JADE: Tail-Latency-SLO-Aware Job Scheduling for Sensing-as-a-Service
JADE: Tail-Latency-SLO-Aware Job Scheduling for Sensing-as-a-Service
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DOI：
10.1109/ucc48980.2020.00058
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Stoddard Rosenkrantz;Huiyang Li;Prathyusha Enganti;Zhongwei Li;Lin Sun;Zhijun Wang;Hao Che;
中科院分区：
文献类型：
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作者：
Stoddard Rosenkrantz;Huiyang Li;Prathyusha Enganti;Zhongwei Li;Lin Sun;Zhijun Wang;Hao Che;
随着物联网-边缘-云层次结构逐渐演变为成熟的生态系统，具有严格作业服务级别目标(SLO)的大规模感知即服务(SAS)服务有望成为占主导地位的云服务。SA的可行业务模式必须通过设计本质上是多层的，并在一个联合环境中工作，该环境涉及大量可能出现在不同层的自愿利益相关者。它还必须尊重隐私和对利益相关者资源的自主控制。这就要求开发一个完全分布式的、具有SLO意识的作业资源分配和调度平台。本文提出了一种尾部延迟SLO感知的SAS作业资源分配调度平台JADE。它是一个四层平台，即云层、边缘群集层、边缘层和物联网。为了尊重不同级别的个人利益相关者的隐私和控制自主权，Jade的设计遵循了级别之间分离关注点的设计原则。其设计的核心是开发一种分解技术，将SAS服务需求，特别是作业尾部延迟SLO分解为映射到每个较低层的各个传感任务的任务性能预算。这使得有可能允许每个较低层自主地管理其自己的资源，以满足传感任务预算，从而满足SAS服务要求，同时保持其隐私和控制的自主性。最后，给出了基于模拟和JADE初始样机的初步测试结果，展示了该解决方案的前景。
As the IoT-Edge-Cloud hierarchy is evolving into a mature ecosystem, large-scale Sensing-as-a-Service (SaS) based services with stringent job service level objectives (SLOs) are expected to emerge as dominant cloud services. A viable business model for SaS must be inherently multi-tier by design and work in a confederated environment involving a large number of voluntary stakeholders who may appear at different tiers. It must also honor privacy and autonomous control of stakeholder resources. This calls for a fully distributed, SLO-aware job resource allocation and scheduling platform to be developed. In this paper, we propose a tail-latency-SLO-aware job resource allocation and scheduling platform for SaS, called JADE. It is a four-tier platform, i.e., cloud, edge cluster, edge, and IoT tiers. To honor the privacy and autonomy of control for individual stakeholders at different tiers, the JADE design follows the design principle of separation of concerns among tiers. Central to its design is to develop a decomposition technique that decomposes SaS service requirements, in particular, the job tail-latency SLO, into task performance budgets for individual sensing tasks mapped to each lower tier. This makes it possible to allow each lower tier to manage its own resources autonomously to meet the sensing task budgets and hence the SaS service requirements, while preserving its privacy and autonomy of control. Finally, preliminary testing results based on both simulation and an initial prototype of JADE are presented to demonstrate the promising prospects of the solution.