DSSP: A Distributed, SLO-aware, Sensing-domain-privacy-Preserving Architecture for Sensing-as-a-Service

TL;DR

DSSP introduces a scalable, privacy-preserving architecture for Sensing-as-a-Service that guarantees per-query latency SLOs and enables multi-buyer sensing data marketplaces through distributed query scheduling.

Contribution

DSSP's novel budget decomposition technique enables independent resource allocation at IADs, ensuring latency guarantees and supporting multi-buyer data sharing in a distributed sensing architecture.

Findings

DSSP achieves query tail-latency SLO guarantees.

The architecture improves sensing coverage and privacy.

DSSP demonstrates scalability through experiments and simulations.

Abstract

In this paper, we propose DSSP, a Distributed, SLO-aware, Sensing-domain-privacy-Preserving architecture for Sensing-as-a-Service (SaS). DSSP addresses four major limitations of the current SaS architecture. First, to improve sensing quality and enhance geographic coverage, DSSP allows Independent sensing Administrative Domains (IADs) to participate in sensing services, while preserving the autonomy of control and privacy for individual domains. Second, DSSP enables a marketplace in which a sensing data seller (i.e., an IAD) can sell its sensing data to more than one buyer (i.e., cloud service provider (CSP)), rather than being locked in with just one CSP. Third, DSSP enables per-query tail-latency service-level-objective (SLO) guaranteed SaS. Fourth, DSSP enables distributed, rather than centralized, query scheduling, making SaS highly scalable. At the core of DSSP is the design of a budget decomposition technique that translates: (a) a query tail-latency SLO into exact task response time budgets for sensing tasks of the query dispatched to individual IADs; and (b) the task budget for a task arrived at an IAD into exact subtask queuing deadlines for subtasks of the task dispatched to individual edge nodes in each IAD. This enables IADs to allocate their internal resources independently and accurately to meet the task budgets and hence, query tail-latency SLO, based on a simple subtask-budget-aware earliest-deadline-first queuing (EDFQ) policy for all the subtasks. The performance and scalability of DSSP are evaluated and verified by both on-campus testbed experiment at small scale and simulation at large scale.