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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: cac@unisinos.br
2
Agenda
•  Introduction
•  Theoretical basis
•  Research question
•  Related works
•  Methodology and justification
•  Proposed model
•  Proposed algorithms
•  Conclusion and Future Works
3
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
4
Application of IoT on health area
Internet
Healthcare	
  server
Caregiver	
  or	
  
physician
Emergency	
  services	
  or	
  
Medical	
  researcher
Database
)))
)))
)))
Information
Assessment,	
  assistance,	
  treatment
Inertial	
  sensor
Pulse	
  and	
  
blood	
  
pressure	
  
sensor
Oximetry	
  
Sensor
Source: adaptated from Jiang et al (2008)
5
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
6
Typical architecture of an RFID system
Users	
  and	
  
Applications
Data	
  Storage
RFID	
  Middlewares
and
Local	
  Applications
RFID	
  Readers
Antennas
RFID	
  Tags
7
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 of
RFID Middlewares
8
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
9
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?
10
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
11
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
12
Methodology and Justification
•  MIB: Micro Benchmark for Evaluating Internet of Things
Middlewares
Source: Developed by the author
13
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?
14
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)
15
Methodology and Justification
ALE module average behavior – Current model
16
Methodology and Justification
EPCIS module average behavior – Current model
17
Proposed Model
Current model Proposed model
Nuvem
User	
  Applications
App	
  1 App	
  2 App	
  “n”
ALE	
  multicore	
  multithread
RFID	
  Reader	
  1 RFID	
  Reader	
  2 RFID	
  Reader	
  “n”
NoSQL	
  P2P
Database
EPCIS	
  in	
  a	
  cloud
Capture	
  Interface	
  (HTTP)
o	
  o	
  oVM VM
Query	
  Interface	
  (SOAP)
o	
  o	
  oVM VM
Capturing	
  Applications
App	
  1 App	
  2 App	
  “n”
18
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)
19
Proposed Algorithms
20
Proposed Algorithms
21
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
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: cac@unisinos.br

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Internet of Things Scalability

  • 1. 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: cac@unisinos.br
  • 2. 2 Agenda •  Introduction •  Theoretical basis •  Research question •  Related works •  Methodology and justification •  Proposed model •  Proposed algorithms •  Conclusion and Future Works
  • 3. 3 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
  • 4. 4 Application of IoT on health area Internet Healthcare  server Caregiver  or   physician Emergency  services  or   Medical  researcher Database ))) ))) ))) Information Assessment,  assistance,  treatment Inertial  sensor Pulse  and   blood   pressure   sensor Oximetry   Sensor Source: adaptated from Jiang et al (2008)
  • 5. 5 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
  • 6. 6 Typical architecture of an RFID system Users  and   Applications Data  Storage RFID  Middlewares and Local  Applications RFID  Readers Antennas RFID  Tags
  • 7. 7 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 of RFID Middlewares
  • 8. 8 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
  • 9. 9 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?
  • 10. 10 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
  • 11. 11 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
  • 12. 12 Methodology and Justification •  MIB: Micro Benchmark for Evaluating Internet of Things Middlewares Source: Developed by the author
  • 13. 13 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?
  • 14. 14 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)
  • 15. 15 Methodology and Justification ALE module average behavior – Current model
  • 16. 16 Methodology and Justification EPCIS module average behavior – Current model
  • 17. 17 Proposed Model Current model Proposed model Nuvem User  Applications App  1 App  2 App  “n” ALE  multicore  multithread RFID  Reader  1 RFID  Reader  2 RFID  Reader  “n” NoSQL  P2P Database EPCIS  in  a  cloud Capture  Interface  (HTTP) o  o  oVM VM Query  Interface  (SOAP) o  o  oVM VM Capturing  Applications App  1 App  2 App  “n”
  • 18. 18 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)
  • 21. 21 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
  • 22. 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: cac@unisinos.br