Non-minimal adaptive routing for efficient interconnection networks

RESUMEN: La red de interconexión es un concepto clave de los sistemas de computación paralelos. El primer aspecto que define una red de interconexión es su topología. Habitualmente, las redes escalables y eficientes en términos de coste y consumo energético tienen bajo diámetro y se basan en topologías que encaran el límite de Moore y en las que no hay diversidad de caminos mínimos. Una vez definida la topología, quedando implícitamente definidos los límites de rendimiento de la red, es necesario diseñar un algoritmo de enrutamiento que se acerque lo máximo posible a esos límites y debido a la ausencia de caminos mínimos, este además debe explotar los caminos no mínimos cuando el tráfico es adverso. Estos algoritmos de enrutamiento habitualmente seleccionan entre rutas mínimas y no mínimas en base a las condiciones de la red. Las rutas no mínimas habitualmente se basan en el algoritmo de balanceo de carga propuesto por Valiant, esto implica que doblan la longitud de las rutas mínimas y por lo tanto, la latencia soportada por los paquetes se incrementa. En cuanto a la tecnología, desde su introducción en entornos HPC a principios de los años 2000, Ethernet ha sido usado en un porcentaje representativo de los sistemas. Esta tesis introduce una implementación realista y competitiva de una red escalable y sin pérdidas basada en dispositivos de red Ethernet commodity, considerando topologías de bajo diámetro y bajo consumo energético y logrando un ahorro energético de hasta un 54%. Además, propone un enrutamiento sobre la citada arquitectura, en adelante QCN-Switch, el cual selecciona entre rutas mínimas y no mínimas basado en notificaciones de congestión explícitas. Una vez implementada la decisión de enrutar siguiendo rutas no mínimas, se introduce un enrutamiento adaptativo en fuente capaz de adaptar el número de saltos en las rutas no mínimas. Este enrutamiento, en adelante ACOR, es agnóstico de la topología y mejora la latencia en hasta un 28%. Finalmente, se introduce un enrutamiento dependiente de la topología, en adelante LIAN, que optimiza el número de saltos de las rutas no mínimas basado en las condiciones de la red. Los resultados de su evaluación muestran que obtiene una latencia cuasi óptima y mejora el rendimiento de algoritmos de enrutamiento actuales reduciendo la latencia en hasta un 30% y obteniendo un rendimiento estable y equitativo.ABSTRACT: Interconnection network is a key concept of any parallel computing system. The first aspect to define an interconnection network is its topology. Typically, power and cost-efficient scalable networks with low diameter rely on topologies that approach the Moore bound in which there is no minimal path diversity. Once the topology is defined, the performance bounds of the network are determined consequently, so a suitable routing algorithm should be designed to accomplish as much as possible of those limits and, due to the lack of minimal path diversity, it must exploit non-minimal paths when the traffic pattern is adversarial. These routing algorithms usually select between minimal and non-minimal paths based on the network conditions, where the non-minimal paths are built according to Valiant load-balancing algorithm. This implies that these paths double the length of minimal ones and then the latency supported by packets increases. Regarding the technology, from its introduction in HPC systems in the early 2000s, Ethernet has been used in a significant fraction of the systems. This dissertation introduces a realistic and competitive implementation of a scalable lossless Ethernet network for HPC environments considering low-diameter and low-power topologies. This allows for up to 54% power savings. Furthermore, it proposes a routing upon the cited architecture, hereon QCN-Switch, which selects between minimal and non-minimal paths per packet based on explicit congestion notifications instead of credits. Once the miss-routing decision is implemented, it introduces two mechanisms regarding the selection of the intermediate switch to develop a source adaptive routing algorithm capable of adapting the number of hops in the non-minimal paths. This routing, hereon ACOR, is topology-agnostic and improves average latency in all cases up to 28%. Finally, a topology-dependent routing, hereon LIAN, is introduced to optimize the number of hops in the non-minimal paths based on the network live conditions. Evaluations show that LIAN obtains almost-optimal latency and outperforms state-of-the-art adaptive routing algorithms, reducing latency by up to 30.0% and providing stable throughput and fairness.This work has been supported by the Spanish Ministry of Education, Culture and Sports under grant FPU14/02253, the Spanish Ministry of Economy, Industry and Competitiveness under contracts TIN2010-21291-C02-02, TIN2013-46957-C2-2-P, and TIN2013-46957-C2-2-P (AEI/FEDER, UE), the Spanish Research Agency under contract PID2019-105660RBC22/AEI/10.13039/501100011033, the European Union under agreements FP7-ICT-2011- 7-288777 (Mont-Blanc 1) and FP7-ICT-2013-10-610402 (Mont-Blanc 2), the University of Cantabria under project PAR.30.P072.64004, and by the European HiPEAC Network of Excellence through an internship grant supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No. H2020-ICT-2015-687689

