4,192 research outputs found
Dynamic Configuration of Distributed Multimedia Components
A thesis submitted to the University of London in partial fulfillment of the requirements for the degree of Doctor of Philosoph
Random walks on dynamic configuration models: a trichotomy
We consider a dynamic random graph on vertices that is obtained by
starting from a random graph generated according to the configuration model
with a prescribed degree sequence and at each unit of time randomly rewiring a
fraction of the edges. We are interested in the mixing time of a
random walk without backtracking on this dynamic random graph in the limit as
, when is chosen such that . In [1] we found that, under mild regularity
conditions on the degree sequence, the mixing time is of order
when . In the present paper we investigate
what happens when . It turns out that the mixing time is
of order , with the scaled mixing time exhibiting a one-sided cutoff
when and a two-sided cutoff when . The
occurrence of a one-sided cutoff is a rare phenomenon. In our setting it comes
from a competition between the time scales of mixing on the static graph, as
identified by Ben-Hamou and Salez [4], and the regeneration time of first
stepping across a rewired edge.Comment: 14 pages, 5 figure
Mixing times of random walks on dynamic configuration models
The mixing time of a random walk, with or without backtracking, on a random
graph generated according to the configuration model on vertices, is known
to be of order . In this paper we investigate what happens when the
random graph becomes {\em dynamic}, namely, at each unit of time a fraction
of the edges is randomly rewired. Under mild conditions on the
degree sequence, guaranteeing that the graph is locally tree-like, we show that
for every the -mixing time of random walk
without backtracking grows like
as , provided
that . The latter condition
corresponds to a regime of fast enough graph dynamics. Our proof is based on a
randomised stopping time argument, in combination with coupling techniques and
combinatorial estimates. The stopping time of interest is the first time that
the walk moves along an edge that was rewired before, which turns out to be
close to a strong stationary time.Comment: 23 pages, 6 figures. Previous version contained a mistake in one of
the proofs. In this version we look at nonbacktracking random walk instead of
simple random wal
Operational Dynamic Configuration Analysis
Sectors may combine or split within areas of specialization in response to changing traffic patterns. This method of managing capacity and controller workload could be made more flexible by dynamically modifying sector boundaries. Much work has been done on methods for dynamically creating new sector boundaries [1-5]. Many assessments of dynamic configuration methods assume the current day baseline configuration remains fixed [6-7]. A challenging question is how to select a dynamic configuration baseline to assess potential benefits of proposed dynamic configuration concepts. Bloem used operational sector reconfigurations as a baseline [8]. The main difficulty is that operational reconfiguration data is noisy. Reconfigurations often occur frequently to accommodate staff training or breaks, or to complete a more complicated reconfiguration through a rapid sequence of simpler reconfigurations. Gupta quantified a few aspects of airspace boundary changes from this data [9]. Most of these metrics are unique to sector combining operations and not applicable to more flexible dynamic configuration concepts. To better understand what sort of reconfigurations are acceptable or beneficial, more configuration change metrics should be developed and their distribution in current practice should be computed. This paper proposes a method to select a simple sequence of configurations among operational configurations to serve as a dynamic configuration baseline for future dynamic configuration concept assessments. New configuration change metrics are applied to the operational data to establish current day thresholds for these metrics. These thresholds are then corroborated, refined, or dismissed based on airspace practitioner feedback. The dynamic configuration baseline selection method uses a k-means clustering algorithm to select the sequence of configurations and trigger times from a given day of operational sector combination data. The clustering algorithm selects a simplified schedule containing k configurations based on stability score of the sector combinations among the raw operational configurations. In addition, the number of the selected configurations is determined based on balance between accuracy and assessment complexity
DYNAMIC CONFIGURATION FOR DISTRIBUTED SYSTEMS
Published versio
Dynamic configuration of partitioning in spark applications
Spark has become one of the main options for large-scale analytics running on top of shared-nothing clusters. This work aims to make a deep dive into the parallelism configuration and shed light on the behavior of parallel spark jobs. It is motivated by the fact that running a Spark application on all the available processors does not necessarily imply lower running time, while may entail waste of resources. We first propose analytical models for expressing the running time as a function of the number of machines employed. We then take another step, namely to present novel algorithms for configuring dynamic partitioning with a view to minimizing resource consumption without sacrificing running time beyond a user-defined limit. The problem we target is NP-hard. To tackle it, we propose a greedy approach after introducing the notions of dependency graphs and of the benefit from modifying the degree of partitioning at a stage; complementarily, we investigate a randomized approach. Our polynomial solutions are capable of judiciously use the resources that are potentially at user's disposal and strike interesting trade-offs between running time and resource consumption. Their efficiency is thoroughly investigated through experiments based on real execution data.Peer ReviewedPostprint (author's final draft
Modeling a complex production line using virtual cells
This chapter presents modeling and simulation of a complex multistage multiproduct production line with four closed loop networks configuration, which also act as a virtual cell. This allows for a greater understanding of the functions within the production line through the simplification of the production flow with the addition of buffers between the cells. Virtual cells are crucial in this instance due to the dynamic configuration, which could help production system designers in optimizing the complex configuration of production
- …