Application-Aware Resource Allocation and Data Management for MEC-assisted IoT Service Providers

TL;DR

This paper proposes a MEC-based architecture and a heuristic resource allocation algorithm tailored for IoT services with diverse QoS needs, addressing data management and service placement challenges in wide-area, data-intensive IoT applications.

Contribution

It introduces a novel MEC-compliant platform supporting multiple IoT providers, with a data-aware service placement model and a heuristic algorithm for efficient resource allocation.

Findings

The proposed approach improves resource utilization under non-uniform traffic demands.

The architecture enables sharing data caches among IoT services with overlapping areas.

Simulation results show enhanced performance in delay-sensitive IoT scenarios.

Abstract

To support the growing demand for data-intensive and low-latency IoT applications, Multi-Access Edge Computing (MEC) is emerging as an effective edge-computing approach enabling the execution of delay-sensitive processing tasks close to end-users. However, most of the existing works on resource allocation and service placement in MEC systems overlook the unique characteristics of new IoT use cases. For instance, many IoT applications require the periodic execution of computing tasks on real-time data streams that originate from devices dispersed over a wide area. Thus, users requesting IoT services are typically distant from the data producers. To fill this gap, the contribution of this work is two-fold. Firstly, we propose a MEC-compliant architectural solution to support the operation of multiple IoT service providers over a common MEC platform deployment, which enables the steering and shaping of IoT data transport within the platform. Secondly, we model the problem of service placement and data management in the proposed MEC-based solution taking into account the dependencies at the data level between IoT services and sensing resources. Our model also considers that caches can be deployed on MEC hosts, to allow the sharing of the same data between different IoT services with overlapping geographical scope, and provides support for IoT services with heterogeneous QoS requirements, such as different frequencies of periodic task execution. Due to the complexity of the optimisation problem, a heuristic algorithm is proposed using linear relaxation and rounding techniques. Extensive simulation results demonstrate the efficiency of the proposed approach, especially when traffic demands generated by the service requests are not uniform.