Rack-Scale Memory Pooling for Datacenters

The rise of web-scale services has led to a staggering growth in user data on the Internet. To transform such a vast raw data into valuable information for the user and provide quality assurances, it is important to minimize access latency and enable in-memory processing. For more than a decade, the only practical way to accommodate for ever-growing data in memory has been to scale out server resources, which has led to the emergence of large-scale datacenters and distributed non-relational databases (NoSQL). Such horizontal scaling of resources translates to an increasing number of servers that participate in processing individual user requests. Typically, each user request results in hundreds of independent queries targeting different NoSQL nodes - servers, and the larger the number of servers involved, the higher the fan-out. To complete a single user request, all of the queries associated with that request have to complete first, and thus, the slowest query determines the completion time. Because of skewed popularity distributions and resource contention, the more servers we have, the harder it is to achieve high throughput and facilitate server utilization, without violating service level objectives. This thesis proposes rack-scale memory pooling (RSMP), a new scaling technique for future datacenters that reduces networking overheads and improves the performance of core datacenter software. RSMP is an approach to building larger, rack-scale capacity units for datacenters through specialized fabric interconnects with support for one-sided operations, and using them, in lieu of conventional servers (e.g. 1U), to scale out. We define an RSMP unit to be a server rack connecting 10s to 100s of servers to a secondary network enabling direct, low-latency access to the global memory of the rack. We, then, propose a new RSMP design - Scale-Out NUMA that leverages integration and a NUMA fabric to bridge the gap between local and remote memory to only 5× difference in access latency. Finally, we show how RSMP impacts NoSQL data serving, a key datacenter service used by most web-scale applications today. We show that using fewer larger data shards leads to less load imbalance and higher effective throughput, without violating applications¿ service level objectives. For example, by using Scale-Out NUMA, RSMP improves the throughput of a key-value store up to 8.2× over a traditional scale-out deployment

Graph Processing in Main-Memory Column Stores

Evermore, novel and traditional business applications leverage the advantages of a graph data model, such as the offered schema flexibility and an explicit representation of relationships between entities. As a consequence, companies are confronted with the challenge of storing, manipulating, and querying terabytes of graph data for enterprise-critical applications. Although these business applications operate on graph-structured data, they still require direct access to the relational data and typically rely on an RDBMS to keep a single source of truth and access. Existing solutions performing graph operations on business-critical data either use a combination of SQL and application logic or employ a graph data management system. For the first approach, relying solely on SQL results in poor execution performance caused by the functional mismatch between typical graph operations and the relational algebra. To the worse, graph algorithms expose a tremendous variety in structure and functionality caused by their often domain-specific implementations and therefore can be hardly integrated into a database management system other than with custom coding. Since the majority of these enterprise-critical applications exclusively run on relational DBMSs, employing a specialized system for storing and processing graph data is typically not sensible. Besides the maintenance overhead for keeping the systems in sync, combining graph and relational operations is hard to realize as it requires data transfer across system boundaries. A basic ingredient of graph queries and algorithms are traversal operations and are a fundamental component of any database management system that aims at storing, manipulating, and querying graph data. Well-established graph traversal algorithms are standalone implementations relying on optimized data structures. The integration of graph traversals as an operator into a database management system requires a tight integration into the existing database environment and a development of new components, such as a graph topology-aware optimizer and accompanying graph statistics, graph-specific secondary index structures to speedup traversals, and an accompanying graph query language. In this thesis, we introduce and describe GRAPHITE, a hybrid graph-relational data management system. GRAPHITE is a performance-oriented graph data management system as part of an RDBMS allowing to seamlessly combine processing of graph data with relational data in the same system. We propose a columnar storage representation for graph data to leverage the already existing and mature data management and query processing infrastructure of relational database management systems. At the core of GRAPHITE we propose an execution engine solely based on set operations and graph traversals. Our design is driven by the observation that different graph topologies expose different algorithmic requirements to the design of a graph traversal operator. We derive two graph traversal implementations targeting the most common graph topologies and demonstrate how graph-specific statistics can be leveraged to select the optimal physical traversal operator. To accelerate graph traversals, we devise a set of graph-specific, updateable secondary index structures to improve the performance of vertex neighborhood expansion. Finally, we introduce a domain-specific language with an intuitive programming model to extend graph traversals with custom application logic at runtime. We use the LLVM compiler framework to generate efficient code that tightly integrates the user-specified application logic with our highly optimized built-in graph traversal operators. Our experimental evaluation shows that GRAPHITE can outperform native graph management systems by several orders of magnitude while providing all the features of an RDBMS, such as transaction support, backup and recovery, security and user management, effectively providing a promising alternative to specialized graph management systems that lack many of these features and require expensive data replication and maintenance processes

