TrIMS: Transparent and Isolated Model Sharing for Low Latency Deep LearningInference in Function as a Service Environments

Deep neural networks (DNNs) have become core computation components within low latency Function as a Service (FaaS) prediction pipelines: including image recognition, object detection, natural language processing, speech synthesis, and personalized recommendation pipelines. Cloud computing, as the de-facto backbone of modern computing infrastructure for both enterprise and consumer applications, has to be able to handle user-defined pipelines of diverse DNN inference workloads while maintaining isolation and latency guarantees, and minimizing resource waste. The current solution for guaranteeing isolation within FaaS is suboptimal -- suffering from "cold start" latency. A major cause of such inefficiency is the need to move large amount of model data within and across servers. We propose TrIMS as a novel solution to address these issues. Our proposed solution consists of a persistent model store across the GPU, CPU, local storage, and cloud storage hierarchy, an efficient resource management layer that provides isolation, and a succinct set of application APIs and container technologies for easy and transparent integration with FaaS, Deep Learning (DL) frameworks, and user code. We demonstrate our solution by interfacing TrIMS with the Apache MXNet framework and demonstrate up to 24x speedup in latency for image classification models and up to 210x speedup for large models. We achieve up to 8x system throughput improvement.Comment: In Proceedings CLOUD 201

Improving efficiency and resilience in large-scale computing systems through analytics and data-driven management

Applications running in large-scale computing systems such as high performance computing (HPC) or cloud data centers are essential to many aspects of modern society, from weather forecasting to financial services. As the number and size of data centers increase with the growing computing demand, scalable and efficient management becomes crucial. However, data center management is a challenging task due to the complex interactions between applications, middleware, and hardware layers such as processors, network, and cooling units. This thesis claims that to improve robustness and efficiency of large-scale computing systems, significantly higher levels of automated support than what is available in today's systems are needed, and this automation should leverage the data continuously collected from various system layers. Towards this claim, we propose novel methodologies to automatically diagnose the root causes of performance and configuration problems and to improve efficiency through data-driven system management. We first propose a framework to diagnose software and hardware anomalies that cause undesired performance variations in large-scale computing systems. We show that by training machine learning models on resource usage and performance data collected from servers, our approach successfully diagnoses 98% of the injected anomalies at runtime in real-world HPC clusters with negligible computational overhead. We then introduce an analytics framework to address another major source of performance anomalies in cloud data centers: software misconfigurations. Our framework discovers and extracts configuration information from cloud instances such as containers or virtual machines. This is the first framework to provide comprehensive visibility into software configurations in multi-tenant cloud platforms, enabling systematic analysis for validating the correctness of software configurations. This thesis also contributes to the design of robust and efficient system management methods that leverage continuously monitored resource usage data. To improve performance under power constraints, we propose a workload- and cooling-aware power budgeting algorithm that distributes the available power among servers and cooling units in a data center, achieving up to 21% improvement in throughput per Watt compared to the state-of-the-art. Additionally, we design a network- and communication-aware HPC workload placement policy that reduces communication overhead by up to 30% in terms of hop-bytes compared to existing policies.2019-07-02T00:00:00

Observing the clouds : a survey and taxonomy of cloud monitoring

This research was supported by a Royal Society Industry Fellowship and an Amazon Web Services (AWS) grant. Date of Acceptance: 10/12/2014Monitoring is an important aspect of designing and maintaining large-scale systems. Cloud computing presents a unique set of challenges to monitoring including: on-demand infrastructure, unprecedented scalability, rapid elasticity and performance uncertainty. There are a wide range of monitoring tools originating from cluster and high-performance computing, grid computing and enterprise computing, as well as a series of newer bespoke tools, which have been designed exclusively for cloud monitoring. These tools express a number of common elements and designs, which address the demands of cloud monitoring to various degrees. This paper performs an exhaustive survey of contemporary monitoring tools from which we derive a taxonomy, which examines how effectively existing tools and designs meet the challenges of cloud monitoring. We conclude by examining the socio-technical aspects of monitoring, and investigate the engineering challenges and practices behind implementing monitoring strategies for cloud computing.Publisher PDFPeer reviewe

