4,412 research outputs found
Lower bounds for several online variants of bin packing
We consider several previously studied online variants of bin packing and
prove new and improved lower bounds on the asymptotic competitive ratios for
them. For that, we use a method of fully adaptive constructions. In particular,
we improve the lower bound for the asymptotic competitive ratio of online
square packing significantly, raising it from roughly 1.68 to above 1.75.Comment: WAOA 201
Towards Model Checking Real-World Software-Defined Networks (version with appendix)
In software-defined networks (SDN), a controller program is in charge of
deploying diverse network functionality across a large number of switches, but
this comes at a great risk: deploying buggy controller code could result in
network and service disruption and security loopholes. The automatic detection
of bugs or, even better, verification of their absence is thus most desirable,
yet the size of the network and the complexity of the controller makes this a
challenging undertaking. In this paper we propose MOCS, a highly expressive,
optimised SDN model that allows capturing subtle real-world bugs, in a
reasonable amount of time. This is achieved by (1) analysing the model for
possible partial order reductions, (2) statically pre-computing packet
equivalence classes and (3) indexing packets and rules that exist in the model.
We demonstrate its superiority compared to the state of the art in terms of
expressivity, by providing examples of realistic bugs that a prototype
implementation of MOCS in UPPAAL caught, and performance/scalability, by
running examples on various sizes of network topologies, highlighting the
importance of our abstractions and optimisations
Bin Packing and Related Problems: General Arc-flow Formulation with Graph Compression
We present an exact method, based on an arc-flow formulation with side
constraints, for solving bin packing and cutting stock problems --- including
multi-constraint variants --- by simply representing all the patterns in a very
compact graph. Our method includes a graph compression algorithm that usually
reduces the size of the underlying graph substantially without weakening the
model. As opposed to our method, which provides strong models, conventional
models are usually highly symmetric and provide very weak lower bounds.
Our formulation is equivalent to Gilmore and Gomory's, thus providing a very
strong linear relaxation. However, instead of using column-generation in an
iterative process, the method constructs a graph, where paths from the source
to the target node represent every valid packing pattern.
The same method, without any problem-specific parameterization, was used to
solve a large variety of instances from several different cutting and packing
problems. In this paper, we deal with vector packing, graph coloring, bin
packing, cutting stock, cardinality constrained bin packing, cutting stock with
cutting knife limitation, cutting stock with binary patterns, bin packing with
conflicts, and cutting stock with binary patterns and forbidden pairs. We
report computational results obtained with many benchmark test data sets, all
of them showing a large advantage of this formulation with respect to the
traditional ones
Intercusp Geodesics and Cusp Shapes of Fully Augmented Links
We study the geometry of fully augmented link complements in by looking
at their link diagrams. We extend the method introduced by Thistlethwaite and
Tsvietkova to fully augmented links and define a system of algebraic equations
in terms of parameters coming from edges and crossings of the link diagrams.
Combining it with the work of Purcell, we show that the solutions to these
algebraic equations are related to the cusp shapes of fully augmented link
complements. As an application we use the cusp shapes to study the
commensurability classes of fully augmented links
Exploiting a new level of DLP in multimedia applications
This paper proposes and evaluates MOM: a novel ISA paradigm targeted at multimedia applications. By fusing conventional vector ISA approaches together with more recent SIMD-like (Single Instruction Multiple Data) ISAs (such as MMX), we have developed a new matrix oriented ISA which efficiently deals with the small matrix structures typically found in multimedia applications. MOM exploits a level of DLP not reachable by neither conventional vector ISAs nor SIMD-like media ISA extensions. Our results show that MOM provides a factor of 1.3x to 4x performance improvement when compared with two different multimedia extensions (MMX and MDMX) on several kernels, which translates into up to a 50% of performance gain when measuring full applications (20% in average). Furthermore, the streaming nature of MOM provides additional advantages for executing multimedia applications, such as a very low fetch pressure or a high tolerance to memory latency, making MOM an ideal candidate for the embedded domain.Peer ReviewedPostprint (published version
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