MultiLibOS: an OS architecture for cloud computing

Cloud computing is resulting in fundamental changes to computing infrastructure, yet these changes have not resulted in corresponding changes to operating systems. In this paper we discuss some key changes we see in the computing infrastructure and applications of IaaS systems. We argue that these changes enable and demand a very different model of operating system. We then describe the MulitLibOS architecture we are exploring and how it helps exploit the scale and elasticity of integrated systems while still allowing for legacy software run on traditional OSes

Total order broadcast for fault tolerant exascale systems

In the process of designing a new fault tolerant run-time for future exascale systems, we discovered that a total order broadcast would be necessary. That is, nodes of a supercomputer should be able to broadcast messages to other nodes even in the face of failures. All messages should be seen in the same order at all nodes. While this is a well studied problem in distributed systems, few researchers have looked at how to perform total order broadcasts at large scales for data availability. Our experience implementing a published total order broadcast algorithm showed poor scalability at tens of nodes. In this paper we present a novel algorithm for total order broadcast which scales logarithmically in the number of processes and is not delayed by most process failures. While we are motivated by the needs of our run-time we believe this primitive is of general applicability. Total order broadcasts are used often in datacenter environments and as HPC developers begins to address fault tolerance at the application level we believe they will need similar primitives

EbbRT: Elastic Building Block Runtime - case studies

We present a new systems runtime, EbbRT, for cloud hosted applications. EbbRT takes a different approach to the role operating systems play in cloud computing. It supports stitching application functionality across nodes running commodity OSs and nodes running specialized application specific software that only execute what is necessary to accelerate core functions of the application. In doing so, it allows tradeoffs between efficiency, developer productivity, and exploitation of elasticity and scale. EbbRT, as a software model, is a framework for constructing applications as collections of standard application software and Elastic Building Blocks (Ebbs). Elastic Building Blocks are components that encapsulate runtime software objects and are implemented to exploit the raw access, scale and elasticity of IaaS resources to accelerate critical application functionality. This paper presents the EbbRT architecture, our prototype and experimental evaluation of the prototype under three different application scenarios

EbbRT: Elastic Building Block Runtime - overview

EbbRT provides a lightweight runtime that enables the construction of reusable, low-level system software which can integrate with existing, general purpose systems. It achieves this by providing a library that can be linked into a process on an existing OS, and as a small library OS that can be booted directly on an IaaS node

EbbRT: a customizable operating system for cloud applications

Efficient use of hardware requires operating system components be customized to the application workload. Our general purpose operating systems are ill-suited for this task. We present Genesis, a new operating system that enables per-application customizations for cloud applications. Genesis achieves this through a novel heterogeneous distributed structure, a partitioned object model, and an event-driven execution environment. This paper describes the design and prototype implementation of Genesis, and evaluates its ability to improve the performance of common cloud applications. The evaluation of the Genesis prototype demonstrates memcached, run within a VM, can outperform memcached run on an unvirtualized Linux. The prototype evaluation also demonstrates an 14% performance improvement of a V8 JavaScript engine benchmark, and a node.js webserver that achieves a 50% reduction in 99th percentile latency compared to it run on Linux

EbbRT: a framework for building per-application library operating systems

Efficient use of high speed hardware requires operating system components be customized to the application work- load. Our general purpose operating systems are ill-suited for this task. We present EbbRT, a framework for constructing per-application library operating systems for cloud applications. The primary objective of EbbRT is to enable high-performance in a tractable and maintainable fashion. This paper describes the design and implementation of EbbRT, and evaluates its ability to improve the performance of common cloud applications. The evaluation of the EbbRT prototype demonstrates memcached, run within a VM, can outperform memcached run on an unvirtualized Linux. The prototype evaluation also demonstrates an 14% performance improvement of a V8 JavaScript engine benchmark, and a node.js webserver that achieves a 50% reduction in 99th percentile latency compared to it run on Linux

SEUSS: rapid serverless deployment using environment snapshots

Modern FaaS systems perform well in the case of repeat executions when function working sets stay small. However, these platforms are less effective when applied to more complex, large-scale and dynamic workloads. In this paper, we introduce SEUSS (serverless execution via unikernel snapshot stacks), a new system-level approach for rapidly deploying serverless functions. Through our approach, we demonstrate orders of magnitude improvements in function start times and cacheability, which improves common re-execution paths while also unlocking previously-unsupported large-scale bursty workloads.Published versio

