Pure Edge Computing Platform for the Future Internet

D'Angelo, Mirko; Caporuscio, Mauro

doi:10.1007/978-3-319-50230-4_36

Cited by 6 publications

(1 citation statement)

References 21 publications

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“…This increased use of technology has led to a higher demand for computational resources that are highly available, which, in turn, results in an increase in the number of data centers at the edge of the network, significantly amplifying CO 2 emissions and server maintenance costs. To propose more sustainable solutions in computing, it is necessary to shift towards Extreme Edge Computing (EEC) [4], [5], tak-ing advantage of the massive computing resources available at Extreme Edge Devices (EED) [6] such as laptops, PCs, tablets, smartphones, and connected vehicles, rather than increasing the number of high-capacity servers at the edge of the network. EEC offers computing services much closer to end-users, which can reduce delays and improve the quality of service.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Deep Reinforcement Learning for Efficient Workload Orchestration in Extreme Edge Computing

Safavifar,

Gyamfi,

Mangina

et al. 2024

IEEE Access

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Workload orchestration at the edge of the network has become increasingly challenging with the ever-increasing penetration of resource demanding mobile, and heterogeneous devices offering low latency services. Literature has addressed this challenge assuming the availability of multi-access Mobile Edge Computing (MEC) servers and placing the computing tasks related to such services on the MEC servers. However, to develop a more sustainable and energy-efficient computing paradigm, for applications operating in stochastic environments with unpredictable workloads, it is essential to minimize the MEC servers' usage, and utilize the available resource-constrained edge devices, to keep the resourceful servers idle for handling any unpredictable larger workload. In this paper, we proposed DEWOrch, a deep reinforcement Learning algorithm for efficient workload orchestration. DEWOrch's aim is to increase the utilization of resource-constrained edge devices and minimize resource waste for more sustainable and energy efficient computing solution. This model is evaluated in an Extreme Edge Computing environment, where no MEC servers is available and only edge devices with constrained capacity are used to perform tasks. The results show that DEWOrch outperforms the state-of-the-art methods by around 50% decrease in resource waste while improved task success rate, and decreased energy consumption per task in most scenarios.

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Section: Introductionmentioning

confidence: 99%