A self-adaptable query allocation framework for distributed information systems

In large-scale distributed information systems, where participants are autonomous and have special interests for some queries, query allocation is a challenge. Much work in this context has focused on distributing queries among providers in a way that maximizes overall performance (typically throughput and response time). However, preserving the participants’ interests is also important. In this paper, we make the following contributions. First, we provide a model to define the participants’ perception of the system regarding their interests and propose measures to evaluate the quality of query allocation methods. Then, we propose a framework for query allocation called Satisfaction-based Query Load Balancing (SQLB, for short), which dynamically trades consumers’ interests for providers’ interests based on their satisfaction. Finally, we compare SQLB, through experimentation, with two important baseline query allocation methods, namely Capacity based and Mariposa-like. The results demonstrate that SQLB yields high efficiency while satisfying the participants’ interests and significantly outperforms the baseline methods. Work partially funded by ARA “Massive Data” of the French ministry of research (Respire project) and the European Strep Grid4All project.