OneOS:用于边缘到云连续体的分布式操作系统

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Kumseok Jung;Julien Gascon-Samson;Sathish Gopalakrishnan;Karthik Pattabiraman
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引用次数: 0

摘要

应用程序开发人员通常需要使用诸如通信中间件和基于云的服务之类的软件组合来处理边缘到云连续体中的异构性和网络动态性的挑战。因此,开发人员在应用程序的核心业务逻辑外围编写额外的粘合代码,以提供交互软件框架之间的互操作性。每个软件框架都有自己的特定于框架的API,随着技术的发展,开发人员必须通过更新应用程序中的粘合代码来跟上API的变化。因此,特定于框架的api阻碍了互操作性并导致了技术分裂。我们提出了一种基于中间件的分布式操作系统(OS)的设计,称为OneOS,以实现减轻这种互操作性挑战的计算范式。OneOS提供分布式计算平台的单一系统映像,并通过标准POSIX API透明地提供软件组件之间的互操作性。使用OneOS的领域特定语言,用户可以从传统POSIX程序编写复杂的分布式应用程序。OneOS通过采用分布式检查点恢复算法来容忍故障。我们使用物联网流处理基准套件和视频处理应用程序,针对开源物联网平台ThingsJS评估OneOS的性能。OneOS执行程序的速度大约是ThingsJS的3倍,代码大小减少了大约22%,并且在检测到失败的应用程序后,可以在1秒内恢复其状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
OneOS: Distributed Operating System for the Edge-to-Cloud Continuum
Application developers often need to employ a combination of software such as communication middleware and cloud-based services to deal with the challenges of heterogeneity and network dynamism in the edge-to-cloud continuum. Consequently, developers write extra glue code peripheral to the application’s core business logic, to provide interoperability between interacting software frameworks. Each software framework comes with its own framework-specific API, and as technology evolves, the developer must keep up with the changing APIs by updating the glue code in their application. Thus, framework-specific APIs hinder interoperability and cause technology fragmentation. We propose a design of a middleware-based distributed operating system (OS) called OneOS to realize a computing paradigm that alleviates such interoperability challenges. OneOS provides a single system image of the distributed computing platform, and transparently provides interoperability between software components through the standard POSIX API. Using OneOS’s domain-specific language, users can compose complex distributed applications from legacy POSIX programs. OneOS tolerates failures by adopting a distributed checkpoint-restore algorithm. We evaluate the performance of OneOS against an open-source IoT Platform, ThingsJS, using an IoT stream processing benchmark suite, and a video processing application. OneOS executes the programs about 3x faster than ThingsJS, reduces the code size by about 22%, and recovers the state of failed applications within 1 s upon detecting their failure.
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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