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Embedded Database Benchmark

Explore the importance of benchmarking embedded databases in IoT, with a focus on performance, cost, and energy consumption. Investigate various workloads and metrics to understand the challenges and future research areas.

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Embedded Database Benchmark

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  1. Embedded Database Benchmark Team CodeBlooded

  2. Internet of Things • “As the number of interconnected platforms continues to multiply, vendors and customers increasingly require an impartial means of comparing performance, cost-of-ownership and energy consumption across a widening array of hardware and software systems.” • TPC-IoT recently formed (Aug 2015) • Benchmarking already being done on the analytics side - IotaBench • Embedded databases will play a crucial role in the IoT. Benchmarking them w.r.t. IoT will be an important factor in this. • In IoT itself, different workloads possible

  3. Example

  4. Embedded Databases in IoT • Not overloading backend • Intermediate filtering, summarize data • No continuous connection required to the backend database • Low latency • Triggering actions in case of abnormal readings immediately. • Light-weight analytics

  5. Workloads Under Investigation • RFID middleware • Read only queries for cache. • Redundancy elimination and data quality. • Min-max queries and aggregate queries. • HOPE 2008 data set. • Sensors and Accelerometer • Write dominant queries. • Air pollution sensor data set. • Smart Thermostats • Write and update queries. • Spark Thermostat – open source.

  6. Future Workloads To Research • Set-top boxes • Wearable technologies • Smart Devices

  7. Benchmark Architecture

  8. Metrics • Runtime • Latency • Throughput • CPU Utilization • Memory Usage • SIGAR API to observer OS level metrics

  9. Challenges and Next Steps • Open data sets are available but actual data logs are needed to determine queries. • Decide number of test runs per workload • Implement time series generator for inserts • Iterative development of tests and workloads.

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