Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources

TL;DR

This paper addresses the challenge of optimally placing complex event processing queries across edge and cloud resources for IoT data streams, proposing adaptive heuristics to minimize latency and energy use.

Contribution

It introduces a novel optimization problem for dynamic query placement in edge-cloud environments and proposes heuristics and rebalancing strategies to solve it efficiently.

Findings

Heuristics achieve high-quality solutions with seconds-level planning time.

Rebalancing strategies reduce makespan by 20-25%.

Methods scale to 100-1000 edge devices and VMs.

Abstract

The growing deployment of sensors as part of Internet of Things (IoT) is generating thousands of event streams. Complex Event Processing (CEP) queries offer a useful paradigm for rapid decision-making over such data sources. While often centralized in the Cloud, the deployment of capable edge devices on the field motivates the need for cooperative event analytics that span Edge and Cloud computing. Here, we identify a novel problem of query placement on edge and Cloud resources for dynamically arriving and departing analytic dataflows. We define this as an optimization problem to minimize the total makespan for all event analytics, while meeting energy and compute constraints of the resources. We propose 4 adaptive heuristics and 3 rebalancing strategies for such dynamic dataflows, and validate them using detailed simulations for 100 - 1000 edge devices and VMs. The results show that our heuristics offer O(seconds) planning time, give a valid and high quality solution in all cases, and reduce the number of query migrations. Furthermore, rebalance strategies when applied in these heuristics have significantly reduced the makespan by around 20 - 25%.