Multi-layer Decomposition of Optimal Resource Sharing Problems

Karakoc, Nurullah; Scaglione, Anna; Nedić, Angelia

doi:10.1109/cdc.2018.8619777

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…To this end, WNOS first determines a decomposition approach based on the mathematical structure of the network control problem, including whether the problem involves one or multiple sessions, what protocol layers are to be optimized, if the problem is convex or not, among others. Different decomposition approaches can lead to different structures of the resulting distributed control program with various convergence properties, communication overhead, and achievable network performance [8], [9], [10], [11].…”

Section: Wnos Architecturementioning

confidence: 99%

“…Physical Layer : maximize λ1C1(Π) + λ2C2(Π) + λ3C3(Π), (11) where, at the transport layer, each flow s ∈ {1, 2, 3} maximizes its own utility by adjusting its transmission rate R s with given dual coefficients; while the physical-layer subproblem maximizes a weighted-sum-capacity by adapting the transmission strategies Π, i.e., the transmission power of individual nodes. Now we show how the the decomposition results can be applied at network run time by taking the transport-layer subproblem for s = 1 as an example while the same principles can also be applied to other subproblems.…”

Section: Rsmentioning

confidence: 99%

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WNOS: Enabling Principled Software-Defined Wireless Networking

Guan

Bertizzolo

Demirors

et al. 2021

IEEE/ACM Trans. Networking

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This article investigates the basic design principles for a new Wireless Network Operating System (WNOS), a radically different approach to software-defined networking (SDN) for infrastructure-less wireless networks. Departing from wellunderstood approaches inspired by OpenFlow, WNOS provides the network designer with an abstraction hiding (i) the lowerlevel details of the wireless protocol stack and (ii) the distributed nature of the network operations. Based on this abstract representation, the WNOS takes network control programs written on a centralized, high-level view of the network and automatically generates distributed cross-layer control programs based on distributed optimization theory that are executed by each individual node on an abstract representation of the radio hardware.We first discuss the main architectural principles of WNOS. Then, we discuss a new approach to automatically generate solution algorithms for each of the resulting subproblems in an automated fashion. Finally, we illustrate a prototype implementation of WNOS on software-defined radio devices and test its effectiveness by considering specific cross-layer control problems. Experimental results indicate that, based on the automatically generated distributed control programs, WNOS achieves 18%, 56% and 80.4% utility gain in networks with low, medium and high levels of interference; maybe more importantly, we illustrate how the global network behavior can be controlled by modifying a few lines of code on a centralized abstraction.

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Section: Wnos Architecturementioning

confidence: 99%

Section: Rsmentioning

confidence: 99%

WNOS: Enabling Principled Software-Defined Wireless Networking

Guan

Bertizzolo

Demirors

et al. 2021

IEEE/ACM Trans. Networking

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“…We note that this case study does not seek to examine theoretical convergence guarantees for multi-layer multi-timescale optimization. Initial steps towards such a theoretical analysis have recently been reported in [79]. As a complement to and a motivation for detailed theoretical analyses, this present case study seeks to demonstrate the feasibility of the multi-layer optimization with multiple timescales and to showcase performance gains for wireless backhaul.…”

Section: Wireless Backhaul Network Optimizationmentioning

confidence: 99%

“…Such a two-layer benchmark would still perform the intra-operator optimization, but with only two layers compared to the three layers in the considered benchmark. These two benchmarks would generally perform similarly, with differences being influenced by convergence characteristics [79]. For the present study, we focus on the impact of the sharing of the backhaul resource across operators as quantified by comparing the considered no-SDN "intra-operator" optimization with the full SDN-based optimization involving the central SDN orchestrator.…”

Section: Comparison Benchmarkmentioning

confidence: 99%

A Multi-Layer Multi-Timescale Network Utility Maximization Framework for the SDN-Based LayBack Architecture Enabling Wireless Backhaul Resource Sharing

et al. 2019

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With the emergence of small cell networks and fifth-generation (5G) wireless networks, the backhaul becomes increasingly complex. This study addresses the problem of how a central SDN orchestrator can flexibly share the total backhaul capacity of the various wireless operators among their gateways and radio nodes (e.g., LTE enhanced Node Bs or Wi-Fi access points). In order to address this backhaul resource allocation problem, we introduce a novel backhaul optimization methodology in the context of the recently proposed LayBack SDN backhaul architecture. In particular, we explore the decomposition of the central optimization problem into a layered dual decomposition model that matches the architectural layers of the LayBack backhaul architecture. In order to promote scalability and responsiveness, we employ different timescales, i.e., fast timescales at the radio nodes and slower timescales in the higher LayBack layers that are closer to the central SDN orchestrator. We numerically evaluate the scalable layered optimization for a specific case of the LayBack backhaul architecture with four layers, namely a radio node (eNB) layer, a gateway layer, an operator layer, and central coordination in an SDN orchestrator layer. The coordinated sharing of the total backhaul capacity among multiple operators lowers the queue lengths compared to the conventional backhaul without sharing among operators.

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“…2. Because the cost and constraints of an aggregator are decomposable in terms of the prosumer profiles p b , the problem could be decomposed further to allow individual prosumers to keep C b (p b ) and P b private, interacting with the aggregator in a similar manner as shown in [51].…”

mentioning

confidence: 99%

A secure distributed ledger for transactive energy: The Electron Volt Exchange (EVE) blockchain

et al. 2021

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The adoption of blockchain for Transactive Energy has gained significant momentum as it allows mutually non-trusting agents to trade energy services in a trustless energy market. Research to date has assumed that the built-in Byzantine Fault Tolerance in recording transactions in a ledger is sufficient to ensure integrity. Such work must be extended to address security gaps including random bilateral transactions that do not guarantee reliable and efficient market operation, and market participants having incentives to cheat when reporting actual production/consumption figures. Work herein introduces the Electron Volt Exchange framework with the following characteristics: 1) a distributed protocol for pricing and scheduling prosumers' production/consumption while keeping constraints and bids private, and 2) a distributed algorithm to prevent theft that verifies prosumers' compliance to scheduled transactions using information from grid sensors (such as smart meters) and mitigates the impact of false data injection attacks. Flexibility and robustness of the approach are demonstrated through simulation and implementation using Hyperledger Fabric.

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Multi-layer Decomposition of Optimal Resource Sharing Problems

Cited by 7 publications

References 10 publications

WNOS: Enabling Principled Software-Defined Wireless Networking

WNOS: Enabling Principled Software-Defined Wireless Networking

A Multi-Layer Multi-Timescale Network Utility Maximization Framework for the SDN-Based LayBack Architecture Enabling Wireless Backhaul Resource Sharing

A secure distributed ledger for transactive energy: The Electron Volt Exchange (EVE) blockchain

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