Placing it right!: optimizing energy, processing, and transport in Edge-Fog clouds

Mohan, Nitinder; Kangasharju, Jussi

doi:10.1007/s12243-018-0649-0

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…We assume a hierarchical edge architecture where compute and caching capabilities of EN increase with increasing distance from the MD. Nodes in different (logical) layers of the edge network can be accessed via ad-hoc connections or gateways [33,56,60,65,67,83].…”

Section: System Overviewmentioning

confidence: 99%

Nimbus: Towards Latency-Energy Efficient Task Offloading for AR Services

Cozzolino

Tonetto

Mohan

et al. 2023

IEEE Trans. Cloud Comput.

Self Cite

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Widespread adoption of mobile augmented reality (AR) and virtual reality (VR) applications depends on their smoothness and immersiveness. Modern AR applications applying computationally intensive computer vision algorithms can burden today's mobile devices, and cause high energy consumption and/or poor performance. To tackle this challenge, it is possible to offload part of the computation to nearby devices at the edge. However, this calls for smart task placement strategies in order to efficiently use the resources of the edge infrastructure. In this paper, we introduce Nimbus -a task placement and offloading solution for a multi-tier, edge-cloud infrastructure where deep learning tasks are extracted from the AR application pipeline and offloaded to nearby GPU-powered edge devices. Our aim is to minimize the latency experienced by end-users and the energy costs on mobile devices. Our multifaceted evaluation, based on benchmarked performance of AR tasks, shows the efficacy of our solution. Overall, Nimbus reduces the task latency by ∼4× and the energy consumption by ∼77% for real-time object detection in AR applications. We also benchmark three variants of our offloading algorithm, disclosing the trade-off of centralized versus distributed execution.

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Section: System Overviewmentioning

confidence: 99%

Nimbus: Towards Latency-Energy Efficient Task Offloading for AR Services

Cozzolino

Tonetto

Mohan

et al. 2023

IEEE Trans. Cloud Comput.

Self Cite

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show abstract

“…In another study, researchers in Reference 79 used a decentralized three‐tier architecture to manage computable, extensive, distributable data such as the produced ones by the IoT. Accordingly, they suggested an LPCF method to reduce network costs while improving processing tasks.…”

Section: Energy Management In Fog Computingmentioning

confidence: 99%

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Hashemi,

Sahafi,

Rahmani

et al. 2023

Softw Pract Exp

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The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.

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“…However, unlike siloed deployment of cloud datacenters, setting up edge infrastructures requires significant investment and planning as the compute fabric co-exists alongside its users, e.g., in base stations, city-owned properties, public transport, etc. [4], [8]- [11]. Furthermore, the benefits of edge computing are only apparent when there is a dense availability of computing resources [1], [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Oakestra white paper: An Orchestrator for Edge Computing

Bartolomeo¹,

Yosofie²,

Bäurle³

et al. 2022

Preprint

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Edge computing seeks to enable applications with strict latency requirements by utilizing compute resources deployed closer to the users. The diverse, dynamic, and constrained nature of edge infrastructures necessitates a flexible orchestration framework that dynamically supports application QoS requirements. However, existing state-of-the-art orchestration platforms were designed for datacenter environments and make strict assumptions about underlying infrastructures that do not hold for edge computing. This work proposes a novel hierarchical orchestration framework specifically designed for supporting service operation over edge infrastructures. Through its novel federated cluster management, delegated task scheduling, and semantic overlay networking, our system can flexibly consolidate multiple infrastructure operators and absorb dynamic variations at the edge. We comprehensively evaluate our proof-of-concept implementation -Oakestra -against state-of-the-art solutions in both controlled and realistic testbeds and demonstrate the significant benefits of our approach as we achieve ≈ 10× and 30% reduction in CPU and memory consumption, respectively.

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Placing it right!: optimizing energy, processing, and transport in Edge-Fog clouds

Cited by 10 publications

References 26 publications

Nimbus: Towards Latency-Energy Efficient Task Offloading for AR Services

Nimbus: Towards Latency-Energy Efficient Task Offloading for AR Services

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Oakestra white paper: An Orchestrator for Edge Computing

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