Tommaso Stecconi, Donato Francesco Falcone, et al.
MRS Spring Meeting 2023
Multi-Access Edge Computing (MEC) is increasingly growing in prominence as the de facto enabler for ultra-low latency access to services. MEC averts the high network latencies often encountered in accessing cloud services by deploying application instances on edge servers situated near Internet-of-Things (IoT) device users. Workloads generated by IoT devices can then either be executed locally on the devices or offloaded to the MEC servers. A key cornerstone of the MEC environment is a service placement policy that determines the deployment of services on MEC servers. A service placement policy plays a critical role towards determining the trade-offs involved between latency experienced by users as a function of the resource contention and the resulting energy consumption. In this context, we propose a static-dynamic service placement policy for MEC. The static policy is geared towards placement of services in a prioritised order by leveraging Probabilistic Model Checking, a Formal Methods technique, to ensure probabilistic guarantees on the trade-offs between latencies and energy consumption of edge sites. The dynamic policy alters the static service allocation to cater to runtime variability in latency requirements. We present experimental results on a real-world service usage dataset to show the benefits of our approach over conventional approaches.
Tommaso Stecconi, Donato Francesco Falcone, et al.
MRS Spring Meeting 2023
Ana Stanojevic, Stanisław Woźniak, et al.
Nature Communications
Jose Manuel Bernabe' Murcia, Eduardo Canovas Martinez, et al.
MobiSec 2024
Rodrigo Ordonez-Hurtado, Bo Wen, et al.
ICDH 2023