Transcript
Page 1: Internet of Things Scalability

Internet of Things Scalability: Analyzing the Bottlenecks and Proposing Alternatives

Márcio Miguel Gomes Rodrigo da Rosa Righi

Cristiano André da Costa

Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos - Unisinos – Brazil

Corresponding address: [email protected]

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Agenda •  Introduction

•  Theoretical basis

•  Research question

•  Related works

•  Methodology and justification

•  Proposed model

•  Proposed algorithms

•  Conclusion and Future Works

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Introduction

•  Internet of Things - IoT

•  Objects, animals or people equipped with unique identifiers

•  Ability to automatically transfer data over a network

•  Without the need for human intervention

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Application of IoT on health area

Internet

Healthcare  server

Caregiver  or  physician

Emergency  services  or  Medical  researcher

Database

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Information

Assessment,  assistance,  treatment

Inertial  sensor

Pulse  and  blood  

pressure  sensor

Oximetry  Sensor

Source: adaptated from Jiang et al (2008)

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Perspectives

•  Study by IDC (International Data Corporation) - 2013

•  Digital universe is doubling in size every two years (4.4 trillion gigabytes in 2013)

•  Might be multiplied by 10 to 2020 (44 trillion gigabytes in just 7 years)

•  BRICs with the largest volume of data in 2020

•  30 billion devices connected to the Internet in 2020

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Typical architecture of an RFID system Users  and  

Applications

Data  Storage

RFID  Middlewaresand

Local  Applications

RFID  Readers

Antennas

RFID  Tags

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Theoretical Basis

•  RFID Middleware –  Mediation of communication

between business systems and RFID hardware infrastructure

–  Collecting, filtering, aggregation, storage and availability of data in a standardized way

Source: Al Jaroodi, Aziz and Mohamed (2009)

Service  Management

Data  Management

Device  Management

Typical structure ofRFID Middlewares

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Theoretical Basis

•  EPCglobal Architecture Framework

•  Set of interrelated standards for hardware, software and data interfaces

•  LLRP – Low Level Reader Protocol

•  ALE – Application Level Events

•  EPCIS – Electronic Product Code Information Services

Source: http://www.gs1.org/gsmp/kc/epcglobal

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Research Question

•  How would be a computer architecture and algorithms for managing scalability of an Internet of Things EPCglobal middleware, in order to guarantee the performance from the dynamic demand of applications and RFID sensors?

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Related Work

•  Study of RFID middlewares

•  Listing the most important features, applications, and used technologies for identifying how they manage load balancing and scalability

Middleware   MARM   Fosstrak   WinRFID   Hybrid   RF2ID   LIT   REFiLL  Scalability   Multi-agents

system  Dedicated server, simulation mode and embedded in RFID reader  

Distributed modules  

Peer-to-peer multi-ring network  

Virtual paths between virtual and physical readers  

Readers management interface  

Light programmable framework  

Load Balance   Not addressed  

Readers subscription  

Not addressed  

Peer-to-peer systems  

Path management  

State-based execution model  

Not addressed  

EPCglobal   No   Yes   No   No   No   Yes   Yes  

Comparison between RFID middlewares

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Choosing the RFID middleware

•  EPCglobal compliant

•  Application for general use

•  Availability of access to the source code

•  Possibility of modular deployment, in a distributed way

•  Chosen middleware: Fosstrak

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Methodology and Justification

•  MIB: Micro Benchmark for Evaluating Internet of Things Middlewares

Source: Developed by the author

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Methodology and Justification

•  Is there any situation of system failure?

•  What is the relation between the applied load and resource consumption?

•  What is the system behavior when it reaches CPU usage, network or memory limits?

•  Is it possible to identify overload or underutilization thresholds with this assessment methodology?

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Methodology and Justification

•  Applying MIB in Fosstrak: this work focus in the current model and in the future in the proposed model

•  RFID data load: 4 readers with 0, 1 or 4 active tags, resulting in 0, 4 or 16 data per cycle

•  Parallel queries load: 1 to 512 threads (20 to 29 requests)

•  Serial queries load: 1 to 16 queries (20 to 24 requests)

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Methodology and Justification ALE module average behavior – Current model

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Methodology and Justification EPCIS module average behavior – Current model

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Proposed Model Current model Proposed model

Nuvem

User  ApplicationsApp  1 App  2 App  “n”

ALE  multicore  multithread

RFID  Reader  1 RFID  Reader  2 RFID  Reader  “n”

NoSQL  P2PDatabase

EPCIS  in  a  cloud

Capture  Interface  (HTTP)o  o  oVM VM

Query  Interface  (SOAP)o  o  oVM VM

Capturing  ApplicationsApp  1 App  2 App  “n”

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Proposed Model

•  Parallel processing for the ALE Module (multithreaded and multicore)

•  Split EPCIS module to meet different demands (reading and writing operations)

•  Scalability and elasticity of EPCIS module (scalability manager and virtual machines and templates)

•  High availability and fault tolerance for the database (NoSQL P2P)

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Proposed Algorithms

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Proposed Algorithms

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Conclusion and Future Works

•  Fosstrak presented a good scalability, although the results demonstrated that a higher load can present some performance issues.

•  Opens the possibility of using multiple servers

•  Future works include the implementation of the proposed algorithms and further evaluation using MIB methodology

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Internet of Things Scalability: Analyzing the Bottlenecks and Proposing Alternatives

Márcio Miguel Gomes Rodrigo da Rosa Righi

Cristiano André da Costa

Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos - Unisinos – Brazil

Corresponding address: [email protected]