6,180 research outputs found
On Asymptotic Optimality of Dual Scheduling Algorithm In A Generalized Switch
Generalized switch is a model of a queueing system where parallel servers are interdependent and have time-varying service capabilities. This paper considers the dual scheduling algorithm that uses rate control and queue-length based scheduling to allocate resources for a generalized switch. We consider a saturated system in which each user has infinite amount of data to be served. We prove the asymptotic optimality of the dual scheduling algorithm for such a system, which says that the vector of average service rates of the scheduling algorithm maximizes some aggregate concave utility functions. As the fairness objectives can be achieved by appropriately choosing utility functions, the asymptotic optimality establishes the fairness properties of the dual scheduling algorithm.
The dual scheduling algorithm motivates a new architecture for scheduling, in which an additional queue is introduced to interface the user data queue and the time-varying server and to modulate the scheduling process, so as to achieve different performance objectives. Further research would include scheduling with Quality of Service guarantees with the dual scheduler, and its application and implementation in various versions of the generalized switch model
Towards Fast-Convergence, Low-Delay and Low-Complexity Network Optimization
Distributed network optimization has been studied for well over a decade.
However, we still do not have a good idea of how to design schemes that can
simultaneously provide good performance across the dimensions of utility
optimality, convergence speed, and delay. To address these challenges, in this
paper, we propose a new algorithmic framework with all these metrics
approaching optimality. The salient features of our new algorithm are
three-fold: (i) fast convergence: it converges with only
iterations that is the fastest speed among all the existing algorithms; (ii)
low delay: it guarantees optimal utility with finite queue length; (iii) simple
implementation: the control variables of this algorithm are based on virtual
queues that do not require maintaining per-flow information. The new technique
builds on a kind of inexact Uzawa method in the Alternating Directional Method
of Multiplier, and provides a new theoretical path to prove global and linear
convergence rate of such a method without requiring the full rank assumption of
the constraint matrix
Convex Analysis and Optimization with Submodular Functions: a Tutorial
Set-functions appear in many areas of computer science and applied
mathematics, such as machine learning, computer vision, operations research or
electrical networks. Among these set-functions, submodular functions play an
important role, similar to convex functions on vector spaces. In this tutorial,
the theory of submodular functions is presented, in a self-contained way, with
all results shown from first principles. A good knowledge of convex analysis is
assumed
Distributed Random Access Algorithm: Scheduling and Congesion Control
This paper provides proofs of the rate stability, Harris recurrence, and
epsilon-optimality of CSMA algorithms where the backoff parameter of each node
is based on its backlog. These algorithms require only local information and
are easy to implement.
The setup is a network of wireless nodes with a fixed conflict graph that
identifies pairs of nodes whose simultaneous transmissions conflict. The paper
studies two algorithms. The first algorithm schedules transmissions to keep up
with given arrival rates of packets. The second algorithm controls the arrivals
in addition to the scheduling and attempts to maximize the sum of the utilities
of the flows of packets at the different nodes. For the first algorithm, the
paper proves rate stability for strictly feasible arrival rates and also Harris
recurrence of the queues. For the second algorithm, the paper proves the
epsilon-optimality. Both algorithms operate with strictly local information in
the case of decreasing step sizes, and operate with the additional information
of the number of nodes in the network in the case of constant step size
Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information
This paper considers utility optimal power control for energy harvesting
wireless devices with a finite capacity battery. The distribution information
of the underlying wireless environment and harvestable energy is unknown and
only outdated system state information is known at the device controller. This
scenario shares similarity with Lyapunov opportunistic optimization and online
learning but is different from both. By a novel combination of Zinkevich's
online gradient learning technique and the drift-plus-penalty technique from
Lyapunov opportunistic optimization, this paper proposes a learning-aided
algorithm that achieves utility within of the optimal, for any
desired , by using a battery with an capacity. The
proposed algorithm has low complexity and makes power investment decisions
based on system history, without requiring knowledge of the system state or its
probability distribution.Comment: This version extends v1 (our INFOCOM 2018 paper): (1) add a new
section (Section V) to study the case where utility functions are non-i.i.d.
arbitrarily varying (2) add more simulation experiments. The current version
is published in IEEE/ACM Transactions on Networkin
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