SHF: SMALL: STITCH: Request-SLO-Aware Orchestration for Large-scale Sensing Services over IoT-Edge-Cloud Hierarchy

SHF：SMALL：STITCH：基于 IoT-边缘-云层次结构的大规模传感服务的请求 SLO 感知编排

• 批准号: 2008835
• 负责人: Hong Jiang
• 金额: $ 49.97万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008835&HistoricalAwards=false
• 关键词: SHF; SMALL; STITCH; Request; SLO

As the three-tier IoT-Edge-Cloud hierarchy is evolving into a mature ecosystem in terms of its social and geographical scale, and sensing, computing, and storage capabilities, the cloud is expanding its reach to every corner of the globe. This gives rise to the opportunity of developing a whole new category of cloud services, known as IoT-based Sensing-as-a-Service (ISaaS). With ISaaS, a user would be able to ”sense” any part of the world or even the entire world at his/her fingertip in real time. It is expected that large-scale ISaaS services involving up to billions of edge and IoT devices for simultaneous sensing will emerge as a dominant category of cloud services, in terms of resource demand, and social, economic, and political significance. Notable examples, in decreasing order of time criticality, are earthquake detection and alert, child/patient/object tracking and identification, nation-/world-wide fever/symptom mapping for virus breakout detection, environment and utility monitoring, and crowd sourcing for business analytics. Obviously, to enable such w¬ide range of ISaaS services with diverse response time requirements, technically known as service level objectives (SLOs), a common orchestration platform that can coordinate resource allocation among and provide an SLO guarantee for such services, encompassing the entire IoT-Edge-Cloud ecosystem, must be in place. This project aims at developing such a platform, called requeST-SLO-aware resource orchestration for large-scale sensing services over IoT-edge-Cloud Hierarchy (STITCH). The approach taken by STITCH promotes fundamental analysis in guiding the design of robust complex systems and provides enablers of new cloud services. It will help foster collaboration between academia and industry in designing effective large-scale computing systems. The involvement of underrepresented minority and women students, and students with disability at UTA, a Hispanics Serving Institution, in this research through existing course offerings and a REU program will help enhance the competitiveness of the future US workforce.STITCH is a two-level distributed resource orchestration platform, i.e., the cloud-to-edge-cluster level and the edge-cluster-to-edge-and-IoT level. At the core of STITCH is the development of a novel decomposition technique that can translate a given request SLO into distributed task performance budgets for all the sensing tasks of the request. More specifically, in the cloud, the decomposition technique translates the request SLO for a given ISaaS service into edge-cluster-level task response-time budgets for the sensing tasks of the request to be dispatched to different edge clusters. In turn, at the edge cluster level, the decomposition technique translates the edge-cluster-level task response-time budgets for each task into task queuing deadlines at individual edge and IoT devices the task is further dispatched to. This solution makes it possible for STITCH to schedule the sensing tasks for sensing in a fully distributed manner, so that it can scale up to billions of edge and IoT devices, while providing request SLO guarantee. The proposed research addresses key challenges to enable ISaaS services at scale. First, unlike the existing ISaaS orchestration platforms that are mostly resource centric, STITCH is a holistic, user-centric solution that provides SLO guarantee for individual user request of any ISaaS service. Second, unlike the existing solutions where request/job scheduling in the cloud is directly concerned with the edge-and-IoT resource allocation, in STITCH, request scheduling in the cloud is purely driven by the high-level user requirements, leaving the task resource allocation to be handled by the individual edge clusters. This separation of concerns makes the STITCH highly scalable and the autonomous control at the edge possible. Finally, the approach taken is foundational, applying fundamental principles and mathematical reasoning to address challenges of practical importance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着三层物联网-边缘-云层次结构在其社会和地理规模以及传感、计算和存储功能方面逐渐发展成为一个成熟的生态系统，云正在将其覆盖范围扩展到地球仪的每个角落。这就带来了开发一种全新的云服务类别的机会，称为基于物联网的感知即服务（ISaas）。有了ISaas，用户将能够在真实的时间里在他/她的指尖"感知"世界的任何部分甚至整个世界。预计涉及数十亿边缘和物联网设备的大规模ISaas服务将在资源需求以及社会，经济和政治意义方面成为云服务的主导类别。值得注意的例子，按时间关键性的降序排列，是地震检测和警报，儿童/病人/物体跟踪和识别，用于病毒爆发检测的全国/世界范围的发烧/症状映射，环境和公用事业监测，以及用于业务分析的众包。显然，为了实现具有不同响应时间要求的广泛的ISaas服务，技术上称为服务水平目标（SLO），必须建立一个通用的编排平台，该平台可以协调资源分配并为这些服务提供SLO保证，包括整个IoT边缘云生态系统。该项目旨在开发这样一个平台，称为基于物联网边缘云层次结构（STITCH）的大规模传感服务的requeST-SLO感知资源编排。STITCH所采取的方法促进了基础分析，以指导设计强大的复杂系统，并提供了新的云服务的推动者。它将有助于促进学术界和工业界在设计有效的大规模计算系统方面的合作。通过现有的学习课程和REU项目，让UTA的少数民族学生、女性学生和残疾学生参与这项研究，将有助于提高美国未来劳动力的竞争力。STITCH是一个两级分布式资源编排平台，即：云到边缘集群级别和边缘集群到边缘和物联网级别。 STITCH的核心是开发一种新的分解技术，该技术可以将给定的请求SLO转换为请求的所有感测任务的分布式任务性能预算。 更具体地说，在云中，分解技术将给定ISaas服务的请求SLO转换为边缘集群级任务响应时间预算，用于将请求的感测任务分派到不同的边缘集群。反过来，在边缘集群级别，分解技术将每个任务的边缘集群级别任务响应时间预算转换为任务进一步调度到的各个边缘和IoT设备的任务排队截止日期。该解决方案使STITCH能够以完全分布式的方式调度感测任务，从而可以扩展到数十亿个边缘和物联网设备，同时提供请求SLO保证。 拟议的研究解决了大规模实现ISaas服务的关键挑战。首先，与大多以资源为中心的现有ISaas编排平台不同，STITCH是一个整体的、以用户为中心的解决方案，为任何ISaas服务的单个用户请求提供SLO保证。其次，与现有的解决方案不同，云中的请求/作业调度直接与边缘和物联网资源分配有关，在STITCH中，云中的请求调度纯粹由高级用户需求驱动，任务资源分配由各个边缘集群处理。这种关注点的分离使得STITCH具有高度可扩展性，并使边缘的自主控制成为可能。 最后，所采取的方法是基础性的，应用基本原理和数学推理来解决具有实际意义的挑战。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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A price-aware congestion control protocol for cloud services

