Aadhaar is the world’s largest biometric database with a billion records, being compiled as an identity platform to deliver social services to residents of India. Aadhaar processes streams of biometric data as residents are enrolled and updated. Besides ( )1 million enrollments and updates per day, up to 100 million daily biometric authentications are expected during delivery of various public services. These form critical Big Data applications, with large volumes and high velocity of data. Here, we propose a stream processing workload, based on the Aadhaar enrollment and Authentication applications, as a Big Data benchmark for distributed stream processing systems. We describe the application composition, and characterize their task latencies and selectivity, and data rate and size distributions, based on real observations. We also validate this benchmark on Apache Storm using synthetic streams and simulated application logic. This paper offers a unique glimpse into an operational national identity infrastructure, and proposes a benchmark for “fast data” platforms to support such eGovernance applications.
The @cite_7 simulates a highway toll system for motor vehicles with variable tolling, and was meant to compare Data Stream Management Systems (DSMS) with Database Management Systems (DBMS). The input to the benchmark is from a traffic model that has variable number of vehicles, but each emitting tuples at a uniform rate and with the same type. So while the input rate is variable, each message is of a fixed, small size. The metrics for evaluation are the response time and accuracy of the query, and the maximum sustained rate within a specified response time, but it does not consider resource utilization. While not developed for distributed stream processing systems, which deal with opaque messages and user logic, it can be adopted to validate DSPS. Our proposed benchmark is particularly tuned for DSPS and simulates the behavior of a real-world eGovernance workload, with variable message sizes and resource efficiency as an additional metric.
@cite_7
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This paper specifies the Linear Road Benchmark for Stream Data Management Systems (SDMS). Stream Data Management Systems process streaming data by executing continuous and historical queries while producing query results in real-time. This benchmark makes it possible to compare the performance characteristics of SDMS' relative to each other and to alternative (e.g., Relational Database) systems. Linear Road has been endorsed as an SDMS benchmark by the developers of both the Aurora [1] (out of Brandeis University, Brown University and MIT) and STREAM [8] (out of Stanford University) stream systems. Linear Road simulates a toll system for the motor vehicle expressways of a large metropolitan area. The tolling system uses "variable tolling" [6, 11, 9]: an increasingly prevalent tolling technique that uses such dynamic factors as traffic congestion and accident proximity to calculate toll charges. Linear Road specifies a variable tolling system for a fictional urban area including such features as accident detection and alerts, traffic congestion measurements, toll calculations and historical queries. After specifying the benchmark, we describe experimental results involving two implementations: one using a commercially available Relational Database and the other using Aurora. Our results show that a dedicated Stream Data Management System can outperform a Relational Database by at least a factor of 5 on streaming data applications.