Meeting Data Sharing Needs of Heterogeneous Distributed Users
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The fast growth of wireless networking and mobile computing devices has enabled us to access information from anywhere at any time. However, varying user needs and system resource constraints are two major heterogeneity factors that pose a challenge to information sharing systems. For instance, when a new information item is produced, different users may have different requirements for when the new value should become visible. The resources that each device can contribute to such information sharing applications also vary. Therefore, how to enable information sharing across computing platforms with varying resources to meet different user demands is an important problem for distributed systems research. In this thesis, we address the heterogeneity challenge faced by such systems. We assume that shared information is encapsulated in distributed objects, and we use object replication to increase system scalability and robustness, which introduces the consistency problem. Many consistency models have been proposed in recent years but they are either too strong and do not scale very well, or too weak to meet many users' requirements. We propose a Mixed Consistency (MC) model as a solution. We introduce an access constraints based approach to combine both strong and weak consistency models together. We also propose a MC protocol that combines existing implementations together with minimum modifications. It is designed to tolerate crash failures and slow processes/communication links in the system. We also explore how the heterogeneity challenge can be addressed in the transportation layer by developing an agile dissemination protocol. We implement our MC protocol on top of a distributed publisher-subscriber middleware, Echo. We finally measure the performance of our MC implementation. The results of the experiments are consistent with our expectations. Based on the functionality and performance of mixed consistency protocols, we believe that this model is effective in addressing the heterogeneity of user requirements and available resources in distributed systems.