Toward A Method of Grouping Server Data Fragments for Improving Scalability in Intermittently Synchronized Databases

Abstract

We consider the class of mobile computing applications with periodically connected clients. These clients wish to share data; however, due to the expense of mobile communication, they only connect periodically -- and not necessarily synchronously -- to a common network. Traditionally, a continuously-connected server, containing an aggregate of client data, facilitates sharing amongst clients by allowing the clients to upload local updates and download updates submitted by other clients. The server computes and transmits these updates on a client-by-client basis; consequently, the complexity of these operations is on the order of the number of clients, limiting scalability. Recent research proposes exploiting client data overlap by grouping updates according to how the data is shared amongst clients (data-centric) instead of on a client-by-client basis (client-centric). Each client downloads updates for the relevant set of groups. By grouping, update operation distribution is computed only once per group, irrespective of the number of clients downloading a particular group's updates. Additionally, we may gain bandwidth scalability by employing broadcast delivery since, unlike the case in the per-client approach, multiple clients may be interested in a group's updates. Clearly, group composition directly affects the scalability of this approach. Given a relative cost of resources such as server processing, bandwidth, and storage space, we focus on developing a group derivation approach that significantly improves the scalability of the resources. We construct a formal specification of this problem and discuss the intractability of an optimal solution. Based on observations from the specification, we derive a heuristically based approach and evaluate its efficacy with respect to the client-centric approach. We run experiments on an implemented system that demonstrates that as the amount of overlap increases between client subscriptions, the data-centric approach with groups generated by our heuristic-based algorithm yields significant cost reduction when compared to the traditional client-centric approach.