Dynamic Aggregate: An Elastic Framework for QoS-Aware Distributed Processing of RFID Data on Enterprise Hierarchy

Enterprise RFID data management is highly challenging not only because of the huge volume of data from distributed sources, but particularly because of the dynamic nature of the reader inputs. With user-designated quality of service (QoS) requirements, the data management system must be able to dynamically detect the status changes of the RFID inputs and adjust the processing strategies for continuously maintaining the desired level of QoS. We propose a QoS-aware framework for modeling the enterprise data service problem, and for on-line adaptive processing of distributed RFID data streams. The data processing structure is modeled as a hierarchy of aggregation nodes in accordance with the structure of an organization. Leaf nodes correspond to the RFID streaming inputs. A set of aggregation/deaggregation operations is devised to adjust the processing granularity level based on QoS dynamics. A QoS constrained query issued at any aggregation node is parsed into an aggregation subtree rooted at that node. For QoS-aware processing of the query, several algorithms are designed to dynamically apply proper aggregation/deaggregation operations on selected nodes for raising or lowering the granularity levels or changing the aggregation methods. The goal is to continuously maintain the desired level of QoS under constant variation of the streaming data volume. Prototype development and extensive simulation show that our framework and techniques can handle highly varied RFID streaming inputs and continuously satisfy the QoS constraints.