Discontinuous Computation Offloading for Energy-Efficient Mobile Edge Computing

Merluzzi, Mattia; Pietro, Nicola di; Lorenzo, Paolo Di; Strinati, Emilio Calvanese; Barbarossa, Sergio

doi:10.1109/tgcn.2021.3125543

Cited by 17 publications

(11 citation statements)

References 52 publications

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“…2) AP's energy consumption For the AP energy consumption, we exploit the concept from [13,39], considering that a large portion of its energy is consumed only for being in active state (i.e., to switch on RF chain, power amplifiers, power supply, analog frontend, digital baseband, and digital control). Then, we assume that the AP is able to enter low power sleep operation mode to save energy, a concept known as Discontinuous Transmission (DTX).…”

Section: Energy Consumptionmentioning

confidence: 99%

“…Related works on MEC There is a wide literature on computation offloading, aimed at jointly optimizing communication and computation resources in both static and dynamic MEC scenarios [6][7][8][9][10][11][12][13]. Recent surveys on the topic appear also in [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…In [10], the authors investigate a scenario with multiple APs and edge servers, where an assignment strategy based on matching theory is proposed, coupled with the tools of Lyapunov optimization and Extreme Value Theory to control reliability. In [13], a discontinuous mobile edge computing framework is proposed to minimize the energy consumption under latency constraints, considering a holistic approach that comprises UE, APs, and ESs. In [19], a deep reinforcement learning strategy driven by Lyapunov optimization is proposed to enable stable offloading in dynamic MEC, while the work in [20] proposed a multi-task learning based feedforward neural network trained to jointly optimize the offloading decision and computational resource allocation.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic edge computing empowered by reconfigurable intelligent surfaces

Lorenzo

Merluzzi

Strinati

et al. 2022

J Wireless Com Network

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In this paper, we propose a novel algorithm for energy-efficient low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a scenario where new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic learning algorithm that jointly optimizes the allocation of radio resources (i.e., power, transmission rates, sleep mode and duty cycle), computation resources (i.e., CPU cycles), and RIS reflectivity parameters (i.e., phase shifts), while guaranteeing a target performance in terms of average end-to-end delay. The proposed strategy enables dynamic control of the system, performing a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. The presence and optimization of RISs helps boosting the performance of dynamic MEC, thanks to the capability to shape and adapt the wireless propagation environment. Numerical results assess the performance in terms of service delay, learning, and adaptation capabilities of the proposed strategy for RIS-empowered MEC.

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Section: Energy Consumptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic edge computing empowered by reconfigurable intelligent surfaces

Lorenzo

Merluzzi

Strinati

et al. 2022

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sleep modes. The work in [41] jointly considers the optimization of access points, edge servers, and user equipment for a computation offloading scenario (from end users to edge servers). Uplink and downlink transmission power, modulation and coding scheme selection, edge server CPU frequency, and duty cycles of all network elements are jointly optimized towards reducing the total energy consumption.…”

Section: Joint Resource Schedulersmentioning

confidence: 99%

Energy Efficient Deployment and Orchestration of Computing Resources at the Network Edge: a Survey on Algorithms, Trends and Open Challenges

Shalavi¹,

Perin²,

Zanella³

et al. 2022

Preprint

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Mobile networks are becoming energy hungry, and this trend is expected to continue due to a surge in communication and computation demand. Multi-access Edge Computing (MEC), a key component of 5𝐺 and 6𝐺 networks, will entail energy-consuming services and applications, with non-negligible impact in terms of ecological sustainability. In this paper, we provide a comprehensive review of existing approaches to make edge computing networks greener, including but not limited to the exploitation of renewable energy resources, and context-awareness (e.g., user mobility, or dynamics in the traffic), analyzing their pros and cons. We hence provide an updated account of recent developments on MEC from an energetic sustainability perspective, addressing the initial deployment of computing resources, their dynamic (re)allocation (resource scheduling), as well as distributed and federated learning designs. In doing so, we highlight the energy aspects of these algorithms, advocating the need for energy-sustainable edge computing systems that are aligned with Sustainable Development Goals (SDGs) and the Paris agreement. To the best of our knowledge, this is the first work providing a systematic literature review on the efficient deployment and management of energy harvesting MEC, with special focus on the deployment, provisioning, and scheduling of computing tasks, including federated learning for distributed edge intelligence, toward making edge networks greener and more sustainable. At the end of the paper, open research avenues and challenges are identified for all the surveyed topics. CCS Concepts: • Networks → Network performance evaluation; Wireless access points, base stations and infrastructure; • Computer systems organization → Embedded systems; • Computing methodologies → Planning and scheduling.

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“…Besides improving throughput and latency, the energy efficiency of edge networks has gained much attention lately. For example, reference [3] studies the whole network's energy consumption, including access points, edge servers, and user equipment for a computation offloading scenario. According to this paper, the more we push the computation from cloud servers to the network's edge, the more crucial it becomes to consider the energy consumption of the models that are being exploited by end user applications.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model

Lahmer,

Khoshsirat,

Rossi

et al. 2022

Preprint

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Recently, there has been a trend of shifting the execution of deep learning inference tasks toward the edge of the network, closer to the user, to reduce latency and preserve data privacy. At the same time, growing interest is being devoted to the energetic sustainability of machine learning. At the intersection of these trends, in this paper we focus on the energetic characterization of machine learning at the edge, which is attracting increasing attention. Unfortunately, calculating the energy consumption of a given neural network during inference is complicated by the heterogeneity of the possible underlying hardware implementation. In this work, we aim at profiling the energetic consumption of inference tasks for some modern edge nodes by deriving simple but accurate models. To this end, we performed a large number of experiments to collect the energy consumption of fully connected and convolutional layers on two well-known edge boards by NVIDIA, namely, Jetson TX2 and Xavier. From these experimental measurements, we have then distilled a simple and practical model that can provide an estimate of the energy consumption of a certain inference task on these edge computers. We believe that this model can prove useful in many contexts as, for instance, to guide the search for efficient neural network architectures, as a heuristic in neural network pruning, to find energy-efficient offloading strategies in a split computing context, or to evaluate and compare the energy performance of deep neural network architectures.

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Discontinuous Computation Offloading for Energy-Efficient Mobile Edge Computing

Cited by 17 publications

References 52 publications

Dynamic edge computing empowered by reconfigurable intelligent surfaces

Dynamic edge computing empowered by reconfigurable intelligent surfaces

Energy Efficient Deployment and Orchestration of Computing Resources at the Network Edge: a Survey on Algorithms, Trends and Open Challenges

Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model

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