9,614 research outputs found
Discrete Load Balancing in Heterogeneous Networks with a Focus on Second-Order Diffusion
In this paper we consider a wide class of discrete diffusion load balancing
algorithms. The problem is defined as follows. We are given an interconnection
network and a number of load items, which are arbitrarily distributed among the
nodes of the network. The goal is to redistribute the load in iterative
discrete steps such that at the end each node has (almost) the same number of
items. In diffusion load balancing nodes are only allowed to balance their load
with their direct neighbors.
We show three main results. Firstly, we present a general framework for
randomly rounding the flow generated by continuous diffusion schemes over the
edges of a graph in order to obtain corresponding discrete schemes. Compared to
the results of Rabani, Sinclair, and Wanka, FOCS'98, which are only valid
w.r.t. the class of homogeneous first order schemes, our framework can be used
to analyze a larger class of diffusion algorithms, such as algorithms for
heterogeneous networks and second order schemes. Secondly, we bound the
deviation between randomized second order schemes and their continuous
counterparts. Finally, we provide a bound for the minimum initial load in a
network that is sufficient to prevent the occurrence of negative load at a node
during the execution of second order diffusion schemes.
Our theoretical results are complemented with extensive simulations on
different graph classes. We show empirically that second order schemes, which
are usually much faster than first order schemes, will not balance the load
completely on a number of networks within reasonable time. However, the maximum
load difference at the end seems to be bounded by a constant value, which can
be further decreased if first order scheme is applied once this value is
achieved by second order scheme.Comment: Full version of paper submitted to ICDCS 201
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
Parallel and Distributed Simulation from Many Cores to the Public Cloud (Extended Version)
In this tutorial paper, we will firstly review some basic simulation concepts
and then introduce the parallel and distributed simulation techniques in view
of some new challenges of today and tomorrow. More in particular, in the last
years there has been a wide diffusion of many cores architectures and we can
expect this trend to continue. On the other hand, the success of cloud
computing is strongly promoting the everything as a service paradigm. Is
parallel and distributed simulation ready for these new challenges? The current
approaches present many limitations in terms of usability and adaptivity: there
is a strong need for new evaluation metrics and for revising the currently
implemented mechanisms. In the last part of the paper, we propose a new
approach based on multi-agent systems for the simulation of complex systems. It
is possible to implement advanced techniques such as the migration of simulated
entities in order to build mechanisms that are both adaptive and very easy to
use. Adaptive mechanisms are able to significantly reduce the communication
cost in the parallel/distributed architectures, to implement load-balance
techniques and to cope with execution environments that are both variable and
dynamic. Finally, such mechanisms will be used to build simulations on top of
unreliable cloud services.Comment: Tutorial paper published in the Proceedings of the International
Conference on High Performance Computing and Simulation (HPCS 2011). Istanbul
(Turkey), IEEE, July 2011. ISBN 978-1-61284-382-
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
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