11 research outputs found
Multi-Path Alpha-Fair Resource Allocation at Scale in Distributed Software Defined Networks
The performance of computer networks relies on how bandwidth is shared among
different flows. Fair resource allocation is a challenging problem particularly
when the flows evolve over time. To address this issue, bandwidth sharing
techniques that quickly react to the traffic fluctuations are of interest,
especially in large scale settings with hundreds of nodes and thousands of
flows. In this context, we propose a distributed algorithm based on the
Alternating Direction Method of Multipliers (ADMM) that tackles the multi-path
fair resource allocation problem in a distributed SDN control architecture. Our
ADMM-based algorithm continuously generates a sequence of resource allocation
solutions converging to the fair allocation while always remaining feasible, a
property that standard primal-dual decomposition methods often lack. Thanks to
the distribution of all computer intensive operations, we demonstrate that we
can handle large instances at scale
Robo-Chargers: Optimal Operation and Planning of a Robotic Charging System to Alleviate Overstay
Charging infrastructure availability is a major concern for plug-in electric
vehicle users. Nowadays, the limited public chargers are commonly occupied by
vehicles which have already been fully charged. Such phenomenon, known as
overstay, hinders other vehicles' accessibility to charging resources. In this
paper, we analyze a charging facility innovation to tackle the challenge of
overstay, leveraging the idea of Robo-chargers - automated chargers that can
rotate in a charging station and proactively plug or unplug plug-in electric
vehicles. We formalize an operation model for stations incorporating
Fixed-chargers and Robo-chargers. Optimal scheduling can be solved with the
recognition of the combinatorial nature of vehicle-charger assignments,
charging dynamics, and customer waiting behaviors. Then, with operation model
nested, we develop a planning model to guide economical investment on both
types of chargers so that the total cost of ownership is minimized. In the
planning phase, it further considers charging demand variances and service
capacity requirements. In this paper, we provide systematic techno-economical
methods to evaluate if introducing Robo-chargers is beneficial given a specific
application scenario. Comprehensive sensitivity analysis based on real-world
data highlights the advantages of Robo-chargers, especially in a scenario where
overstay is severe. Validations also suggest the tractability of operation
model and robustness of planning results for real-time application under
reasonable model mismatches, uncertainties and disturbances
Lower Bounds for the Fair Resource Allocation Problem
The -fair resource allocation problem has received remarkable
attention and has been studied in numerous application fields. Several
algorithms have been proposed in the context of -fair resource sharing
to distributively compute its value. However, little work has been done on its
structural properties. In this work, we present a lower bound for the optimal
solution of the weighted -fair resource allocation problem and compare
it with existing propositions in the literature. Our derivations rely on a
localization property verified by optimization problems with separable
objective that permit one to better exploit their local structures. We give a
local version of the well-known midpoint domination axiom used to axiomatically
build the Nash Bargaining Solution (or proportionally fair resource allocation
problem). Moreover, we show how our lower bound can improve the performances of
a distributed algorithm based on the Alternating Directions Method of
Multipliers (ADMM). The evaluation of the algorithm shows that our lower bound
can considerably reduce its convergence time up to two orders of magnitude
compared to when the bound is not used at all or is simply looser.Comment: in IFIP WG 7.3 Performance 2017, New York, NY US
Lower Bounds for the Fair Resource Allocation Problem
International audienceThe α-fair resource allocation problem has received remarkable attention and has been studied in numerous application fields. Several algorithms have been proposed in the context of α-fair resource sharing to distributively compute its value. However, little work has been done on its structural properties. In this work, we present a lower bound for the optimal solution of the weighted α-fair resource allocation problem and compare it with existing propositions in the literature. Our derivations rely on a localization property verified by optimization problems with separable objective that permit one to better exploit their local structures. We give a local version of the well-known midpoint domination axiom used to axiomatically build the Nash Bargaining Solution (or proportionally fair resource allocation problem). Moreover, we show how our lower bound can improve the performances of a distributed algorithm based on the Alternating Directions Method of Multipliers (ADMM). The evaluation of the algorithm shows that our lower bound can considerably reduce its convergence time up to two orders of magnitude compared to when the bound is not used at all or is simply looser
