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
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]