Parallelization by Simulated Tunneling

As highly parallel heterogeneous computers become commonplace, automatic parallelization of software is an increasingly critical unsolved problem. Continued progress on this problem will require large quantities of information about the runtime structure of sequential programs to be stored and reasoned about. Manually formalizing all this information through traditional approaches, which rely on semantic analysis at the language or instruction level, has historically proved challenging. We take a lower level approach, eschewing semantic analysis and instead modeling von Neumann computation as a dynamical system, i.e., a state space and an evolution rule, which gives a natural way to use probabilistic inference to automatically learn powerful representations of this information. This model enables a promising new approach to automatic parallelization, in which probability distributions empirically learned over the state space are used to guide speculative solvers. We describe a prototype virtual machine that uses this model of computation to automatically achieve linear speedups for an important class of deterministic, sequential Intel binary programs through statistical machine learning and a speculative, generalized form of memoization.Engineering and Applied Science

Scalable elastic systems architecture

Cloud computing has spurred the exploration and exploitation of elastic access to large scales of computing. To date the predominate building blocks by which elasticity has been exploited are applications and operating systems that are built around traditional computing infrastructure and programming models that are in-elastic or at best coarsely elastic. What would happen if application themselves could express and exploit elasticity in a fine grain fashion and this elasticity could be efficiently mapped to the scale and elasticity offered by modern cloud hardware systems? Would economic and market models that exploit elasticity pervade even the lowest levels? And would this enable greater efficiency both globally and individually? Would novel approaches to traditional problems such as quality of service arise? Would new applications be enabled both technically and economically? How to construct scalable and elastic software is an open challenge. Our work explores a systematic method for constructing and deploying such software. Building on several years of prior research, we will develop and evaluate a new cloud computing systems software architecture that addresses both scalability and elasticity. We explore a combination of a novel programming model and alternative operating systems structure. The goal of the architecture is to enable applications that inherently can scale up or down to react to changes in demand. We hypothesize that enabling such fine-grain elastic applications will open up new avenues for exploring both supply and demand elasticity across a broad range of research areas such as economic models, optimization, mechanism design, software engineering, networking and others.Department of Energy Office of Science (DE-SC0005365), National Science Foundation (1012798

Transistor scaled HPC application performance

We propose a radically new, biologically inspired, model of extreme scale computer on which ap- plication performance automatically scales with the transistor count even in the face of component failures. Today high performance computers are massively parallel systems composed of potentially hundreds of thousands of traditional processor cores, formed from trillions of transistors, consuming megawatts of power. Unfortunately, increasing the number of cores in a system, unlike increasing clock frequencies, does not automatically translate to application level improvements. No general auto-parallelization techniques or tools exist for HPC systems. To obtain application improvements, HPC application programmers must manually cope with the challenge of multicore programming and the significant drop in reliability associated with the sheer number of transistors. Drawing on biological inspiration, the basic premise behind this work is that computation can be dramatically accelerated by integrating a very large-scale, system-wide, predictive associative memory into the operation of the computer. The memory effectively turns computation into a form of pattern recognition and prediction whose result can be used to avoid significant fractions of computation. To be effective the expectation is that the memory will require billions of concurrent devices akin to biological cortical systems, where each device implements a small amount of storage, computation and localized communication. As typified by the recent announcement of the Lyric GP5 Probability Processor, very efficient scalable hardware for pattern recognition and prediction are on the horizon. One class of such devices, called neuromorphic, was pioneered by Carver Mead in the 80’s to provide a path for breaking the power, scaling, and reliability barriers associated with standard digital VLSI tech- nology. Recent neuromorphic research examples include work at Stanford, MIT, and the DARPA Sponsored SyNAPSE Project. These devices operate transistors as unclocked analog devices orga- nized to implement pattern recognition and prediction several orders of magnitude more efficiently than functionally equivalent digital counterparts. Abstractly, the devices can be used to implement modern machine learning or statistical inference. When exposed to data as a time-varying signal, the devices learn and store patterns in the data at multiple time scales and constantly provide predictions about what the signal will do in the future. This kind of function can be seen as a form of predictive associative memory. In this paper we describe our model and initial plans for exploring it.Department of Energy Office of Science (DE-SC0005365), National Science Foundation (1012798