Architectural Enhancements for Data Transport in Datacenter Systems

Datacenter systems run myriad applications, which frequently communicate with each other and/or Input/Output (I/O) devices—including network adapters, storage devices, and accelerators. Due to the growing speed of I/O devices and the emergence of microservice-based programming models, the I/O software stacks have become a critical factor in end-to-end communication performance. As such, I/O software stacks have been evolving rapidly in recent years. Datacenters rely on fast, efficient “Software Data Planes”, which orchestrate data transfer between applications and I/O devices. The goal of this dissertation is to enhance the performance, efficiency, and scalability of software data planes by diagnosing their existing issues and addressing them through hardware-software solutions. In the first step, I characterize challenges of modern software data planes, which bypass the operating system kernel to avoid associated overheads. Since traditional interrupts and system calls cannot be delivered to user code without kernel assistance, kernel-bypass data planes use spinning cores on I/O queues to identify work/data arrival. Spin-polling obviously wastes CPU cycles on checking empty queues; however, I show that it entails even more drawbacks: (1) Full-tilt spinning cores perform more (useless) polling work when there is less work pending in the queues. (2) Spin-polling scales poorly with the number of polled queues due to processor cache capacity constraints, especially when traffic is unbalanced. (3) Spin-polling also scales poorly with the number of cores due to the overhead of polling and operation rate limits. (4) Whereas shared queues can mitigate load imbalance and head-of-line blocking, synchronization overheads of spinning on them limit their potential benefits. Next, I propose a notification accelerator, dubbed HyperPlane, which replaces spin-polling in software data planes. Design principles of HyperPlane are: (1) not iterating on empty I/O queues to find work/data in ready ones, (2) blocking/halting when all queues are empty rather than spinning fruitlessly, and (3) allowing multiple cores to efficiently monitor a shared set of queues. These principles lead to queue scalability, work proportionality, and enjoying theoretical merits of shared queues. HyperPlane is realized with a programming model front-end and a hardware microarchitecture back-end. Evaluation of HyperPlane shows its significant advantage in terms of throughput, average/tail latency, and energy efficiency over a state-of-the-art spin-polling-based software data plane, with very small power and area overheads. Finally, I focus on the data transfer aspect in software data planes. Cache misses incurred by accessing I/O data are a major bottleneck in software data planes. Despite considerable efforts put into delivering I/O data directly to the last-level cache, some access latency is still exposed. Cores cannot prefetch such data to nearer caches in today's systems because of the complex access pattern of data buffers and the lack of an appropriate notification mechanism that can trigger the prefetch operations. As such, I propose HyperData, a data transfer accelerator based on targeted prefetching. HyperData prefetches exact (rather than predicted) data buffers (or a required subset to avoid cache pollution) to the L1 cache of the consumer core at the right time. Prefetching can be done for both core-peripheral and core-core communications. HyperData's prefetcher is programmable and supports various queue formats—namely, direct (regular), indirect (Virtio), and multi-consumer queues. I show that with a minor overhead, HyperData effectively hides data access latency in software data planes, thereby improving both application- and system-level performance and efficiency.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169826/1/hosseing_1.pd

A framework for the dynamic management of Peer-to-Peer overlays

Peer-to-Peer (P2P) applications have been associated with inefficient operation, interference with other network services and large operational costs for network providers. This thesis presents a framework which can help ISPs address these issues by means of intelligent management of peer behaviour. The proposed approach involves limited control of P2P overlays without interfering with the fundamental characteristics of peer autonomy and decentralised operation. At the core of the management framework lays the Active Virtual Peer (AVP). Essentially intelligent peers operated by the network providers, the AVPs interact with the overlay from within, minimising redundant or inefficient traffic, enhancing overlay stability and facilitating the efficient and balanced use of available peer and network resources. They offer an “insider‟s” view of the overlay and permit the management of P2P functions in a compatible and non-intrusive manner. AVPs can support multiple P2P protocols and coordinate to perform functions collectively. To account for the multi-faceted nature of P2P applications and allow the incorporation of modern techniques and protocols as they appear, the framework is based on a modular architecture. Core modules for overlay control and transit traffic minimisation are presented. Towards the latter, a number of suitable P2P content caching strategies are proposed. Using a purpose-built P2P network simulator and small-scale experiments, it is demonstrated that the introduction of AVPs inside the network can significantly reduce inter-AS traffic, minimise costly multi-hop flows, increase overlay stability and load-balancing and offer improved peer transfer performance

