215 research outputs found
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
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
Performance of CSMA in Multi-Channel Wireless Networks
We analyze the performance of CSMA in multi-channel wireless networks,
accounting for the random nature of traffic. Specifically, we assess the
ability of CSMA to fully utilize the radio resources and in turn to stabilize
the network in a dynamic setting with flow arrivals and departures. We prove
that CSMA is optimal in ad-hoc mode but not in infrastructure mode, when all
data flows originate from or are destined to some access points, due to the
inherent bias of CSMA against downlink traffic. We propose a slight
modification of CSMA, that we refer to as flow-aware CSMA, which corrects this
bias and makes the algorithm optimal in all cases. The analysis is based on
some time-scale separation assumption which is proved valid in the limit of
large flow sizes
Weighted Spectral Embedding of Graphs
We present a novel spectral embedding of graphs that incorporates weights
assigned to the nodes, quantifying their relative importance. This spectral
embedding is based on the first eigenvectors of some properly normalized
version of the Laplacian. We prove that these eigenvectors correspond to the
configurations of lowest energy of an equivalent physical system, either
mechanical or electrical, in which the weight of each node can be interpreted
as its mass or its capacitance, respectively. Experiments on a real dataset
illustrate the impact of weighting on the embedding
Power Control in Massive MIMO with Dynamic User Population
This paper considers the problem of power control in Massive MIMO systems
taking into account the pilot contamination issue and the arrivals and
departures of users in the network. Contrary to most of existing work in MIMO
systems that focuses on the physical layer with fixed number of users, we
consider in this work that the users arrive dynamically and leave the network
once they are served. We provide a power control strategy, having a polynomial
complexity, and prove that this policy stabilizes the network whenever
possible. We then provide a distributed implementation of the power control
policy requiring low information exchange between the BSs and show that it
achieves the same stability region as the centralized policy.Comment: conference paper, submitte
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