Energy saving local control policy for Green Reconfigurable Routers

Wei, Yifei; Wang, Xiaojun; Feng, Guo; Hogan, Gabriel; Collier, Martin

doi:10.1109/icc.2015.7248325

Cited by 6 publications

(3 citation statements)

References 13 publications

(8 reference statements)

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“…One of the earliest known applications of using an MDP to control resources in computing systems at runtime is [12]. Other notable examples of differing approaches in the literature include a reconfigurable router [13], a reconfigurable digital filter bank [14,15], a power management module for a microprocessor [16], and a smartphone scheduling program that synchronizes email efficiently [17].…”

Section: Markov Decision Processesmentioning

confidence: 99%

Runtime Adaptation in Wireless Sensor Nodes Using Structured Learning

Sapio¹,

Bhattacharyya²,

Wolf³

2020

Preprint

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Markov Decision Processes (MDPs) provide important capabilities for facilitating the dynamic adaptation and self-optimization of cyber physical systems at runtime. In recent years, this has primarily taken the form of Reinforcement Learning (RL) techniques that eliminate some MDP components for the purpose of reducing computational requirements. In this work, we show that recent advancements in Compact MDP Models (CMMs) provide sufficient cause to question this trend when designing wireless sensor network nodes. In this work, a novel CMM-based approach to designing self-aware wireless sensor nodes is presented and compared to Q-Learning, a popular RL technique. We show that a certain class of CPS nodes is not well served by RL methods, and contrast RL versus CMM methods in this context. Through both simulation and a prototype implementation, we demonstrate that CMM methods can provide significantly better runtime adaptation performance relative to Q-Learning, with comparable resource requirements.1 This is a pre-publication version of a paper that has been accepted for publication in the ACM Transactions on Cyber-Physical Systems. The official/final version of the paper will be posted on the ACM Digital Library.

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Section: Markov Decision Processesmentioning

confidence: 99%

Runtime Adaptation in Wireless Sensor Nodes Using Structured Learning

Sapio¹,

Bhattacharyya²,

Wolf³

2020

Preprint

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“…Other researchers have sought to use MDPs with similar goals. Wei et al have demonstrated the effective use of an MDP to control the processing rate of a network router [23]. This work created a Markov model of only the external environment, not the system under control.…”

Section: Background and Related Workmentioning

confidence: 99%

“…We define g 2 based on a formulation in [23] for the normalized power savings of an electronic system. Specifically, in order to normalize power consumption and treat it as a form of savings, we measure power consumption (x 2 ) in each state and note the minimum and maximum possible values.…”

Section: R(s A)mentioning

confidence: 99%

Reconfigurable Digital Channelizer Design Using Factored Markov Decision Processes

Sapio

et al. 2017

J Sign Process Syst

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In this work, a novel digital channelizer design is developed through the use of a compact, system-level modeling approach. The model efficiently captures key properties of a digital channelizer system and its time-varying operation. The model applies powerful Markov Decision Process (MDP) techniques in new ways for design optimization of reconfigurable channelization processing. The result is a promising methodology for design and implementation of digital channelizers that adapt dynamically to changing use cases and stochastic environments while optimizing simultaneously for multiple conflicting performance goals. The method is used to employ an MDP to generate a runtime reconfiguration policy for a time-varying environment. Through extensive simulations, the robustness of the adaptation is demonstrated in comparison with the prior state of the art.

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