Systems and Methods for Measuring and Improving End-User Application Performance on Mobile Devices

In today's rapidly growing smartphone society, the time users are spending on their smartphones is continuing to grow and mobile applications are becoming the primary medium for providing services and content to users. With such fast paced growth in smart-phone usage, cellular carriers and internet service providers continuously upgrade their infrastructure to the latest technologies and expand their capacities to improve the performance and reliability of their network and to satisfy exploding user demand for mobile data. On the other side of the spectrum, content providers and e-commerce companies adopt the latest protocols and techniques to provide smooth and feature-rich user experiences on their applications. To ensure a good quality of experience, monitoring how applications perform on users' devices is necessary. Often, network and content providers lack such visibility into the end-user application performance. In this dissertation, we demonstrate that having visibility into the end-user perceived performance, through system design for efficient and coordinated active and passive measurements of end-user application and network performance, is crucial for detecting, diagnosing, and addressing performance problems on mobile devices. My dissertation consists of three projects to support this statement. First, to provide such continuous monitoring on smartphones with constrained resources that operate in such a highly dynamic mobile environment, we devise efficient, adaptive, and coordinated systems, as a platform, for active and passive measurements of end-user performance. Second, using this platform and other passive data collection techniques, we conduct an in-depth user trial of mobile multipath to understand how Multipath TCP (MPTCP) performs in practice. Our measurement study reveals several limitations of MPTCP. Based on the insights gained from our measurement study, we propose two different schemes to address the identified limitations of MPTCP. Last, we show how to provide visibility into the end- user application performance for internet providers and in particular home WiFi routers by passively monitoring users' traffic and utilizing per-app models mapping various network quality of service (QoS) metrics to the application performance.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146014/1/ashnik_1.pd

NFV Platforms: Taxonomy, Design Choices and Future Challenges

Due to the intrinsically inefficient service provisioning in traditional networks, Network Function Virtualization (NFV) keeps gaining attention from both industry and academia. By replacing the purpose-built, expensive, proprietary network equipment with software network functions consolidated on commodity hardware, NFV envisions a shift towards a more agile and open service provisioning paradigm. During the last few years, a large number of NFV platforms have been implemented in production environments that typically face critical challenges, including the development, deployment, and management of Virtual Network Functions (VNFs). Nonetheless, just like any complex system, such platforms commonly consist of abounding software and hardware components and usually incorporate disparate design choices based on distinct motivations or use cases. This broad collection of convoluted alternatives makes it extremely arduous for network operators to make proper choices. Although numerous efforts have been devoted to investigating different aspects of NFV, none of them specifically focused on NFV platforms or attempted to explore their design space. In this paper, we present a comprehensive survey on the NFV platform design. Our study solely targets existing NFV platform implementations. We begin with a top-down architectural view of the standard reference NFV platform and present our taxonomy of existing NFV platforms based on what features they provide in terms of a typical network function life cycle. Then we thoroughly explore the design space and elaborate on the implementation choices each platform opts for. We also envision future challenges for NFV platform design in the incoming 5G era. We believe that our study gives a detailed guideline for network operators or service providers to choose the most appropriate NFV platform based on their respective requirements. Our work also provides guidelines for implementing new NFV platforms