云服务的价格感知拥塞控制协议

• DOI: 10.1186/s13677-021-00271-5
• 发表时间: 2021
• 期刊: Journal of Cloud Computing
• 作者: Sun, Xiaocui;Wang, Zhijun;Wu, Yunxiang;Che, Hao;Jiang, Hong
• 通讯作者: Jiang, Hong

Improving scalability of database systems by reshaping user parallel I/O

• DOI: 10.1145/3492321.3519570
• 发表时间: 2022-03
• 期刊: Proceedings of the Seventeenth European Conference on Computer Systems
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JADE: Tail-Latency-SLO-Aware Job Scheduling for Sensing-as-a-Service

• DOI: 10.1109/ucc48980.2020.00058
• 发表时间: 2020-12
• 期刊: 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)
• 作者: Stoddard Rosenkrantz;Huiyang Li;Prathyusha Enganti;Zhongwei Li;Lin Sun;Zhijun Wang;Hao Che

TailGuard: Tail Latency SLO Guaranteed Task Scheduling for Data-Intensive User-Facing Applications

• DOI: 10.1109/icdcs57875.2023.00042
• 发表时间: 2023-07
• 期刊: 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS)
• 作者: Zhijun Wang;Huiyang Li;Lin Sun;Todd Rosenkrantz;Hao Che;Hong Jiang