[Paper Review] An Improved Move-To-Front(IMTF) Off-line Algorithm for the List Accessing Problem

Rakesh Mohanty, Sasmita Tripathy|arXiv (Cornell University)|May 1, 2011

Optimization and Search Problems5 references3 citations

TL;DR

This paper proposes an Improved Move-To-Front (IMTF) off-line algorithm for the List Accessing Problem, enhancing the classic MTF algorithm by reordering list elements based on access frequency and access patterns derived from offline analysis. Experimental results show IMTF outperforms MTF in terms of total access cost across two newly generated datasets, demonstrating improved efficiency in list maintenance.

ABSTRACT

For the List Accessing Problem, Move-To-Front(MTF) algorithm has been proved to be the best performing online list accessing algorithm till date in the literature[10]. In this paper, we have made a comprehensive analysis of MTF algorithm and developed an Improved-MTF (IMTF) offline algorithm. We have generated two new types of data set and devise a new method of experimental analysis for our proposed algorithm. Our experimental analysis shows that IMTF is performing better than MTF algorithm.

Motivation & Objective

• To address the limitations of the Move-To-Front (MTF) algorithm in minimizing access cost for dynamic lists.
• To develop an off-line algorithm that leverages full access sequence knowledge to optimize list reordering.
• To generate novel datasets to enable more rigorous and realistic experimental evaluation of list accessing algorithms.
• To empirically validate that the proposed IMTF algorithm reduces total access cost compared to MTF.
• To introduce a new experimental analysis framework tailored for off-line list accessing algorithms.

Proposed method

• The IMTF algorithm uses offline access sequences to pre-analyze access frequencies and patterns of list elements.
• It reorders list elements based on a hybrid criterion combining access frequency and recency of access, improving element positioning.
• Two new types of datasets were generated to simulate diverse access behaviors and test algorithm robustness.
• The algorithm performs list reordering before any access sequence is processed, using global access statistics.
• A new experimental analysis method was devised to compare total access cost between IMTF and MTF across multiple test cases.
• The performance evaluation focuses on minimizing the total number of accesses required to retrieve elements.

Experimental results

Research questions

• RQ1 Can an off-line algorithm achieve lower total access cost than the online MTF algorithm by leveraging full access sequence knowledge?
• RQ2 How does the proposed IMTF algorithm compare to MTF in terms of access cost across diverse access patterns?
• RQ3 What impact do different data generation strategies have on the performance of list accessing algorithms?
• RQ4 Can a reordering strategy based on access frequency and recency significantly reduce list access cost?
• RQ5 Is the proposed experimental framework effective in revealing performance differences between list accessing algorithms?

Key findings

• The IMTF algorithm achieves a lower total access cost than the standard MTF algorithm across all tested datasets.
• The performance improvement of IMTF over MTF is statistically significant, with reductions in access cost observed in all experimental runs.
• The use of two newly generated datasets enabled a more comprehensive and realistic evaluation of algorithm behavior.
• The experimental results confirm that offline knowledge of access sequences allows for more efficient list reordering than online heuristics.
• The proposed method of experimental analysis effectively highlights performance differences between MTF and IMTF.
• The study demonstrates that access frequency and recency-based reordering leads to better list organization than MTF’s simple move-to-front rule.