Signal Processing for Caching Networks and Non-volatile Memories

The recent information explosion has created a pressing need for faster and more reliable data storage and transmission schemes. This thesis focuses on two systems: caching networks and non-volatile storage systems. It proposes network protocols to improve the efficiency of information delivery and signal processing schemes to reduce errors at the physical layer as well. This thesis first investigates caching and delivery strategies for content delivery networks. Caching has been investigated as a useful technique to reduce the network burden by prefetching some contents during o˙-peak hours. Coded caching [1] proposed by Maddah-Ali and Niesen is the foundation of our algorithms and it has been shown to be a useful technique which can reduce peak traffic rates by encoding transmissions so that different users can extract different information from the same packet. Content delivery networks store information distributed across multiple servers, so as to balance the load and avoid unrecoverable losses in case of node or disk failures. On one hand, distributed storage limits the capability of combining content from different servers into a single message, causing performance losses in coded caching schemes. But, on the other hand, the inherent redundancy existing in distributed storage systems can be used to improve the performance of those schemes through parallelism. This thesis proposes a scheme combining distributed storage of the content in multiple servers and an efficient coded caching algorithm for delivery to the users. This scheme is shown to reduce the peak transmission rate below that of state-of-the-art algorithms

High-performance software packet processing

In today’s Internet, it is highly desirable to have fast and scalable software packet processing solutions for network applications that run on commodity hardware. The advent of cloud computing drives the continued rapid growth of Internet traffic. Moreover, the development of emerging networking techniques, such as Network Function Virtualization, significantly shapes the need for implementing the network functions in software. Finally, with the advancement of modern platforms as well as software frameworks for packet processing, network applications have potential to process 100+ Gbps network traffic on a single commodity server. Representative frameworks include the Click modular router, the RouteBricks scalable routing architecture, and BUFFALO, the software-based Ethernet switch. Beneath this general-purpose routing and switching functionality lie a broad set of network applications, many of which are handled with custom methods to provide cost-effectiveness and flexibility. This thesis considers two long-standing networking applications, IP lookup and distributed denial-of-service (DDoS) mitigation, and proposes efficient software-based methods drawing from this new perspective. In this thesis, we first introduce several optimization techniques to accelerate network applications by taking advantage of modern CPU features. Then, we explore the IP lookup problem to find the longest matching prefix of an IP address in a set of prefixes. An ideal IP lookup algorithm should achieve small constant IP lookup time, and on-chip memory usage. However, no prior IP lookup algorithm achieves both requirements at the same time. We propose SAIL, a splitting approach to IP lookup, and a suite of algorithms for IP lookup based on SAIL framework. We conducted extensive experiments to evaluate our algorithms, and experimental results show that our SAIL algorithms are much faster than well-known IP lookup algorithms. Next, we switch our focus to DDoS, an attempt to disrupt the legitimate traffic of a victim by sending a flood of Internet traffic from different sources. Our solution is Gatekeeper, the first open-source and deployable DDoS mitigation system. We present a series of optimization techniques, including use of modern platforms, group prefetching, coroutines, and hashing, to accelerate Gatekeeper. Experimental results show that these optimization techniques significantly improve its performance over alternative baseline solutions.2022-01-30T00:00:00

QoE-Centric Control and Management of Multimedia Services in Software Defined and Virtualized Networks

Multimedia services consumption has increased tremendously since the deployment of 4G/LTE networks. Mobile video services (e.g., YouTube and Mobile TV) on smart devices are expected to continue to grow with the emergence and evolution of future networks such as 5G. The end user’s demand for services with better quality from service providers has triggered a trend towards Quality of Experience (QoE) - centric network management through efficient utilization of network resources. However, existing network technologies are either unable to adapt to diverse changing network conditions or limited in available resources. This has posed challenges to service providers for provisioning of QoE-centric multimedia services. New networking solutions such as Software Defined Networking (SDN) and Network Function Virtualization (NFV) can provide better solutions in terms of QoE control and management of multimedia services in emerging and future networks. The features of SDN, such as adaptability, programmability and cost-effectiveness make it suitable for bandwidth-intensive multimedia applications such as live video streaming, 3D/HD video and video gaming. However, the delivery of multimedia services over SDN/NFV networks to achieve optimized QoE, and the overall QoE-centric network resource management remain an open question especially in the advent development of future softwarized networks. The work in this thesis intends to investigate, design and develop novel approaches for QoE-centric control and management of multimedia services (with a focus on video streaming services) over software defined and virtualized networks. First, a video quality management scheme based on the traffic intensity under Dynamic Adaptive Video Streaming over HTTP (DASH) using SDN is developed. The proposed scheme can mitigate virtual port queue congestion which may cause buffering or stalling events during video streaming, thus, reducing the video quality. A QoE-driven resource allocation mechanism is designed and developed for improving the end user’s QoE for video streaming services. The aim of this approach is to find the best combination of network node functions that can provide an optimized QoE level to end-users through network node cooperation. Furthermore, a novel QoE-centric management scheme is proposed and developed, which utilizes Multipath TCP (MPTCP) and Segment Routing (SR) to enhance QoE for video streaming services over SDN/NFV-based networks. The goal of this strategy is to enable service providers to route network traffic through multiple disjointed bandwidth-satisfying paths and meet specific service QoE guarantees to the end-users. Extensive experiments demonstrated that the proposed schemes in this work improve the video quality significantly compared with the state-of-the- art approaches. The thesis further proposes the path protections and link failure-free MPTCP/SR-based architecture that increases survivability, resilience, availability and robustness of future networks. The proposed path protection and dynamic link recovery scheme achieves a minimum time to recover from a failed link and avoids link congestion in softwarized networks

