access to scalable compute and storage resources necessary for today's cloud computing needs. A typical datacenter is made up of thousands of servers connected with a large network and usually managed by one operator. To provide quality access to the variety of applications and services hosted on datacenters and maximize performance, it deems necessary to use datacenter networks effectively and efficiently. Datacenter traffic is often a mix of several classes with different priorities and requirements. This includes user-generated interactive traffic, traffic with deadlines, and long-running traffic. To this end, custom transport protocols and traffic management techniques have been developed to improve datacenter network performance. In this tutorial paper, we review the general architecture of datacenter networks, various topologies proposed for them, their traffic properties, general traffic control challenges in datacenters and general traffic control objectives. The purpose of this paper is to bring out the important characteristics of traffic control in datacenters and not to survey all existing solutions (as it is virtually impossible due to massive body of existing research). We hope to provide readers with a wide range of options and factors while considering a variety of traffic control mechanisms. We discuss various characteristics of datacenter traffic control including management schemes, transmission control, traffic shaping, prioritization, load balancing, multipathing, and traffic scheduling. Next, we point to several open challenges as well as new and interesting networking paradigms. At the end of this paper, we briefly review inter-datacenter networks that connect geographically dispersed datacenters which have been receiving increasing attention recently and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial

Application-centric bandwidth allocation in datacenters

Today's datacenters host a large number of concurrently executing applications with diverse intra-datacenter latency and bandwidth requirements. Some of these applications, such as data analytics, graph processing, and machine learning training, are data-intensive and require high bandwidth to function properly. However, these bandwidth-hungry applications can often congest the datacenter network, leading to queuing delays that hurt application completion time. To remove the network as a potential performance bottleneck, datacenter operators have begun deploying high-end HPC-grade networks like InfiniBand. These networks offer fully offloaded network stacks, remote direct memory access (RDMA) capability, and non-discarding links, which allow them to provide both low latency and high bandwidth for a single application. However, it is unclear how well such networks accommodate a mix of latency- and bandwidth-sensitive traffic in a real-world deployment. In this thesis, we aim to answer the above question. To do so, we develop RPerf, a latency measurement tool for RDMA-based networks that can precisely measure the InfiniBand switch latency without hardware support. Using RPerf, we benchmark a rack-scale InfiniBand cluster in both isolated and mixed-traffic scenarios. Our key finding is that the evaluated switch can provide either low latency or high bandwidth, but not both simultaneously in a mixed-traffic scenario. We also evaluate several options to improve the latency-bandwidth trade-off and demonstrate that none are ideal. We find that while queue separation is a solution to protect latency-sensitive applications, it fails to properly manage the bandwidth of other applications. We also aim to resolve the problem with bandwidth management for non-latency-sensitive applications. Previous efforts to address this problem have generally focused on achieving max-min fairness at the flow level. However, we observe that different workloads exhibit varying levels of sensitivity to network bandwidth. For some workloads, even a small reduction in available bandwidth can significantly increase completion time, while for others, completion time is largely insensitive to available network bandwidth. As a result, simply splitting the bandwidth equally among all workloads is sub-optimal for overall application-level performance. To address this issue, we first propose a robust methodology capable of effectively measuring the sensitivity of applications to bandwidth. We then design Saba, an application-aware bandwidth allocation framework that distributes network bandwidth based on application-level sensitivity. Saba combines ahead-of-time application profiling to determine bandwidth sensitivity with runtime bandwidth allocation using lightweight software support, with no modifications to network hardware or protocols. Experiments with a 32-server hardware testbed show that Saba can significantly increase overall performance by reducing the job completion time for bandwidth-sensitive jobs