Cross-Layer Pathfinding for Off-Chip Interconnects

Off-chip interconnects for integrated circuits (ICs) today induce a diverse design space, spanning many different applications that require transmission of data at various bandwidths, latencies and link lengths. Off-chip interconnect design solutions are also variously sensitive to system performance, power and cost metrics, while also having a strong impact on these metrics. The costs associated with off-chip interconnects include die area, package (PKG) and printed circuit board (PCB) area, technology and bill of materials (BOM). Choices made regarding off-chip interconnects are fundamental to product definition, architecture, design implementation and technology enablement. Given their cross-layer impact, it is imperative that a cross-layer approach be employed to architect and analyze off-chip interconnects up front, so that a top-down design flow can comprehend the cross-layer impacts and correctly assess the system performance, power and cost tradeoffs for off-chip interconnects. Chip architects are not exposed to all the tradeoffs at the physical and circuit implementation or technology layers, and often lack the tools to accurately assess off-chip interconnects. Furthermore, the collaterals needed for a detailed analysis are often lacking when the chip is architected; these include circuit design and layout, PKG and PCB layout, and physical floorplan and implementation. To address the need for a framework that enables architects to assess the system-level impact of off-chip interconnects, this thesis presents power-area-timing (PAT) models for off-chip interconnects, optimization and planning tools with the appropriate abstraction using these PAT models, and die/PKG/PCB co-design methods that help expose the off-chip interconnect cross-layer metrics to the die/PKG/PCB design flows. Together, these models, tools and methods enable cross-layer optimization that allows for a top-down definition and exploration of the design space and helps converge on the correct off-chip interconnect implementation and technology choice. The tools presented cover off-chip memory interfaces for mobile and server products, silicon photonic interfaces, 2.5D silicon interposers and 3D through-silicon vias (TSVs). The goal of the cross-layer framework is to assess the key metrics of the interconnect (such as timing, latency, active/idle/sleep power, and area/cost) at an appropriate level of abstraction by being able to do this across layers of the design flow. In additional to signal interconnect, this thesis also explores the need for such cross-layer pathfinding for power distribution networks (PDN), where the system-on-chip (SoC) floorplan and pinmap must be optimized before the collateral layouts for PDN analysis are ready. Altogether, the developed cross-layer pathfinding methodology for off-chip interconnects enables more rapid and thorough exploration of a vast design space of off-chip parallel and serial links, inter-die and inter-chiplet links and silicon photonics. Such exploration will pave the way for off-chip interconnect technology enablement that is optimized for system needs. The basis of the framework can be extended to cover other interconnect technology as well, since it fundamentally relates to system-level metrics that are common to all off-chip interconnects

NetGlance NMS - An integrated network monitoring system

Mestrado de dupla diplomação com a Kuban State Agrarian UniversityThis work is about IT infrastructure and, in particular, computer networks in KubSAU and IPB. Also, it is about a network monitoring system “NetGlance NMS” developed for KubSAU System Administration Department. Work objective is to optimize the information structure for KubSAU and IPB. During the work, following tasks were completed: Research the existing IPB information structure, Compare the information structure for KubSAU and IPB, Model the IPB computer network (topology, services), Research bottlenecks and potential pitfalls in the data-center and in the computer network of IPB, Research information security mechanisms in the computer network of IPB, Organize monitoring process for the computer network in KubSAU. The most important impact of the work is an increasing network productivity and user experience as a result of creation and deploy a monitoring software.O trabalho descrito no âmbito desta dissertação incide sobre a infraestrutura TI e, em particular, sobre as redes de computadores da KubSAU e do IPB. Além disso, descreve-se um sistema de gestão integrada de redes, designada “NetGlance NMS”, desenvolvido para o Departamento de Administração de Sistemas da KubSAU. O objetivo do trabalho é desenvolver uma ferramenta para otimizar a gestão da estrutura de comunicações das duas instituições. Durante o trabalho, as seguintes tarefas foram concluídas: levantamento da estrutura de comunicações do IPB, comparação da estrutura de comunicações entre a KubSAU e o IPB, modelação da rede de comunicações do IPB (topologia, serviços), estudo de possíveis estrangulamentos no datacenter e na rede de comunicações doIPB, estudo de mecanismos de segurança na rede de comunicações do IPB, organização do processo de monitorização da rede de comunicações da KubSAU. O contributo mais relevante deste trabalho é o desenvolvimento de uma aplicação de gestão integrada de redes, de forma a contribuir para o aumento da produtividade da rede e da experiência dos utilizadores

Managing Distributed Cloud Applications and Infrastructure

The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities

Scalable and Distributed Resource Management Protocols for Cloud and Big Data Clusters

Cloud data centers require an operating system to manage resources and satisfy operational requirements and management objectives. The growth of popularity in cloud services causes the appearance of a new spectrum of services with sophisticated workload and resource management requirements. Also, data centers are growing by addition of various type of hardware to accommodate the ever-increasing requests of users. Nowadays a large percentage of cloud resources are executing data-intensive applications which need continuously changing workload fluctuations and specific resource management. To this end, cluster computing frameworks are shifting towards distributed resource management for better scalability and faster decision making. Such systems benefit from the parallelization of control and are resilient to failures. Throughout this thesis we investigate algorithms, protocols and techniques to address these challenges in large-scale data centers. We introduce a distributed resource management framework which consolidates virtual machine to as few servers as possible to reduce the energy consumption of data center and hence decrease the cost of cloud providers. This framework can characterize the workload of virtual machines and hence handle trade-off energy consumption and Service Level Agreement (SLA) of customers efficiently. The algorithm is highly scalable and requires low maintenance cost with dynamic workloads and it tries to minimize virtual machines migration costs. We also introduce a scalable and distributed probe-based scheduling algorithm for Big data analytics frameworks. This algorithm can efficiently address the problem job heterogeneity in workloads that has appeared after increasing the level of parallelism in jobs. The algorithm is massively scalable and can reduce significantly average job completion times in comparison with the-state of-the-art. Finally, we propose a probabilistic fault-tolerance technique as part of the scheduling algorithm