Multi-Path Alpha-Fair Resource Allocation at Scale in Distributed Software Defined Networks
International audienceThe performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time. To address this issue, bandwidth sharing techniques that quickly react to the traffic fluctuations are of interest, especially in large scale settings with hundreds of nodes and thousands of flows. In this context, we propose a distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM) that tackles the multi-path fair resource allocation problem in a distributed SDN control architecture. Our ADMM-based algorithm continuously generates a sequence of resource allocation solutions converging to the fair allocation while always remaining feasible, a property that standard primal-dual decomposition methods often lack. Thanks to the distribution of all computer intensive operations, we demonstrate that we can handle large instances at scale
Partage équitable de ressources en temps réel dans les Software Defined Networks distribués
The performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time.To address this issue, bandwidth sharing techniques that quickly react to the traffic fluctuations are of interest, especially in large scale settings with hundreds of nodes and thousands of flows. In this context, we propose a distributed algorithm that tackles the fair resource allocation problem in a distributed SDN control architecture. Our algorithm continuously generates a sequence of resource allocation solutions converging to the fair allocation while always remaining feasible, a property that standard primal-dual decomposition methods often lack. Thanks to the distribution of all computer intensive operations, we demonstrate that we can handle large instances in real-time.La performance des réseaux informatiques est fortement liée au partage équitable de la bande-passante entre les différents flux.Lorsque la taille de ces flux varie constamment dans le temps, le problème de partage des ressources est non-trivial. Afin d'aborder ce problème, des techniques de partage pouvant réagir rapidement aux fluctuations de trafic sont désirables, en particulier pour le contrôle de grands réseaux avec des centaines de noeuds et des milliers de flux. Nous proposons un algorithme distribué qui s'attaque au problème de partage de ressources équitable dans le contexte des architectures Software-Defined Networks (SDN) distribuées. Cet algorithme génère en chaque instant des solutions convergeant vers le partage équitable en respectant toujours l'ensemble des contraintes, une propriété non satisfaite par les méthodes classiques de décomposition primale-duale. Grâce à la distribution des calculs, nous montrons que notre algorithme peut contrôler de grands réseaux en temps réel
Real-Time Fair Resource Allocation in Distributed Software Defined Networks
International audienceThe performance of computer networks relies on how bandwidth is shared among different flows. Fair resource allocation is a challenging problem particularly when the flows evolve over time. To address this issue, bandwidth sharing techniques that quickly react to the traffic fluctuations are of interest, especially in large scale settings with hundreds of nodes and thousands of flows. In this context, we propose a distributed algorithm based on the Alternating Direction Method of Multipliers (ADMM) that tackles the fair resource allocation problem in a distributed SDN control architecture. Our ADMM-based algorithm continuously generates a sequence of resource allocation solutions converging to the fair allocation while always remaining feasible, a property that standard primal-dual decomposition methods often lack. Thanks to the distribution of all computer intensive operations, we demonstrate that we can handle large instances in real-time
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Robo-Chargers: Optimal Operation and Planning ofa Robotic Charging System to Alleviate Overstay
Lower Bounds for the Fair Resource Allocation Problem
International audienceThe α-fair resource allocation problem has received remarkable attention and has been studied in numerous application fields. Several algorithms have been proposed in the context of α-fair resource sharing to distributively compute its value. However, little work has been done on its structural properties. In this work, we present a lower bound for the optimal solution of the weighted α-fair resource allocation problem and compare it with existing propositions in the literature. Our derivations rely on a localization property verified by optimization problems with separable objective that permit one to better exploit their local structures. We give a local version of the well-known midpoint domination axiom used to axiomatically build the Nash Bargaining Solution (or proportionally fair resource allocation problem). Moreover, we show how our lower bound can improve the performances of a distributed algorithm based on the Alternating Directions Method of Multipliers (ADMM). The evaluation of the algorithm shows that our lower bound can considerably reduce its convergence time up to two orders of magnitude compared to when the bound is not used at all or is simply looser