468 research outputs found
Online Bin Packing with Advice
We consider the online bin packing problem under the advice complexity model
where the 'online constraint' is relaxed and an algorithm receives partial
information about the future requests. We provide tight upper and lower bounds
for the amount of advice an algorithm needs to achieve an optimal packing. We
also introduce an algorithm that, when provided with log n + o(log n) bits of
advice, achieves a competitive ratio of 3/2 for the general problem. This
algorithm is simple and is expected to find real-world applications. We
introduce another algorithm that receives 2n + o(n) bits of advice and achieves
a competitive ratio of 4/3 + {\epsilon}. Finally, we provide a lower bound
argument that implies that advice of linear size is required for an algorithm
to achieve a competitive ratio better than 9/8.Comment: 19 pages, 1 figure (2 subfigures
On the List Update Problem with Advice
We study the online list update problem under the advice model of
computation. Under this model, an online algorithm receives partial information
about the unknown parts of the input in the form of some bits of advice
generated by a benevolent offline oracle. We show that advice of linear size is
required and sufficient for a deterministic algorithm to achieve an optimal
solution or even a competitive ratio better than . On the other hand, we
show that surprisingly two bits of advice are sufficient to break the lower
bound of on the competitive ratio of deterministic online algorithms and
achieve a deterministic algorithm with a competitive ratio of . In this
upper-bound argument, the bits of advice determine the algorithm with smaller
cost among three classical online algorithms, TIMESTAMP and two members of the
MTF2 family of algorithms. We also show that MTF2 algorithms are
-competitive
Online Bin Packing with Advice
We consider the online bin packing problem under the advice complexity model where the "online constraint" is relaxed and an algorithm receives partial information about the future requests. We provide tight upper and lower bounds for the amount of advice an algorithm needs to achieve an optimal packing. We also introduce an algorithm that, when provided with log(n)+o(log(n)) bits of advice, achieves a competitive ratio of 3/2 for the general problem. This algorithm is simple and is expected to find real-world applications. We introduce another algorithm that receives 2n+o(n) bits of advice and achieves a competitive ratio of 4/3+e. Finally, we provide a lower bound argument that implies that advice of linear size is required for an algorithm to achieve a competitive ratio better than 9/8
Relative Interval Analysis of Paging Algorithms on Access Graphs
Access graphs, which have been used previously in connection with competitive
analysis and relative worst order analysis to model locality of reference in
paging, are considered in connection with relative interval analysis. The
algorithms LRU, FIFO, FWF, and FAR are compared using the path, star, and cycle
access graphs. In this model, some of the expected results are obtained.
However, although LRU is found to be strictly better than FIFO on paths, it has
worse performance on stars, cycles, and complete graphs, in this model. We
solve an open question from [Dorrigiv, Lopez-Ortiz, Munro, 2009], obtaining
tight bounds on the relationship between LRU and FIFO with relative interval
analysis.Comment: IMADA-preprint-c
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