Creating a Worldwide Network For the Global Environment for Network Innovations (GENI) and Related Experimental Environments

Many important societal activities are global in scope, and as these activities continually expand world-wide, they are increasingly based on a foundation of advanced communication services and underlying innovative network architecture, technology, and core infrastructure. To continue progress in these areas, research activities cannot be limited to campus labs and small local testbeds or even to national testbeds. Researchers must be able to explore concepts at scale—to conduct experiments on world-wide testbeds that approximate the attributes of the real world. Today, it is possible to take advantage of several macro information technology trends, especially virtualization and capabilities for programming technology resources at a highly granulated level, to design, implement and operate network research environments at a global scale. GENI is developing such an environment, as are research communities in a number of other countries. Recently, these communities have not only been investigating techniques for federating these research environments across multiple domains, but they have also been demonstration prototypes of such federations. This chapter provides an overview of key topics and experimental activities related to GENI international networking and to related projects throughout the world

Designing, Building, and Modeling Maneuverable Applications within Shared Computing Resources

Extending the military principle of maneuver into war-fighting domain of cyberspace, academic and military researchers have produced many theoretical and strategic works, though few have focused on researching actual applications and systems that apply this principle. We present our research in designing, building and modeling maneuverable applications in order to gain the system advantages of resource provisioning, application optimization, and cybersecurity improvement. We have coined the phrase “Maneuverable Applications” to be defined as distributed and parallel application that take advantage of the modification, relocation, addition or removal of computing resources, giving the perception of movement. Our work with maneuverable applications has been within shared computing resources, such as the Clemson University Palmetto cluster, where multiple users share access and time to a collection of inter-networked computers and servers. In this dissertation, we describe our implementation and analytic modeling of environments and systems to maneuver computational nodes, network capabilities, and security enhancements for overcoming challenges to a cyberspace platform. Specifically we describe our work to create a system to provision a big data computational resource within academic environments. We also present a computing testbed built to allow researchers to study network optimizations of data centers. We discuss our Petri Net model of an adaptable system, which increases its cybersecurity posture in the face of varying levels of threat from malicious actors. Lastly, we present work and investigation into integrating these technologies into a prototype resource manager for maneuverable applications and validating our model using this implementation

A manifesto for future generation cloud computing: research directions for the next decade

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing

Application-Aware Resource Management for Cloud Platforms

Cloud computing has become increasingly popular in recent years. The benefits of cloud platforms include ease of application deployment, a pay-as-you-go model, and the ability to scale resources up or down based on an application\u27s workload. Today\u27s cloud platforms are being used to host increasingly complex distributed and parallel applications. The main premise of this thesis is that application-aware resource management techniques are better suited for distributed cloud applications over a systems-level one-size-fits-all approach. In this thesis, I study the cloud-based resource management techniques with a particular emphasis on how application-aware approaches can be used to improve system resource utilization and enhance applications\u27 performance and cost. I first study always-on interactive applications that run on transient cloud servers such as Amazon spot instances. I show that by combining techniques like nested virtualization, live migration and lazy restoration together with intelligent bidding strategies, it is feasible to provide high availability to such applications while significantly reducing cost. I next study how to improve performance of parallel data processing applications like Hadoop and Spark that run in the cloud. I argue that network I/O contention in Hadoop can impact application throughput and implement a collaborative application-aware network and task scheduler using software-defined networking. By combining flow scheduling with task scheduling, our system can effectively avoid network contention and improve Hadoop\u27s performance. I then investigate similar issues in Spark and find that task scheduling is more important for Spark jobs. I propose a network-aware task scheduling method that can adaptively schedule tasks for different types of jobs without system tuning and improve Spark\u27s performance significantly. Finally, I study how to deploy network functions in the cloud. Specifically, I focus on comparing different methods of chaining network functions. By carrying out empirical evaluation of two different deployment methods, we figure out the advantages and disadvantages of each method. Our results suggest that the tenant-centric placement provides lower latencies while service-centric approach is more flexible for reconfiguration and capacity scaling

