performance & scalability experiments dr. ray huetter, cto sensorconnect reid phillips,...

Performance & Scalability Experiments

Dr. Ray Huetter, CTO SensorConnect

Reid Phillips, University of Arkansas

© 2007 SensorConnect Inc.

www.sensorconnect.com 2

Overview

Data collection and modeling of large sensor networks (EPCIS level)

A key to Return on Investment (ROI) is performance & scalability

SensorConnect builds systems to maximize success thru ROI

University of Arkansas is helping verify these systems

Today we present Background on SensorConnect technology Test results from University of Arkansas


Authors

Joe Hoag PhD Candidate University of Arkansas

Reid Phillips PhD Candidate University of Arkansas

Dr. Craig Thompson Professor and Database Chair University of Arkansas

Dr. Ray Huetter CTO SensorConnect

John Veizades VP Product Management SensorConnect


Our View of RFID

RFID & sensors augment the physical world Goal: assist people and machines to make better use

of physical objects plan & observe use, identify misuse, predict service analyze systemic cause and effect

Succeed when ROI is demonstrated coincides with maximal assistance reduction in time, space, matter & energy of processes

This is common across many domains Supply chain, ePedigree, health care, MRO, logistics,

…


Potential of RFID

RFID will make many contributions Economic, environmental, social (health)

Effectiveness & ROI will be substantial Physical optimization (more for less) Correct distribution, location and usage Safety and correctness Prevents harm (food safety) Reduction in resources, waste and errors Physical process improvements Lead to new opportunities…


Best ROI Results

Successful pilot projects are showing 5 to 10 times ROI when end-to-end visibility occurs Single, accurate timely view Across physical & logical boundaries By multiple parties

Why? Able to see what happened and when Able to reason about it, as and when it happens Discover cause and effect Use it to ones advantage or correct it Optimize: time, space, energy & matter


Control-Feedback Loop

Holistic View

Real-WorldSystems

ComputerSystem

Observe

Optimize


Maximizing ROI

Maximal ROI occurs when optimization takes into account As much fine-grained detail as possible Of as many physical objects as possible Across as many boundaries as possible In as short a time-frame as possible For the least price possible

Conversely, ROI will be limited by coarse-grained, filtered / summarized, isolated,

untimely or expensive systems


Not Possible Today

Most contemporary systems substantially constrain effectiveness & ROI Are expensive (relative to the cost of tags) Are isolated “stove-pipes” Are not real-time Do not support continuous operation Do not scale with hardware Do not cope with volume

Will be suboptimal There is a missing link here…


SensorConnect Technology

Build systems & expertise to maximize ROI Collect sensor based-data (notably RFID) of arbitrarily large

physical systems in real-time Use that data to create fine-grained models of in real-time Enable new & existing applications / systems to securely

observe, reason & optimize physical systems by querying the current state and history of the model adjust the physical system continuously in real-time

Do this by supporting Real-time write back to tags Apply rules to produce actionable alerts in real-time Pushing changes to applications as they happen Applications querying history (prior state) as required Replay history of events as they occurred


Holistic View of Physical Systemsreal world

Supply-Chain 1

Supply-Chain 2

Supply-Chain 3

SensorConnect

Model of Supply-Chain 1



History

Applications

tracking, planning, optimization, exception

management, reporting...

Events

(in-memory model)

(real-world system)

Queries


SensorConnect System Qualities

High performance > 50,000 events per second per 64-bit CPU < 100 millisecond response time per event, including write-back Balance queries with ingestion maintain detailed history; replay event history

Indefinitely scalable Support models with billions .. trillions of physical objects

Widely compatible Devices & systems

Standards compliant EPCIS (repository)

Highly reliable Continuous operation via hot failover

Secure Access & authorization controls


University of Arkansas

University of Arkansas invited to test SensorConnect core

Run tests indicative of loads of an entire supply chain

Motivations: Interested in scalable grid technology with application to sensor

networks and identity Have skills and technology to do synthetic data generation Longer term collaboration with RFID technology