New cross-layer techniques for multi-criteria scheduling in large-scale systems

The global ecosystem of information technology (IT) is in transition to a new generation of applications that require more and more intensive data acquisition, processing and storage systems. As a result of that change towards data intensive computing, there is a growing overlap between high performance computing (HPC) and Big Data techniques in applications, since many HPC applications produce large volumes of data, and Big Data needs HPC capabilities. The hypothesis of this PhD. thesis is that the potential interoperability and convergence of the HPC and Big Data systems are crucial for the future, being essential the unification of both paradigms to address a broad spectrum of research domains. For this reason, the main objective of this Phd. thesis is purposing and developing a monitoring system to allow the HPC and Big Data convergence, thanks to giving information about behaviors of applications in a system which execute both kind of them, giving information to improve scalability, data locality, and to allow adaptability to large scale computers. To achieve this goal, this work is focused on the design of resource monitoring and discovery to exploit parallelism at all levels. These collected data are disseminated to facilitate global improvements at the whole system, and, thus, avoid mismatches between layers. The result is a two-level monitoring framework (both at node and application level) with a low computational load, scalable, and that can communicate with different modules thanks to an API provided for this purpose. All data collected is disseminated to facilitate the implementation of improvements globally throughout the system, and thus avoid mismatches between layers, which combined with the techniques applied to deal with fault tolerance, makes the system robust and with high availability. On the other hand, the developed framework includes a task scheduler capable of managing the launch of applications, their migration between nodes, as well as the possibility of dynamically increasing or decreasing the number of processes. All these thanks to the cooperation with other modules that are integrated into LIMITLESS, and whose objective is to optimize the execution of a stack of applications based on multi-criteria policies. This scheduling mode is called coarse-grain scheduling based on monitoring. For better performance and in order to further reduce the overhead during the monitorization, different optimizations have been applied at different levels to try to reduce communications between components, while trying to avoid the loss of information. To achieve this objective, data filtering techniques, Machine Learning (ML) algorithms, and Neural Networks (NN) have been used. In order to improve the scheduling process and to design new multi-criteria scheduling policies, the monitoring information has been combined with other ML algorithms to identify (through classification algorithms) the applications and their execution phases, doing offline profiling. Thanks to this feature, LIMITLESS can detect which phase is executing an application and tries to share the computational resources with other applications that are compatible (there is no performance degradation between them when both are running at the same time). This feature is called fine-grain scheduling, and can reduce the makespan of the use cases while makes efficient use of the computational resources that other applications do not use.El ecosistema global de las tecnologías de la información (IT) se encuentra en transición a una nueva generación de aplicaciones que requieren sistemas de adquisición de datos, procesamiento y almacenamiento cada vez más intensivo. Como resultado de ese cambio hacia la computación intensiva de datos, existe una superposición, cada vez mayor, entre la computación de alto rendimiento (HPC) y las técnicas Big Data en las aplicaciones, pues muchas aplicaciones HPC producen grandes volúmenes de datos, y Big Data necesita capacidades HPC. La hipótesis de esta tesis es que hay un gran potencial en la interoperabilidad y convergencia de los sistemas HPC y Big Data, siendo crucial para el futuro tratar una unificación de ambos para hacer frente a un amplio espectro de problemas de investigación. Por lo tanto, el objetivo principal de esta tesis es la propuesta y desarrollo de un sistema de monitorización que facilite la convergencia de los paradigmas HPC y Big Data gracias a la provisión de datos sobre el comportamiento de las aplicaciones en un entorno en el que se pueden ejecutar aplicaciones de ambos mundos, ofreciendo información útil para mejorar la escalabilidad, la explotación de la localidad de datos y la adaptabilidad en los computadores de gran escala. Para lograr este objetivo, el foco se ha centrado en el diseño de mecanismos de monitorización y localización de recursos para explotar el paralelismo en todos los niveles de la pila del software. El resultado es un framework de monitorización en dos niveles (tanto a nivel de nodo como de aplicación) con una baja carga computacional, escalable, y que se puede comunicar con distintos módulos gracias a una API proporcionada para tal objetivo. Todos datos recolectados se difunden para facilitar la realización de mejoras de manera global en todo el sistema, y así evitar desajustes entre capas, lo que combinado con las técnicas aplicadas para lidiar con la tolerancia a fallos, hace que el sistema sea robusto y con una alta disponibilidad. Por otro lado, el framework desarrollado incluye un planificador de tareas capaz de gestionar el lanzamiento de aplicaciones, la migración de las mismas entre nodos, además de la posibilidad de incrementar o disminuir su número de procesos de forma dinámica. Todo ello gracias a la cooperación con otros módulos que se integran en LIMITLESS, y cuyo objetivo es optimizar la ejecución de una pila de aplicaciones en base a políticas multicriterio. Esta funcionalidad se llama planificación de grano grueso. Para un mejor desempeño y con el objetivo de reducir más aún la carga durante la ejecución, se han aplicado distintas optimizaciones en distintos niveles para tratar de reducir las comunicaciones entre componentes, a la vez que se trata de evitar la pérdida de información. Para lograr este objetivo se ha hecho uso de técnicas de filtrado de datos, algoritmos de Machine Learning (ML), y Redes Neuronales (NN). Finalmente, para obtener mejores resultados en la planificación de aplicaciones y para diseñar nuevas políticas de planificación multi-criterio, los datos de monitorización recolectados han sido combinados con nuevos algoritmos de ML para identificar (por medio de algoritmos de clasificación) aplicaciones y sus fases de ejecución. Todo ello realizando tareas de profiling offline. Gracias a estas técnicas, LIMITLESS puede detectar en qué fase de su ejecución se encuentra una determinada aplicación e intentar compartir los recursos de computacionales con otras aplicaciones que sean compatibles (no se produce una degradación del rendimiento entre ellas cuando ambas se ejecutan a la vez en el mismo nodo). Esta funcionalidad se llama planificación de grano fino y puede reducir el tiempo total de ejecución de la pila de aplicaciones en los casos de uso porque realiza un uso más eficiente de los recursos de las máquinas.This PhD dissertation has been partially supported by the Spanish Ministry of Science and Innovation under an FPI fellowship associated to a National Project with reference TIN2016-79637-P (from July 1, 2018 to October 10, 2021)Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Félix García Carballeira.- Secretario: Pedro Ángel Cuenca Castillo.- Vocal: María Cristina V. Marinesc