JADE: Tail-Latency-SLO-Aware Job Scheduling for Sensing-as-a-Service

Abstract

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.