RIoTBench: A Real-time IoT Benchmark for Distributed Stream Processing Platforms
Anshu Shukla, Shilpa Chaturvedi and Yogesh Simmhan, Concurrency and Computation: Practice and Experience, Volume 29, Issue 21, 2017
Online: http://onlinelibrary.wiley.com/doi/10.1002/cpe.4257/abstract
Pre-print: https://arxiv.org/abs/1701.08530
IoT Micro-benchmarks
| Annotate | ANN | Parse | Transform | 1:1 | No |
|---|---|---|---|---|---|
| CsvToSenML | C2S | Parse | Transform | 1:1 | No |
| SenML Parsing | SML | Parse | Transform | 1:1 | No |
| XML Parsing | XML | Parse | Transform | 1:1 | No |
| Bloom Filter | BLF | Filter | Filter | 1:0/1 | No |
| Range Filter | RGF | Filter | Filter | 1:0/1 | No |
| Accumlator | ACC | Statistical | Aggregate | N:1 | Yes |
| Average | AVG | Statistical | Aggregate | N:1 | Yes |
| Distinct Appox. Count | DAC | Statistical | Transform | 1:1 | Yes |
| Kalman Filter | KAL | Statistical | Transform | 1:1 | Yes |
| Second Order Moment | SOM | Statistical | Transform | 1:1 | Yes |
| Decision Tree Classify | DTC | Predictive | Transform | 1:1 | No |
| Decision Tree Train | DTT | Predictive | Aggregate | N:1 | No |
| Interpolation | INP | Predictive | Transform | 1:1 | Yes |
| Multi-var. Linear Reg. | MLR | Predictive | Transform | 1:1 | No |
| Multi-var. Linear Reg. Train | MLT | Predictive | Aggregate | N:1 | No |
| Sliding Linear Regression | SLR | Predictive | Flat Map | N:M | Yes |
| Azure Blob D/L | ABD | IO | Source/Transform | 1:1 | No |
| Azure Blob U/L | ABU | IO | Sink | 1:1 | No |
| Azure Table Lookup | ATL | IO | Source/Transform | 1:1 | No |
| Azure Table Range | ATR | IO | Source/Transform | 1:1 | No |
| Azure Table Insert | ATI | IO | Transform | 1:1 | No |
| MQTT Publish | MQP | IO | Sink | 1:1 | No |
| MQTT Subscribe | MQS | IO | Sink | 1:1 | No |
| Local Files Zip | LZP | IO | Sink | 1:1 | No |
| Remote Files Zip | RZP | IO | Sink | 1:1 | No |
| MultiLine Plot | PLT | Visualization | Transform | 1:1 | No |
Application benchmarks
| App. Name | Code |
|---|---|
| Extraction, Transform and Load dataflow | ETL |
| Statistical Summarization dataflow | STATS |
| Model Training dataflow | TRAIN |
| Predictive Analytics dataflow | PRED |
Extraction, Transform and Load dataflow (ETL)
Statistical Summarization dataflow (STATS)
Predictive Analytics dataflow (PRED)
Model Training dataflow (TRAIN)
- Steps to run benchmark's
- Once cloned run
mvn clean compile package -DskipTests -
To submit jar microbenchmarks-
storm jar <stormJarPath> in.dream_lab.bm.stream_iot.storm.topo.micro.MicroTopologyDriver C <TopoName> <inputDataFilePath used by CustomEventGen and spout> PLUG-<expNum> <rate as 1x,2x> <outputLogPath> <tasks.properties File Path> <microTaskName> - For microTaskName please refer switch logic in MicroTopologyFactory class in package "in.dream_lab.bm.stream_iot.storm.topo.micro"
Please refer the paper for detailed info - https://arxiv.org/abs/1701.08530
Please cite as:
- RIoTBench: A Real-time IoT Benchmark for Distributed Stream Processing Platforms, Anshu Shukla, Shilpa Chaturvedi and Yogesh Simmhan, Concurrency and Computation: Practice and Experience, Volume 29, Issue 21, 2017, doi:10.1002/cpe.4257
This article is an extended version of:
- Benchmarking Distributed Stream Processing Platforms for IoT Applications, Anshu Shukla and Yogesh Simmhan, TPC Technology Conference on Performance Evaluation & Benchmarking (TPCTC), 2016.