TRANS outperforms MTF for two special types of request sequences without locality of reference

Various list accessing algorithms have been proposed in the literature and their performances have been analyzed theoretically and experimentally. Move-To-Front (MTF) and Transpose (TRANS) are two well known primitive list accessing algorithms. MTF has been proved to be the best performing online algorithm till date in the literature for real life inputs and practical applications with locality of reference. It has been shown that when storage space is extremely limited and pointers for lists cannot be used, then array implementation of TRANS gives efficient reorganization. Use of MTF is extensive in the literature whereas, the use of TRANS is rare. As mentioned as an open problem in literature, direct bounds on the behavior and performance of various list accessing algorithms are needed to allow realistic comparisons. Since it has been shown that no single optimal permutation algorithm exists, it becomes necessary to characterize the circumstances that indicate the advantage in using a particular list accessing algorithm. Motivated by above challenging research issue, in this paper we have made an analytical study for evaluating the performance of TRANS list accessing algorithm using two special types of request sequences without locality of reference. We have compared the performance of TRANS with MTF and observed that TRANS outperforms MTF for these considered types of request sequences.Comment: 9 Pages, Proceedings of International Conference on Communication, Computing and Security (ICCCS)-2012, India. http://www.sciencedirect.com/science/article/pii/S221201731200612

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 $15/14$. On the other hand, we show that surprisingly two bits of advice are sufficient to break the lower bound of $2$ on the competitive ratio of deterministic online algorithms and achieve a deterministic algorithm with a competitive ratio of $5/3$. 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 $2.5$-competitive

On utilizing an enhanced object partitioning scheme to optimize self-organizing lists-on-lists

Author's accepted manuscript.This is a post-peer-review, pre-copyedit version of an article published in Evolving Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s12530-020-09327-4.acceptedVersio

Samoupravující seznamy

Samoupravující seznamy Samoupravující seznamy jsou datové struktury sloužící k rychlému vyhledávání za předpokladu, že některé prvky v nich uložené jsou vyhledávány častěji než jiné, přičemž pravděpodobnosti přístupu k jednotlivým prvkům obecně nejsou předem známy. Efektivnějšího vyhledávání je dosaženo použitím různých permutačních pravidel, která průběžně mění uspořádání seznamu tak, aby častěji vyhledávané prvky byly blíže k jeho začátku. V této práci je uveden přehled známých algoritmů pro řešení tohoto problému (s uvedením teoretických výsledků o jejich složitosti, jsou-li známy) a experimentální studie o jejich chování (s využitím vlastních nebo volně dostupných implementací a programových prostředků pro generování vstupních dat, testování algoritmů a zpracování výsledků experimentů).Self-organizing linear lists Self-organizing linear lists are data structures for fast search, provided that certain elements stored in them are searched more frequently than others, while the probability of access to individual elements is generally not known in advance. Efficient search is achieved using different permutation rules that keep changing the list structure so that the more frequently searched elements are closer to the beginning. This thesis gives an overview of known algorithms for solving this problem (with the theoretical results about their complexity, if they are known), and experimental study of their behavior (using its own or freely available implementations and software for generating input data, testing algorithms and processing the results of experiments).Department of Distributed and Dependable SystemsKatedra distribuovaných a spolehlivých systémůFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

On the competitive theory and practice of online list accessing algorithms

This paper concerns the online list accessing problem. In the first part of the paper we present two new families of list accessing algorithms. The first family is of optimal, 2-competitive, deterministic online algorithms. This family, called the mri (move-to-recent-item) family, includes as members the well known move-to-front (MTF) algorithm, and the recent, more "conservative" algorithm TIMESTAMP due to Albers. So far move-to-front and TIMESTAMP were the only algorithms known to be optimal in terms of their competitive ratio. This new family contains a sequence of algorithms fA(i)g i1 where A(1) is equivalent to TIMESTAMP and the limit element A(1) is MTF. Further, in this class, for each i, the algorithm A(i) is more conservative than algorithm A(i + 1) in the sense that it is more reluctant to move an accessed item to the front, thus giving a gradual transition from the conservative TIMESTAMP to the "reckless" MTF. The second new family , called the pri (pass-recent-item) family is also infinite and contains TIMESTAMP; We show that most algorithms in this family attain a competitive ratio of 3

Alternative Approaches for Analysis of Bin Packing and List Update Problems

In this thesis we introduce and evaluate new algorithms and models for the analysis of online bin packing and list update problems. These are two classic online problems which are extensively studied in the literature and have many applications in the real world. Similar to other online problems, the framework of competitive analysis is often used to study the list update and bin packing algorithms. Under this framework, the behavior of online algorithms is compared to an optimal offline algorithm on the worst possible input. This is aligned with the traditional algorithm theory built around the concept of worst-case analysis. However, the pessimistic nature of the competitive analysis along with unrealistic assumptions behind the proposed models for the problems often result in situations where the existing theory is not quite useful in practice. The main goal of this thesis is to develop new approaches for studying online problems, and in particular bin packing and list update, to guide development of practical algorithms performing quite well on real-world inputs. In doing so, we introduce new algorithms with good performance (not only under the competitive analysis) as well as new models which are more realistic for certain applications of the studied problems. For many online problems, competitive analysis fails to provide a theoretical justification for observations made in practice. This is partially because, as a worst-case analysis method, competitive analysis does not necessarily reflect the typical behavior of algorithms. In the case of bin packing problem, the Best Fit and First Fit algorithms are widely used in practice. There are, however, other algorithms with better competitive ratios which are rarely used in practice since they perform poorly on average. We show that it is possible to optimize for both cases. In doing so, we introduce online bin packing algorithms which outperform Best Fit and First Fit in terms of competitive ratio while maintaining their good average-case performance. An alternative for analysis of online problems is the advice model which has received significant attention in the past few years. Under the advice model, an online algorithm receives a number of bits of advice about the unrevealed parts of the sequence. Generally, there is a trade-off between the size of the advice and the performance of online algorithms. The advice model generalizes the existing frameworks in which an online algorithm has partial knowledge about the input sequence, e.g., the access graph model for the paging problem. We study list update and bin packing problems under the advice model and answer several relevant questions about the advice complexity of these problems. Online problems are usually studied under specific settings which are not necessarily valid for all applications of the problem. As an example, online bin packing algorithms are widely used for server consolidation to minimize the number of active servers in a data center. In some applications, e.g., tenant placement in the Cloud, often a `fault-tolerant' solution for server consolidation is required. In this setting, the problem becomes different and the classic algorithms can no longer be used. We study a fault-tolerant model for the bin packing problem and analyze algorithms which fit this particular application of the problem. Similarly, the list update problem was initially proposed for maintaining self-adjusting linked lists. However, presently, the main application of the problem is in the data compression realm. We show that the standard cost model is not suitable for compression purposes and study a compression cost model for the list update problem. Our analysis justifies the advantage of the compression schemes which are based on Move-To-Front algorithm and might lead to improved compression algorithms