Energy-Aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning

Kalntis, Michail; Iosifidis, George

doi:10.1109/globecom48099.2022.10001330

Cited by 3 publications

(3 citation statements)

References 38 publications

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“…Interestingly, the RIC policies can shape the performance and energy cost of the vRAN in two ways: (i) by assigning carefully the vBSs workloads to different PUs of O-Cloud; and (ii) by affecting the characteristics of these workloads in almost real-time. For instance, the RIC could dictate the vBSs to route their most voluminous flows to servers equipped with HAs; to refrain from using energy-costly modulation schemes [16,51]; or to bound their transmission power [15]. Such compute control and radio control policies can, in principle, be very effective in balancing the vRAN performance and energy costs, but require access to system and user parameters that are unknown and vary rapidly, and presume solving large-scale challenging optimization problems.…”

Section: Background and Motivationmentioning

confidence: 99%

Fair Resource Allocation in Virtualized O-RAN Platforms

Aslan,

Iosifidis,

Ayala-Romero

et al. 2024

Proc. ACM Meas. Anal. Comput. Syst.

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O-RAN systems and their deployment in virtualized general-purpose computing platforms (O-Cloud) constitute a paradigm shift expected to bring unprecedented performance gains. However, these architectures raise new implementation challenges and threaten to worsen the already-high energy consumption of mobile networks. This paper presents first a series of experiments which assess the O-Cloud's energy costs and their dependency on the servers' hardware, capacity and data traffic properties which, typically, change over time. Next, it proposes a compute policy for assigning the base station data loads to O-Cloud servers in an energy-efficient fashion; and a radio policy that determines at near-real-time the minimum transmission block size for each user so as to avoid unnecessary energy costs. The policies balance energy savings with performance, and ensure that both of them are dispersed fairly across the servers and users, respectively. To cater for the unknown and time-varying parameters affecting the policies, we develop a novel online learning framework with fairness guarantees that apply to the entire operation horizon of the system (long-term fairness). The policies are evaluated using trace-driven simulations and are fully implemented in an O-RAN compatible system where we measure the energy costs and throughput in realistic scenarios.

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Section: Background and Motivationmentioning

confidence: 99%

Fair Resource Allocation in Virtualized O-RAN Platforms

Aslan,

Iosifidis,

Ayala-Romero

et al. 2024

Proc. ACM Meas. Anal. Comput. Syst.

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“…The authors in [10] have tailored a deep learning-based framework to solve the contextual bandit problem of managing the interplay between computing and radio resources. The other contextual bandits in [11] and [27] utilize a data-efficient algorithm, Bayesian online learning for an energy-aware BS in a vRAN system. These approaches offer remarkable performance with the condition that the current context observation must not be affected by the previous actions, i.e., it only includes exogenous parameters.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs

Murti

Ali

Iosifidis

2024

IEEE Trans. Netw. Serv. Manage.

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Virtualized Radio Access Networks (vRANs) are fully configurable and can be implemented at a low cost over commodity platforms to enable network management flexibility. In this paper, a novel vRAN reconfiguration problem is formulated to jointly reconfigure the functional splits of the base stations (BSs), locations of the virtualized central units (vCUs) and distributed units (vDUs), their resources, and the routing for each BS data flow. The objective is to minimize the long-term total network operation cost while adapting to the varying traffic demands and resource availability. In the first step, testbed measurements are performed to study the relationship between the traffic demands and computing resources, which reveals high variance and depends on the platform and its load. Consequently, finding the perfect model of the underlying system is non-trivial. Therefore, to solve the proposed problem, a deep reinforcement learning (RL)-based framework is proposed and developed using model-free RL approaches. Moreover, the problem consists of multiple BSs sharing the same resources, which results in a multi-dimensional discrete action space and leads to a combinatorial number of possible actions. To overcome this curse of dimensionality, action branching architecture, which is an action decomposition method with a shared decision module followed by neural network is combined with Dueling Double Deep Q-network (D3QN) algorithm. Simulations are carried out using an O-RAN compliant model and real traces of the testbed. Our numerical results show that the proposed framework successfully learns the optimal policy that adaptively selects the vRAN configurations, where its learning convergence can be further expedited through transfer learning even in different vRAN systems. It also offers significant cost savings by up to 59% of a static benchmark, 35% of Deep Deterministic Policy Gradient with discretization, and 76% of non-branching D3QN.

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“…To address this, the authors in [54] propose an online learning-based energy-aware scheduling method for virtualized Base Stations (vBS) in O-RAN. The goal is to optimize scheduling policies that reduce energy consumption while maximizing vBS performance.…”

Section: Energy Efficiencymentioning

confidence: 99%

Recent Advances in Machine Learning for Network Automation in the O-RAN

Hamdan,

Lee,

Triantafyllopoulou

et al. 2023

Sensors

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The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.

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Energy-Aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning

Cited by 3 publications

References 38 publications

Fair Resource Allocation in Virtualized O-RAN Platforms

Fair Resource Allocation in Virtualized O-RAN Platforms

Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs

Recent Advances in Machine Learning for Network Automation in the O-RAN

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