Future pervasive applications, like mobile augmented reality, have huge bandwidth and computation demands and very stringent delay constraints. Edge computing has been proposed to cope with such challenging requirements, since it shortens significantly the distance between the end users and the servers. On the other hand, serverless computing is emerging among cloud technologies to respond to the need of highly scalable event-driven execution of stateless tasks. In this paper, we investigate the convergence of the two to enable very low-latency execution of short-lived stateless tasks whose computation is offloaded from the user terminal to servers hosted by or close to edge devices in mobile pervasive environments. We realized a proof-of-concept implementation to delve into the specific issue of efficient dispatching of tasks in a distributed manner to achieve high scalability. We evaluated our proposed algorithm with experiments in a large-scale emulated network environment, showing that our solution achieves similar or better delay performance than a centralized solution, with far less network utilization.
Low-latency distributed computation offloading for pervasive environments
Cicconetti C;Conti M;Passarella A
2019
Abstract
Future pervasive applications, like mobile augmented reality, have huge bandwidth and computation demands and very stringent delay constraints. Edge computing has been proposed to cope with such challenging requirements, since it shortens significantly the distance between the end users and the servers. On the other hand, serverless computing is emerging among cloud technologies to respond to the need of highly scalable event-driven execution of stateless tasks. In this paper, we investigate the convergence of the two to enable very low-latency execution of short-lived stateless tasks whose computation is offloaded from the user terminal to servers hosted by or close to edge devices in mobile pervasive environments. We realized a proof-of-concept implementation to delve into the specific issue of efficient dispatching of tasks in a distributed manner to achieve high scalability. We evaluated our proposed algorithm with experiments in a large-scale emulated network environment, showing that our solution achieves similar or better delay performance than a centralized solution, with far less network utilization.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.