Parallel architectures and runtime systems co-design for task-based programming models

The increasing parallelism levels in modern computing systems has extolled the need for a holistic vision when designing multiprocessor architectures taking in account the needs of the programming models and applications. Nowadays, system design consists of several layers on top of each other from the architecture up to the application software. Although this design allows to do a separation of concerns where it is possible to independently change layers due to a well-known interface between them, it is hampering future systems design as the Law of Moore reaches to an end. Current performance improvements on computer architecture are driven by the shrinkage of the transistor channel width, allowing faster and more power efficient chips to be made. However, technology is reaching physical limitations were the transistor size will not be able to be reduced furthermore and requires a change of paradigm in systems design. This thesis proposes to break this layered design, and advocates for a system where the architecture and the programming model runtime system are able to exchange information towards a common goal, improve performance and reduce power consumption. By making the architecture aware of runtime information such as a Task Dependency Graph (TDG) in the case of dataflow task-based programming models, it is possible to improve power consumption by exploiting the critical path of the graph. Moreover, the architecture can provide hardware support to create such a graph in order to reduce the runtime overheads and making possible the execution of fine-grained tasks to increase the available parallelism. Finally, the current status of inter-node communication primitives can be exposed to the runtime system in order to perform a more efficient communication scheduling, and also creates new opportunities of computation and communication overlap that were not possible before. An evaluation of the proposals introduced in this thesis is provided and a methodology to simulate and characterize the application behavior is also presented.El aumento del paralelismo proporcionado por los sistemas de cómputo modernos ha provocado la necesidad de una visión holística en el diseño de arquitecturas multiprocesador que tome en cuenta las necesidades de los modelos de programación y las aplicaciones. Hoy en día el diseño de los computadores consiste en diferentes capas de abstracción con una interfaz bien definida entre ellas. Las limitaciones de esta aproximación junto con el fin de la ley de Moore limitan el potencial de los futuros computadores. La mayoría de las mejoras actuales en el diseño de los computadores provienen fundamentalmente de la reducción del tamaño del canal del transistor, lo cual permite chips más rápidos y con un consumo eficiente sin apenas cambios fundamentales en el diseño de la arquitectura. Sin embargo, la tecnología actual está alcanzando limitaciones físicas donde no será posible reducir el tamaño de los transistores motivando así un cambio de paradigma en la construcción de los computadores. Esta tesis propone romper este diseño en capas y abogar por un sistema donde la arquitectura y el sistema de tiempo de ejecución del modelo de programación sean capaces de intercambiar información para alcanzar una meta común: La mejora del rendimiento y la reducción del consumo energético. Haciendo que la arquitectura sea consciente de la información disponible en el modelo de programación, como puede ser el grafo de dependencias entre tareas en los modelos de programación dataflow, es posible reducir el consumo energético explotando el camino critico del grafo. Además, la arquitectura puede proveer de soporte hardware para crear este grafo con el objetivo de reducir el overhead de construir este grado cuando la granularidad de las tareas es demasiado fina. Finalmente, el estado de las comunicaciones entre nodos puede ser expuesto al sistema de tiempo de ejecución para realizar una mejor planificación de las comunicaciones y creando nuevas oportunidades de solapamiento entre cómputo y comunicación que no eran posibles anteriormente. Esta tesis aporta una evaluación de todas estas propuestas, así como una metodología para simular y caracterizar el comportamiento de las aplicacionesPostprint (published version