Direct Backtracking: An Advanced Adaptation Algorithm for Pervasive Applications

Abstract

The adaptation of pervasive applications is in the focus of many current research projects. While decentralized adaptation is mandatory in infrastructureless ad hoc scenarios, most realistic pervasive application scenarios are situated in heterogeneous environments where additional computation power of resource-rich devices can be exploited. Therefore, we propose a hybrid approach to application configuration that applies centralized as well as decentralized configuration as appropriate in the given environment. In this paper we introduce the Direct Backtracking algorithm that represents an efficient way for centralized configuration and adaptation of pervasive applications in heterogeneous scenarios. In our evaluation, we show that compared with other centralized algorithms, our algorithm significantly reduces adaptation latency as it avoids unnecessary adaptations that arise in many other backtracking algorithms, without significantly increasing memory waste. This is achieved by introducing two mechanisms: 1. proactive backtracking avoidance and 2. intelligent backtracking.