1,707 research outputs found
A Stochastic Resource-Sharing Network for Electric Vehicle Charging
We consider a distribution grid used to charge electric vehicles such that
voltage drops stay bounded. We model this as a class of resource-sharing
networks, known as bandwidth-sharing networks in the communication network
literature. We focus on resource-sharing networks that are driven by a class of
greedy control rules that can be implemented in a decentralized fashion. For a
large number of such control rules, we can characterize the performance of the
system by a fluid approximation. This leads to a set of dynamic equations that
take into account the stochastic behavior of EVs. We show that the invariant
point of these equations is unique and can be computed by solving a specific
ACOPF problem, which admits an exact convex relaxation. We illustrate our
findings with a case study using the SCE 47-bus network and several special
cases that allow for explicit computations.Comment: 13 pages, 8 figure
Enhanced Cluster Computing Performance Through Proportional Fairness
The performance of cluster computing depends on how concurrent jobs share
multiple data center resource types like CPU, RAM and disk storage. Recent
research has discussed efficiency and fairness requirements and identified a
number of desirable scheduling objectives including so-called dominant resource
fairness (DRF). We argue here that proportional fairness (PF), long recognized
as a desirable objective in sharing network bandwidth between ongoing flows, is
preferable to DRF. The superiority of PF is manifest under the realistic
modelling assumption that the population of jobs in progress is a stochastic
process. In random traffic the strategy-proof property of DRF proves
unimportant while PF is shown by analysis and simulation to offer a
significantly better efficiency-fairness tradeoff.Comment: Submitted to Performance 201
Game-Theoretic Pricing and Selection with Fading Channels
We consider pricing and selection with fading channels in a Stackelberg game
framework. A channel server decides the channel prices and a client chooses
which channel to use based on the remote estimation quality. We prove the
existence of an optimal deterministic and Markovian policy for the client, and
show that the optimal policies of both the server and the client have threshold
structures when the time horizon is finite. Value iteration algorithm is
applied to obtain the optimal solutions for both the server and client, and
numerical simulations and examples are given to demonstrate the developed
result.Comment: 6 pages, 4 figures, accepted by the 2017 Asian Control Conferenc
Improved Spectrum Mobility using Virtual Reservation in Collaborative Cognitive Radio Networks
Cognitive radio technology would enable a set of secondary users (SU) to
opportunistically use the spectrum licensed to a primary user (PU). On the
appearance of this PU on a specific frequency band, any SU occupying this band
should free it for PUs. Typically, SUs may collaborate to reduce the impact of
cognitive users on the primary network and to improve the performance of the
SUs. In this paper, we propose and analyze the performance of virtual
reservation in collaborative cognitive networks. Virtual reservation is a novel
link maintenance strategy that aims to maximize the throughput of the cognitive
network through full spectrum utilization. Our performance evaluation shows
significant improvements not only in the SUs blocking and forced termination
probabilities but also in the throughput of cognitive users.Comment: 7 pages, 10 figures, IEEE ISCC 201
Multi-resource fairness: Objectives, algorithms and performance
Designing efficient and fair algorithms for sharing multiple resources
between heterogeneous demands is becoming increasingly important. Applications
include compute clusters shared by multi-task jobs and routers equipped with
middleboxes shared by flows of different types. We show that the currently
preferred objective of Dominant Resource Fairness has a significantly less
favorable efficiency-fairness tradeoff than alternatives like Proportional
Fairness and our proposal, Bottleneck Max Fairness. In addition to other
desirable properties, these objectives are equally strategyproof in any
realistic scenario with dynamic demand
A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning
In this tutorial paper, a comprehensive survey is given on several major
systematic approaches in dealing with delay-aware control problems, namely the
equivalent rate constraint approach, the Lyapunov stability drift approach and
the approximate Markov Decision Process (MDP) approach using stochastic
learning. These approaches essentially embrace most of the existing literature
regarding delay-aware resource control in wireless systems. They have their
relative pros and cons in terms of performance, complexity and implementation
issues. For each of the approaches, the problem setup, the general solution and
the design methodology are discussed. Applications of these approaches to
delay-aware resource allocation are illustrated with examples in single-hop
wireless networks. Furthermore, recent results regarding delay-aware multi-hop
routing designs in general multi-hop networks are elaborated. Finally, the
delay performance of the various approaches are compared through simulations
using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201
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