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.

由于三层物联网边缘云的层次结构正在从其社会和地理规模以及敏感性，计算和存储能力方面发展成为一个成熟的生态系统，因此云将其覆盖范围扩展到了全球的每个角落。这引起了开发全新类别的云服务的机会，即基于物联网的Sensing-As-A-Service（Isaas）。借助Isaas，用户将能够“感知”世界上任何部分，甚至可以预期，在资源，社会，经济和政治意义上，大规模的ISAAS服务将涉及多达数十亿英镑和物联网设备，以使其成为简单敏感性的云层服务。值得注意的例子，以减少时间的关键顺序，是地震检测和警觉，儿童/患者/对象跟踪和识别，国家/世界范围/世界范围的发烧/症状映射，用于病毒突破检测，环境和公用事业监测，以及业务分析的人群来源。显然，要启用具有潜水员响应时间要求的WI-ide范围的ISAAS服务，从技术上讲称为服务水平对象（SLO），这是一个可以协调资源分配并为此类服务提供SLO保证的常见编排平台，必须涵盖整个Iot-Edge-Cloud Ecosystem，必须具有适当的位置。该项目旨在开发这样一个平台，称为“ Request-Slo-Awaine Resource Crestration”，用于在IoT-Edge-Cloud层次结构（Stitch）上进行大规模灵敏度服务。 Stitch采用的方法促进了基本分析，以指导健壮的复杂系统的设计，并提供新的云服务的推动者。它将有助于培养学术界与行业之间的合作，以设计有效的大规模计算系统。通过现有的课程和REU计划在UTA，西班牙裔美国人服务机构的UTA的代表性不足的少数群体和女学生以及残疾学生的参与将有助于增强未来的美国劳动力的竞争力。 Stitch的核心是开发一种新颖的分解技术，该技术可以将给定的请求SLO转化为请求的所有灵敏度任务的分布式任务绩效预算。更具体地说，在云中，分解技术将给定ISAAS服务的请求SLO转换为边缘群集级的任务响应时间预算，用于将请求的传感任务发送到不同的边缘群集。反过来，在边缘群集级别，分解技术将每个任务的边缘群集级任务响应时间预算转化为在单个边缘和IoT设备的任务排队截止日期。该解决方案使Stitch可以安排以完全分布的方式进行感测的灵敏度任务，从而使其可以扩展到数十亿优势和IoT设备，同时提供请求SLO保证。拟议的研究针对使ISAAS服务大规模提供的关键挑战。首先，与主要以资源为中心的现有ISAAS编排平台不同，Stitch是一种整体，以用户为中心的解决方案，可为任何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iot资源分配有关，在针迹中，云中的请求调度纯粹是由高级用户要求驱动的，从而使各个边缘群集处理要处理的任务资源分配。这种关注点的分离使针迹高度可扩展性和边缘的自主控制。最后，采用的方法是基本的，采用基本原则和数学推理来应对实际重要性的挑战。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估被认为是宝贵的支持。

项目成果

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
• 作者: Ning Li;Hong Jiang;Hao Che;Zhijun Wang;Minh Q. Nguyen

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

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

CurTail: Distributed Cotask Scheduling with Guaranteed Tail-Latency SLO

CurTail：具有保证尾部延迟 SLO 的分布式协同任务调度

• 发表时间: 2021
• 期刊: the seventh international conference on networking and services
• 作者: Wang, Zhijun;Che, Hao;Jiang, Hong
• 通讯作者: Jiang, Hong