76,769 research outputs found
Online Bin Covering: Expectations vs. Guarantees
Bin covering is a dual version of classic bin packing. Thus, the goal is to
cover as many bins as possible, where covering a bin means packing items of
total size at least one in the bin.
For online bin covering, competitive analysis fails to distinguish between
most algorithms of interest; all "reasonable" algorithms have a competitive
ratio of 1/2. Thus, in order to get a better understanding of the combinatorial
difficulties in solving this problem, we turn to other performance measures,
namely relative worst order, random order, and max/max analysis, as well as
analyzing input with restricted or uniformly distributed item sizes. In this
way, our study also supplements the ongoing systematic studies of the relative
strengths of various performance measures.
Two classic algorithms for online bin packing that have natural dual versions
are Harmonic and Next-Fit. Even though the algorithms are quite different in
nature, the dual versions are not separated by competitive analysis. We make
the case that when guarantees are needed, even under restricted input
sequences, dual Harmonic is preferable. In addition, we establish quite robust
theoretical results showing that if items come from a uniform distribution or
even if just the ordering of items is uniformly random, then dual Next-Fit is
the right choice.Comment: IMADA-preprint-c
Distributed data mining in grid computing environments
The official published version of this article can be found at the link below.The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper
Competitive Parallel Disk Prefetching and Buffer Management
We provide a competitive analysis framework for online prefetching and buffer management
algorithms in parallel I/O systems, using a read-once model of block references. This has
widespread applicability to key I/O-bound applications such as external merging and concurrent
playback of multiple video streams. Two realistic lookahead models, global lookahead and local
lookahead, are defined. Algorithms NOM and GREED based on these two forms of lookahead
are analyzed for shared buffer and distributed buffer configurations, both of which occur frequently
in existing systems. An important aspect of our work is that we show how to implement
both the models of lookahead in practice using the simple techniques of forecasting and flushing.
Given a
-disk parallel I/O system and a globally shared I/O buffer that can hold upto
disk
blocks, we derive a lower bound of
on the competitive ratio of any deterministic online
prefetching algorithm with
lookahead. NOM is shown to match the lower bound using
global
-block lookahead. In contrast, using only local lookahead results in an
competitive
ratio. When the buffer is distributed into
portions of
blocks each, the algorithm
GREED based on local lookahead is shown to be optimal, and NOM is within a constant factor
of optimal. Thus we provide a theoretical basis for the intuition that global lookahead is more
valuable for prefetching in the case of a shared buffer configuration whereas it is enough to provide
local lookahead in case of the distributed configuration. Finally, we analyze the performance
of these algorithms for reference strings generated by a uniformly-random stochastic process and
we show that they achieve the minimal expected number of I/Os. These results also give bounds
on the worst-case expected performance of algorithms which employ randomization in the data
layout
Real-time computational attention model for dynamic scenes analysis: from implementation to evaluation.
International audienceProviding real time analysis of the huge amount of data generated by computer vision algorithms in interactive applications is still an open problem. It promises great advances across a wide variety of elds. When using dynamics scene analysis algorithms for computer vision, a trade-o must be found between the quality of the results expected, and the amount of computer resources allocated for each task. It is usually a design time decision, implemented through the choice of pre-de ned algorithms and parameters. However, this way of doing limits the generality of the system. Using an adaptive vision system provides a more exible solution as its analysis strategy can be changed according to the new information available. As a consequence, such a system requires some kind of guiding mechanism to explore the scene faster and more e ciently. We propose a visual attention system that it adapts its processing according to the interest (or salience) of each element of the dynamic scene. Somewhere in between hierarchical salience based and competitive distributed, we propose a hierarchical yet competitive and non salience based model. Our original approach allows the generation of attentional focus points without the need of neither saliency map nor explicit inhibition of return mechanism. This new real- time computational model is based on a preys / predators system. The use of this kind of dynamical system is justi ed by an adjustable trade-o between nondeterministic attentional behavior and properties of stability, reproducibility and reactiveness
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