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.

随着物联网-边缘-云三层层次结构在社会和地理规模以及传感、计算和存储能力方面正在演变成一个成熟的生态系统，云正在将其覆盖范围扩展到全球的每个角落。一个全新的云服务类别的机会，称为基于物联网的传感即服务（ISaaS），通过 ISaaS，用户将能够“感知”世界上任何发展中的地区，甚至是整个世界。整个世界在预计涉及多达数十亿边缘和物联网设备的大规模 ISaaS 服务将成为云服务的主导类别，从资源需求以及社会、经济、值得注意的例子（按照时间紧迫性从高到低的顺序）是地震检测和警报、儿童/患者/物体跟踪和识别、用于病毒爆发检测的国家/全球发烧/症状绘图、环境和公用事业监测以及众包显然，对于业务分析来说，要支持具有不同响应时间要求的广泛 ISaaS 服务，技术上称为服务级别目标 (SLO)，这是一个通用编排平台，可以协调此类服务之间的资源分配并为此类服务提供 SLO 保证。该项目旨在开发一个名为 requeST-SLO 感知资源编排的平台，涵盖整个 IoT-Edge-Cloud 生态系统，用于 IoT-edge-Cloud 层次结构上的大规模传感服务。 （STITCH）。STITCH 所采用的方法促进了指导稳健复杂系统设计的基础分析，并提供了新云服务的推动者，这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。 UTA（西班牙裔服务机构）的少数民族学生和女学生以及残疾学生在这项研究中通过现有的课程设置和 REU 计划将有助于提高未来美国劳动力的竞争力。STITCH 是STITCH的核心是开发一种新型分解技术，即云到边缘集群级别和边缘集群到边缘和物联网级别的两级分布式资源编排平台。将给定的请求 SLO 转换为请求的所有感知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。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各个边缘的任务排队期限。该解决方案使STITCH能够以完全分布式的方式调度感知任务，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。这拟议的研究解决了大规模启用 ISaaS 服务的关键挑战，首先，与主要以资源为中心的现有 ISaaS 编排平台不同，STITCH 是一个整体的、以用户为中心的解决方案，可为任何 ISaaS 服务的个人用户请求提供 SLO 保证。与云中的请求/作业调度直接与边缘和物联网资源分配相关的现有解决方案不同，在 STITCH 中，云中的请求调度纯粹由高级用户需求驱动，将任务资源分配留给被处理这种关注点的分离使得 STITCH 具有高度可扩展性，并且可以实现边缘的自主控制。最后，所采用的方法是基础性的，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 的法定要求。使命，并通过使用基金会的智力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

PACCP: A Price-Aware Congestion Control Protocol for Datacenters

PACCP：数据中心的价格感知拥塞控制协议

• 发表时间: 2020-12
• 期刊: the 7th The Seventh IEEE/ACM International Conference on Utility and 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

JADE：用于传感即服务的尾部延迟 SLO 感知作业调度

• 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: Rosenkrantz, Stoddard;Li, Huiyang;Enganti, Prathyusha;Li, Zhongwei;Sun, Lin;Wang, Zhijun;Che, Hao;Jiang, Hong
• 通讯作者: Jiang, Hong

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

TailGuard：面向数据密集型用户的应用程序的尾部延迟 SLO 保证任务调度

• DOI: 10.1109/icdcs57875.2023.00042
• 发表时间: 2023-07-01
• 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
• 通讯作者: Hong Jiang

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: 2021-11
• 期刊: Journal of Cloud Computing
• 作者: Sun, Xiaocui;Wang, Zhijun;Wu, Yunxiang;Che, Hao;Jiang, Hong
• 通讯作者: Jiang, Hong

L oo p D e l t a : E m b e dd i n g L o c a li t y - a w a r e O pp o r t un i s t i c D e l t a C o m p r e ss i o n i n I n li n e D e dup li c a t i o n f o r Hi g h l y E ffic i e n t D a t a R e du c t i o n

Loop Delta：嵌入位置感知机会的统一 DELTA 压缩内联重复数据删除，以实现高效数据缩减n

• 发表时间: 2023-07
• 期刊: USENIX annual technical conference
• 作者: Zhang, Yucheng;Jiang, Hong;Feng, Dan;Jiang Nan;Qui, Taorong;Huang, Wei
• 通讯作者: Huang, Wei