A New Proposed Cost Model for List Accessing Problem using Buffering

There are many existing well known cost models for the list accessing problem. The standard cost model developed by Sleator and Tarjan is most widely used. In this paper, we have made a comprehensive study of the existing cost models and proposed a new cost model for the list accessing problem. In our proposed cost model, for calculating the processing cost of request sequence using a singly linked list, we consider the access cost, matching cost and replacement cost. The cost of processing a request sequence is the sum of access cost, matching cost and replacement cost. We have proposed a novel method for processing the request sequence which does not consider the rearrangement of the list and uses the concept of buffering, matching, look ahead and flag bit.Comment: 05 Pages, 2 figure

Semi-online Scheduling with Lookahead

The knowledge of future partial information in the form of a lookahead to design efficient online algorithms is a theoretically-efficient and realistic approach to solving computational problems. Design and analysis of semi-online algorithms with extra-piece-of-information (EPI) as a new input parameter has gained the attention of the theoretical computer science community in the last couple of decades. Though competitive analysis is a pessimistic worst-case performance measure to analyze online algorithms, it has immense theoretical value in developing the foundation and advancing the state-of-the-art contributions in online and semi-online scheduling. In this paper, we study and explore the impact of lookahead as an EPI in the context of online scheduling in identical machine frameworks. We introduce a $k$-lookahead model and design improved competitive semi-online algorithms. For a $2$-identical machine setting, we prove a lower bound of $\frac{4}{3}$ and design an optimal algorithm with a matching upper bound of $\frac{4}{3}$ on the competitive ratio. For a $3$-identical machine setting, we show a lower bound of $\frac{15}{11}$ and design a $\frac{16}{11}$-competitive improved semi-online algorithm.Comment: 14 pages, 1 figur