Proof of Concept Experiments

Purpose Test configuration Synthetic Data Generation (SDG) Descriptions, results, and analysis


Purpose

Measure performance of the SensorConnect system while accepting data from an independent, outside source Ingestion (insertion) Balanced (concurrent ingestion and queries)


Test Configuration

ACE four node grid (provided by NSF grant #0410966)

64-bit dual processor AMD Opterons 1.6 GHz 2 GB RAM 60 GB Hard Drive 1Gbps Ethernet Rocks 4.2, Linux Kernel 2.6.9

Part of the Open Science Grid


Synthetic Data Generation (SDG)

Written in Java Accepts Synthetic Data Description Language (SDDL)

file as input Capable of generating data sequentially or in parallel Partitioning algorithms assure that the resulting data set

will be consistent regardless of the degree of parallelism used during generation

Capable of direct-to-database generation, but generating to intermediate text file is more common, and faster


Synthetic Data Description Language (SDDL) SDDL Constraint Types

Min/Max/Step Probabilistic Distribution Pool Reference: basically a parameterized dictionary lookup.

Users can define their own dictionaries Formula: field value based on mathematical formulas involving

constants and other fields Iteration: iterate through a set of values. The value set could be

a sequence of integers, a record set from a query, or a set of dictionary values

Data types supported: integer, real, string, date, time, timestamp, boolean


Synthetic Data Generation

SDG Operation Parallel processes all reference

the same SDDL file Each parallel process generates

a single text output file, containing a portion of the generated table

Database then imports the text files as data

Lack of inter-process dependencies make linear speedup a real possibility

Speed of SDG is only limited by number and speed of processors

Output is identical regardless of the number of generation processes utilized


Application:Simple RFID Supply Chain Data

Problem: Generate synthetic RFID events (“arrive” and “depart”) for 10 million unique objects traversing 100 read points (total = 2 billion events)

Row: TagID, ReaderNum, BizEvt, Timestamp

Total data generated: 86 GB (2B rows)

Reader 1

Reader 2

Reader 3

Reader 100

. . .


Experiments Run

Peak ingestion Event replay Query item Query history Query location description


Peak Ingestion Test

Not a balanced test (no queries) Used to determine the sustained insertion rate of

the SensorConnect system All available data was ingested into the system First test terminated prematurely due to a

configuration problem Second test ran to completion in approximately

01:24:00


Peak Ingestion Test


Event Replay Test

This balanced test replays the events logged by the system during a specified time interval in the order the events were received

Replay rate must be greater than or equal to the ingestion rate

Models a store-and-forward supply chain Three runs replaying 10, 20, and 20 minutes

respectively


Event Replay Test


Query Item Test

A balanced test that returns a tag’s current, or most recent, location


Query History Test

This balanced query returned the event history of a tag, or all records recording an “enter” or “leave” event for a given tag


Query Location Description Test

A balanced test that returns all tags at a given location, or position, within a supply chain


Experiment Conclusions

SensorConnect is designed for multi-core, multi-cpu System allows for an unbalanced 400,000 events/second

peak ingestion rate Balanced tests were able to query data at a rate greater

than ingestion Deployment of the SensorConnect system in a foreign

environment was accomplished with relative ease Ultimately the test results far exceeded expectations

indicating great promise for the system


Summary

Goal of RFID is to assist people and machines to make better use of physical objects

Successful projects demonstrate ROI ROI coincides with maximal assistance SensorConnect is a high-volume real-time

EPCIS system which models the real-world Tests by University of Arkansas show peak

performance >400,000 events per sec

performance & scalability experiments dr. ray huetter, cto sensorconnect reid phillips,...

Documents

roi maximal roi

roi university of arkansas

university of arkansas

investment roi

times roi

large physical systems

energy matter slide

expensive systems