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

随着三层IoT-Edge-Cloud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演变为一个成熟的生态系统，云正在将其覆盖范围扩展到全球每一个角落。这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，用户将能够实时地在他/她的指尖“感觉”世界的任何地方，甚至整个世界。预计在资源需求以及社会、经济和政治意义方面，涉及多达数十亿个用于同时感知的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。按照时间危急程度的降序，显著的例子是地震检测和警报、儿童/患者/物体跟踪和识别、用于病毒爆发检测的全国/全球范围的发烧/症状图、环境和公用事业监测以及用于业务分析的人群外包。显然，要实现这种具有不同响应时间要求的iSaaS服务，在技术上称为服务级别目标(SLO)，必须建立一个通用的协调平台，该平台可以协调资源分配并为此类服务提供SLO保证，涵盖整个IoT-Edge-Cloud生态系统。本项目旨在开发这样一个平台，称为基于物联网-边缘-云层次结构的大规模传感服务的请求-SLO感知资源编排(STTCH)。Stitch采取的方法促进了基础分析在指导强大的复杂系统设计方面的作用，并为新的云服务提供了推动者。它将有助于促进学术界和工业界在设计合理的大规模计算系统方面的合作。通过现有的ff课程和REU计划，未被充分代表的少数族裔和女性学生以及西班牙裔服务机构UTA的残疾学生参与这项研究，将有助于增强未来美国劳动力的竞争力。STITCH是一个两级分布式资源协调平台，即云到边缘集群级别和边缘集群到边缘和物联网级别。SINTCH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转换为该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转换为边缘群集级任务响应时间预算，以用于将被分派到不同边缘群集的请求的感测任务。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换为任务进一步分派到的各个边缘和物联网设备上的任务排队截止日期。该解决方案使得Stich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EDGE和IoT设备，同时提供请求SLO保证。拟议的研究解决了实现iSaaS服务规模化的关键挑战。首先，与现有iSaaS编排平台大多以资源为中心不同，Stitch是一个以用户为中心的整体解决方案，为任何iSaaS服务的个性化用户请求提供SLO保证。其次，不同于现有的云中请求/作业调度直接涉及边缘和物联网资源分配的解决方案，云中的请求调度纯粹由高层用户需求驱动，将任务资源分配留给各个边缘集群来处理。这种关注点的分离使缝合具有高度的可扩展性，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，应用基本原则和数学推理来应对实际重要的挑战。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.