Base-Station Sleeping Control and Power Matching for Energy–Delay Tradeoffs With Bursty Traffic

Wu, Jian; Bao, Yongping; Miao, Guowang; Zhou, Sheng; Niu, Zhisheng

doi:10.1109/tvt.2015.2434381

Cited by 84 publications

(50 citation statements)

References 36 publications

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“…The traffic-aware N -threshold transmission scheme inherently differentiates from the previously published literature [21]- [26]. In particular, the mean waiting time, as a key performance metric, is typically composed of the queuing delay, transmission delay as well as processing delay.…”

Section: B Related Workmentioning

confidence: 99%

Traffic-Aware Two-Stage Queueing Communication Networks: Queue Analysis and Energy Saving

Miridakis

Xiao

et al. 2020

IEEE Trans. Commun.

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To boost energy saving for the general delay-tolerant IoT networks, a two-stage and single-relay queueing communication scheme is investigated. Concretely, a traffic-aware Nthreshold and gated-service policy are applied at the relay. As two fundamental and significant performance metrics, the mean waiting time and long-term expected power consumption are explicitly derived and related with the queueing and service parameters, such as packet arrival rate, service threshold and channel statistics. Besides, we take into account the electrical circuit energy consumptions when the relay server and access point (AP) are in different modes and energy costs for mode transitions, whereby the power consumption model is more practical. The expected power minimization problem under the mean waiting time constraint is formulated. Tight closed-form bounds are adopted to obtain tractable analytical formulae with less computational complexity. The optimal energy-saving service threshold that can flexibly adjust to packet arrival rate is determined. In addition, numerical results reveal that: 1) sacrificing the mean waiting time not necessarily facilitates power savings; 2) a higher arrival rate leads to a greater optimal service threshold; and 3) our policy performs better than the current state-of-the-art.Index Terms-Delay tolerant IoT relaying networks, mean waiting time, N -threshold and gated policy, two-hop queueing, and traffic-aware power saving.

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Section: B Related Workmentioning

confidence: 99%

Traffic-Aware Two-Stage Queueing Communication Networks: Queue Analysis and Energy Saving

Miridakis

Xiao

et al. 2020

IEEE Trans. Commun.

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“…We nevertheless use it due to the difficulties in finding open datasets containing computing requests. site n at time slot t is formulated as follows, inspired by [9], [43], [44], [45] and [46]:…”

Section: A Traffic Load and Energy Consumptionmentioning

confidence: 99%

Online Supervisory Control and Resource Management for Energy Harvesting BS Sites Empowered with Computation Capabilities

Dlamini

Gambı́n

Munaretto³

et al. 2019

Wireless Communications and Mobile Computing

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The convergence of communication and computing has lead to the emergence of Multi-access Edge Computing (MEC), where computing resources (supported by Virtual Machines (VMs)) are distributed at the edge of the Mobile Network (MN), i.e., in Base Stations (BSs), with the aim of ensuring reliable and ultra-low latency services. Moreover, BSs equipped with Energy Harvesting (EH) systems can decrease the amount of energy drained from the power grid resulting into energetically self-sufficient MNs. The combination of these paradigms is considered here. Specifically, we propose an online optimization algorithm, called ENergy Aware and Adaptive Management (ENAAM), based on foresighted control policies exploiting (short-term) traffic load and harvested energy forecasts, where BSs and VMs are dynamically switched on/off towards energy savings and QoS provisioning. Our numerical results reveal that ENAAM achieves energy savings with respect to the case where no energy management is applied, ranging from 57% and 69%. Moreover, the extension of ENAAM within a cluster of BSs provides a further gain ranging from 9% to 16% in energy savings with respect to the optimization performed in isolation for each BS.

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“…For example, in [3], a traffic load balancing has been proposed to achieve tradeoffs between two network utilities; namely the average traffic delivery latency and the green energy utilization. The authors in [4] have investigated the impact of optimizing the BS statuses on the energy-delay tradeoff. A comprehensive survey about BS on/off switching strategies is provided in [5].…”

Section: Introductionmentioning

confidence: 99%

A Time-Varied Probabilistic ON/OFF Switching Algorithm for Cellular Networks

et al. 2018

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Base-Station Sleeping Control and Power Matching for Energy–Delay Tradeoffs With Bursty Traffic

Cited by 84 publications

References 36 publications

Traffic-Aware Two-Stage Queueing Communication Networks: Queue Analysis and Energy Saving

Traffic-Aware Two-Stage Queueing Communication Networks: Queue Analysis and Energy Saving

Online Supervisory Control and Resource Management for Energy Harvesting BS Sites Empowered with Computation Capabilities

A Time-Varied Probabilistic ON/OFF Switching Algorithm for Cellular Networks

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