Providing Scalable Data Services in Ubiquitous Networks

TL;DR

This paper explores algorithms for dynamic topology reconfiguration in ubiquitous data networks, demonstrating that a metrical task system-based approach achieves similar performance to greedy methods but with lower reconfiguration costs.

Contribution

It introduces and compares a metrical task system-based algorithm for topology reconfiguration, offering a cost-effective alternative to traditional greedy algorithms.

Findings

Metrical task system algorithm has comparable performance to greedy algorithms.

The metrical task system approach results in lower reconfiguration costs.

Experiments validate the efficiency of the proposed algorithm.

Abstract

Topology is a fundamental part of a network that governs connectivity between nodes, the amount of data flow and the efficiency of data flow between nodes. In traditional networks, due to physical limitations, topology remains static for the course of the network operation. Ubiquitous data networks (UDNs), alternatively, are more adaptive and can be configured for changes in their topology. This flexibility in controlling their topology makes them very appealing and an attractive medium for supporting "anywhere, any place" communication. However, it raises the problem of designing a dynamic topology. The dynamic topology design problem is of particular interest to application service providers who need to provide cost-effective data services on a ubiquitous network. In this paper we describe algorithms that decide when and how the topology should be reconfigured in response to a change in the data communication requirements of the network. In particular, we describe and compare a greedy algorithm, which is often used for topology reconfiguration, with a non-greedy algorithm based on metrical task systems. Experiments show the algorithm based on metrical task system has comparable performance to the greedy algorithm at a much lower reconfiguration cost.