A survey and classification of software-defined storage systems

The exponential growth of digital information is imposing increasing scale and efficiency demands on modern storage infrastructures. As infrastructure complexity increases, so does the difficulty in ensuring quality of service, maintainability, and resource fairness, raising unprecedented performance, scalability, and programmability challenges. Software-Defined Storage (SDS) addresses these challenges by cleanly disentangling control and data flows, easing management, and improving control functionality of conventional storage systems. Despite its momentum in the research community, many aspects of the paradigm are still unclear, undefined, and unexplored, leading to misunderstandings that hamper the research and development of novel SDS technologies. In this article, we present an in-depth study of SDS systems, providing a thorough description and categorization of each plane of functionality. Further, we propose a taxonomy and classification of existing SDS solutions according to different criteria. Finally, we provide key insights about the paradigm and discuss potential future research directions for the field.This work was financed by the Portuguese funding agency FCT-Fundacao para a Ciencia e a Tecnologia through national funds, the PhD grant SFRH/BD/146059/2019, the project ThreatAdapt (FCT-FNR/0002/2018), the LASIGE Research Unit (UIDB/00408/2020), and cofunded by the FEDER, where applicable

On Improving Efficiency of Data-Intensive Applications in Geo-Distributed Environments

Distributed systems are pervasively demanded and adopted in nowadays for processing data-intensive workloads since they greatly accelerate large-scale data processing with scalable parallelism and improved data locality. Traditional distributed systems initially targeted computing clusters but have since evolved to data centers with multiple clusters. These systems are mostly built on top of homogeneous, tightly integrated resources connected in high-speed local-area networks (LANs), and typically require data to be ingested to a central data center for processing. Today, with enormous volumes of data continuously generated from geographically distributed locations, direct adoption of such systems is prohibitively inefficient due to the limited system scalability and high cost for centralizing the geo-distributed data over the wide-area networks (WANs). More commonly, it becomes a trend to build geo-distributed systems wherein data processing jobs are performed on top of geo-distributed, heterogeneous resources in proximity to the data at vastly distributed geo-locations. However, critical challenges and mechanisms for efficient execution of data-intensive applications in such geo-distributed environments are unclear by far. The goal of this dissertation is to identify such challenges and mechanisms, by extensively using the research principles and methodology of conventional distributed systems to investigate the geo-distributed environment, and by developing new techniques to tackle these challenges and run data-intensive applications with efficiency at scale. The contributions of this dissertation are threefold. Firstly, the dissertation shows that the high level of resource heterogeneity exhibited in the geo-distributed environment undermines the scalability of geo-distributed systems. Virtualization-based resource abstraction mechanisms have been introduced to abstract the hardware, network, and OS resources throughout the system, to mitigate the underlying resource heterogeneity and enhance the system scalability. Secondly, the dissertation reveals the overwhelming performance and monetary cost incurred by indulgent data sharing over the WANs in geo-distributed systems. Network optimization approaches, including linear- programming-based global optimization, greedy bin-packing heuristics, and TCP enhancement, are developed to optimize the network resource utilization and circumvent unnecessary expenses imposed on data sharing in WANs. Lastly, the dissertation highlights the importance of data locality for data-intensive applications running in the geo-distributed environment. Novel data caching and locality-aware scheduling techniques are devised to improve the data locality.Doctor of Philosoph