Martin R. Ehmsen

Parameterized analysis of paging and list update algorithms

It is well-established that input sequences for paging and list update have locality of reference. In this paper we analyze the performance of algorithms for these problems in terms of the amount of locality in the input sequence. We define a measure for locality that is based on Denning’s working set model and express the performance of well known algorithms in term of this parameter. This introduces parameterized-style analysis to online algorithms. The idea is that rather than normalizing the performance of an online algorithm by an (optimal) offline algorithm, we explicitly express the behavior of the algorithm in terms of two more natural parameters: the size of the cache and Denning’s working set measure. This technique creates a performance hierarchy of paging algorithms which better reflects their intuitive relative strengths. Also it reflects the intuition that a larger cache leads to a better performance. We obtain similar separation for list update algorithms. Lastly, we show that, surprisingly, certain randomized algorithms which are superior to MTF in the classical model are not so in the parameterized case, which matches experimental results.

List Factoring and Relative Worst Order Analysis

Relative worst order analysis is a supplement or alternative to competitive analysis which has been shown to give results more in accordance with observed behavior of online algorithms for a range of different online problems. The contribution of this paper is twofold. As the first contribution, it adds the static list accessing problem to the collection of online problems where relative worst order analysis gives better results. List accessing is a classic data structuring problem of maintaining optimal ordering in a linked list. It is also one of the classic problems in online algorithms, in that it is used as a model problem, along with paging and a few other problems, when trying out new techniques and quality measures. As the second contribution, this paper adds the non-trivial supplementary proof technique of list factoring to the theoretical toolbox for relative worst order analysis. List factoring is perhaps the most successful technique for analyzing list accessing algorithms, reducing the complexity of the analysis of algorithms on full-length lists to lists of length two.

A theoretical comparison of LRU and LRU-K

The paging algorithm Least Recently Used Second Last Request (LRU-2) was proposed for use in database disk buffering and shown experimentally to perform better than Least Recently Used (LRU). We compare LRU-2 and LRU theoretically, using both the standard competitive analysis and the newer relative worst order analysis. The competitive ratio for LRU-2 is shown to be 2k for cache size k, which is worse than LRU’s competitive ratio of k. However, using relative worst order analysis, we show that LRU-2 and LRU are comparable in LRU-2’s favor, giving a theoretical justification for the experimental results. Many of our results for LRU-2 also apply to its generalization, Least Recently Used Kth Last Request.

Parameterized Analysis of Paging and List Update Algorithms

It is well-established that input sequences for paging and list update have locality of reference. In this paper we analyze the performance of algorithms for these problems in terms of the amount of locality in the input sequence. We define a measure for locality that is based on Denning’s working set model and express the performance of well known algorithms in terms of this parameter. This explicitly introduces parameterized-style analysis to online algorithms. The idea is that rather than normalizing the performance of an online algorithm by an (optimal) offline algorithm, we explicitly express the behavior of the algorithm in terms of two more natural parameters: the size of the cache and Denning’s working set measure. This technique creates a performance hierarchy of paging algorithms which better reflects their experimentally observed relative strengths. It also reflects the intuition that a larger cache leads to a better performance. We also apply the parameterized analysis framework to list update and show that certain randomized algorithms which are superior to MTF in the classical model are not so in the parameterized case, which matches experimental results.

List factoring and relative worst order analysis

Relative worst order analysis is a supplement or alternative to competitive analysis which has been shown to give results more in accordance with observed behavior of online algorithms for a range of different online problems. The contribution of this paper is twofold. First, it adds the static list accessing problem to the collection of online problems where relative worst order analysis gives better results. Second, and maybe more interesting, it adds the non-trivial supplementary proof technique of list factoring to the theoretical toolbox for relative worst order analysis.

Better bounds on online unit clustering

Unit Clustering is the problem of dividing a set of points from a metric space into a minimal number of subsets such that the points in each subset are enclosable by a unit ball. We continue work initiated by Chan and Zarrabi-Zadeh on determining the competitive ratio of the online version of this problem. For the one-dimensional case, we develop a deterministic algorithm, improving the best known upper bound of 7/4 by Epstein and van Stee to 5/3. This narrows the gap to the best known lower bound of 8/5 to only 1/15. Our algorithm automatically leads to improvements in all higher dimensions as well. Finally, we strengthen the deterministic lower bound in two dimensions and higher from 2 to 13/6.

Comparing First-Fit and Next-Fit for online edge coloring

We study the performance of the algorithms First -Fit and Next -Fit for two online edge coloring problems. In the min-coloring problem, all edges must be colored using as few colors as possible. In the max-coloring problem, a fixed number of colors is given, and as many edges as possible should be colored. Previous analysis using the competitive ratio has not separated the performance of First -Fit and Next -Fit, but intuition suggests that First -Fit should be better than Next -Fit. We compare First -Fit and Next -Fit using the relative worst-order ratio, and show that First -Fit is better than Next -Fit for the min-coloring problem. For the max-coloring problem, we show that First -Fit and Next -Fit are not strictly comparable, i.e., there are graphs for which First -Fit is significantly better than Next -Fit and graphs where Next -Fit is slightly better than First -Fit.

Comparing First-Fit and Next-Fit for Online Edge Coloring

We study the performance of the algorithms First-Fit and Next-Fit for two online edge coloring problems. In the min-coloring problem, all edges must be colored using as few colors as possible. In the max-coloring problem, a fixed number of colors is given, and as many edges as possible should be colored. Previous analysis using the competitive ratio has not separated the performance of First-Fit and Next-Fit, but intuition suggests that First-Fit should be better than Next-Fit. We compare First-Fit and Next-Fit using the relative worst order ratio, and show that First-Fit is better than Next-Fit for the min-coloring problem. For the max-coloring problem, we show that First-Fit and Next-Fit are not strictly comparable, i.e., there are graphs for which First-Fit is better than Next-Fit and graphs where Next-Fit is slightly better than First-Fit.

A TECHNIQUE FOR EXACT COMPUTATION OF PRECOLORING EXTENSION ON INTERVAL GRAPHS

Inspired by a real-life application, we investigate the computationally hard problem of extending a precoloring of an interval graph to a proper coloring under some bound on the number of available colors. We are interested in quickly determining whether or not such an extension exists on instances occurring in practice in connection with campsite bookings on a campground. A naive exhaustive search does not terminate in reasonable time. We have formulated a new approach which moves the computation time within the usable range on all the data samples available to us.