Identifying Common Periodicities in Mobile Service Demands with Spectral Analysis

Márquez, Cristina; Gramaglia, Marco; Fiore, Marco; Banchs, Albert; Smoreda, Zbigniew

doi:10.1109/medcomnet49392.2020.9191477

Cited by 7 publications

(7 citation statements)

References 28 publications

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“…We simulate one regular week using synthetic traffic patterns obtained from [44], which emulate the behavior of real RANs at scale (see [44] for details). To simplify the analysis, in the following we focus on a 24-hour period during weekdays-with the traffic profile (relative to the capacity of the system) shown in Fig.…”

Section: R I S E Do F5 4s T a G E S )O Fo U Rs Y N T H E T I C C O N mentioning

confidence: 99%

vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs

Ayala‐Romero¹,

García‐Saavedra²,

Gramaglia³

et al. 2021

IEEE Trans. on Mobile Comput.

Self Cite

116

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The virtualization of radio access networks (vRAN) is the last milestone in the NFV revolution. However, the complex relationship between computing and radio dynamics make vRAN resource control particularly daunting. We present vrAIn, a resource orchestrator for vRANs based on deep reinforcement learning. First, we use an autoencoder to project high-dimensional context data (traffic and channel quality patterns) into a latent representation. Then, we use a deep deterministic policy gradient (DDPG) algorithm based on an actor-critic neural network structure and a classifier to map contexts into resource control decisions. We have evaluated vrAIn experimentally, using an open-source LTE stack over different platforms, and via simulations over a production RAN. Our results show that: (i) vrAIn provides savings in computing capacity of up to 30% over CPU-agnostic methods; (ii) it improves the probability of meeting QoS targets by 25% over static policies; (iii) upon computing capacity under-provisioning, vrAIn improves throughput by 25% over state-of-the-art schemes; and (iv) it performs close to an optimal offline oracle. To our knowledge, this is the first work that thoroughly studies the computational behavior of vRANs and the first approach to a model-free solution that does not need to assume any particular platform or context.

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Section: R I S E Do F5 4s T a G E S )O Fo U Rs Y N T H E T I C C O N mentioning

confidence: 99%

vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs

Ayala‐Romero¹,

García‐Saavedra²,

Gramaglia³

et al. 2021

IEEE Trans. on Mobile Comput.

Self Cite

116

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“…Next, we evaluate the performance of BP-vRAN and SBP-vRAN using a realistic one-day traffic pattern from [67] (Fig. 11 (well below the demand).…”

Section: Performance In Real Network Contextsmentioning

confidence: 99%

Orchestrating Energy-Efficient vRANs: Bayesian Learning and Experimental Results

Ayala‐Romero

García‐Saavedra²,

Costa‐Pérez

et al. 2023

IEEE Trans. on Mobile Comput.

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Virtualized base stations (vBS) can be implemented in diverse commodity platforms and are expected to bring unprecedented operational flexibility and cost efficiency to the next generation of cellular networks. However, their widespread adoption is hampered by their complex configuration options that affect in a non-traditional fashion both their performance and their power consumption. Following an in-depth experimental analysis in a bespoke testbed, we characterize the vBS power consumption profile and reveal previously unknown couplings between their various control knobs. Motivated by these findings, we develop a Bayesian learning framework for the orchestration of vBSs and design two novel algorithms: (i) BP-vRAN, which employs online learning to balance the vBS performance and energy consumption, and (ii) SBP-vRAN, which augments our optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, i.e., converge an order of magnitude faster than state-of-the-art Deep Reinforcement Learning methods, and achieve optimal performance. We demonstrate the efficacy of these solutions in an experimental prototype using real traffic traces.

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“…Next, we evaluate the performance of BP-vRAN and SBP-vRAN using a realistic one-day traffic pattern from [55] (Fig. 10, top).…”

Section: Performance In Real Network Contextsmentioning

confidence: 99%

Bayesian Online Learning for Energy-Aware Resource Orchestration in Virtualized RANs

Ayala‐Romero

García‐Saavedra

Costa‐Pérez

et al. 2021

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications

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Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We perform an in-depth experimental analysis of the energy consumption of virtualized Base Stations (vBSs) and render two conclusions: (i) characterizing performance and power consumption is intricate as it depends on human behavior such as network load or user mobility; and (ii) there are many control policies and some of them have non-linear and monotonic relations with power and throughput. Driven by our experimental insights, we argue that machine learning holds the key for vBS control. We formulate two problems and two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient and have provably performance, which is paramount for carriergrade vRANs. We demonstrate the convergence and flexibility of our approach and assess its performance using an experimental prototype.

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Identifying Common Periodicities in Mobile Service Demands with Spectral Analysis

Cited by 7 publications

References 28 publications

vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs

vrAIn: Deep Learning based Orchestration for Computing and Radio Resources in vRANs

Orchestrating Energy-Efficient vRANs: Bayesian Learning and Experimental Results

Bayesian Online Learning for Energy-Aware Resource Orchestration in Virtualized RANs

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