"Walking through the fog (computing): trends, use-cases and open issues" Despite its huge success in many IT-enabled application scenarios, cloud computing has demonstrated some intrinsic limitations that may severely limit its adoption in several contexts where constraints like e.g. preserving data locally, ensuring real-time reactivity or guaranteeing operation continuity despite lack of Internet connectivity (or a combination of them) are mandatory. These distinguishing requirements fostered an increased interest toward computing approaches that inherit the flexibility and adaptability of the cloud paradigm, while acting in proximity of a specific scenario. As a consequence, the emergence of this “proximity computing” approach has exploded into a plethora of architectural solutions (and novel terms) like fog computing, edge computing, dew computing, mist computing but also cloudlets, mobile cloud computing, mobile edge computing (and probably few others I may not be aware of…). The talk will initially make an attempt to introduce some clarity among these “foggy” definitions by proposing a taxonomy whose aim is to help identifying their peculiarities as well as their overlaps. Afterwards, the most important components of a generalized proximity computing architecture will be explained, followed by the description of few research works and use cases investigated within our Center and based on this emerging paradigm. An overview of open issues and interesting research directions will conclude the talk.

1. Walking through the fog (computing):
trends, use-cases and open issues
Elio Salvadori
Direttore Centro CREATE-NET
Fondazione Bruno Kessler – Trento
Italian Networking Workshop 2019
Bormio, January 17th 2019

2. Outline
• Trends & rationale
• Paradigms & technologies
• Emerging ecosystem
• Deployment examples: MEC, fog & edge computing
• Toward a generalised architecture
• Open issues & research opportunities
• Research work @ FBK CREATE-NET
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The Pendulum swinging back

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data tsunami
data ownership & privacy decentralisation
real-time constraints

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6. Proximity vs Cloud computing
Cloud Computing Proximity Computing
Infrastructure owners Amazon, Microsoft, Google Telco & private
Management centralised distributed/centralised
Computation device big servers (homogeneous) any (heterogeneous)
Computation capacity high low
Storage high low
Privacy (Data management) low high (private)
Cost (computation/cooling) high low
Space for deployment warehouse little (e.g. outdoor)
Connectivity (internal) mostly wired mostly wireless
Off-line mode (Internet Connectivity) not possible possible
Latency high low
Power consumption (& type) high (direct) low (battery/direct)
Node mobility absent high
Nature of failure predictable highly diverse
Scalability high good
Proximity multiple hops one/few hops
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Constraints
are research
opportunities!

7. Emerging vertical applications
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8. Proximity computing terminology(ies)
CC
MCC
MEC
Fog
Edge
MistDew
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9. Proximity computing terminology(ies)
CC
MCC
MEC
Fog
Edge
MistDew
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10. Proximity computing paradigms...
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Paradigm Domain Definition Year
Cloudlets Computing Telco CC is a mobility-enhanced small-scale cloud Data Center that is located at the edge of the internet and available for use
by nearby mobile devices [Satyanarayanan et al]
2009
Mobile Cloud Computing Telco MCC is the instantiation of the Cloudlets paradigm to mobile access network [Bahl et al] 2012
Mobile Edge Computing Telco MEC brings computational and storage capacities to the edge of the network within the Radio Access Network to
reduce latency and improve context awareness. [Beck et al, 2014; ETSI, 2015]
2014
Multi-access EC Telco MEC extended to a mutiplicity of access technologies (mainly WiFi and fixed access) [ETSI] 2017
Edge Computing IoT EC refers to the enabling technologies allowing computation to be performed at the edge of the network, on
downstream data on behalf of cloud services and upstream data on behalf of IoT services [Shi et al]
2016
Fog Computing IoT FC is a highly virtualized platform that provides compute, storage, and networking services between IoT devices and
traditional cloud computing DC, typically, but not exclusively located at the edge of network [Bonomi et al]
2012
Dew Computing IoT DC is a sub-platform based on a microservice concept for which its computing hierarchy is vertically distributed and
encompassing resources such as sensors, tablets, and smartphones [Wang et al]
2015
Mist Computing IoT MC pushes appropriate computation to the very edge of the network, in the microcontrollers in the embedded nodes
of sensors and actuators. [Preden et al]
2015

11. Proximity computing emerging ecosystem
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Cloud players
Telco vendors Open-source initiatives & projects
Standardisation bodies & fora New stakeholders
HW manufacturers

12. Proximity computing: a taxonomy
MEC Fog Edge
Infrastructure owners Telco Private Private
Network scope Yes (RAN) Yes (LAN) No / Limited
Main computation element MEC Server Any device (continuum) Gateway (or Micro-DC)
Resource pooling (Orchestration) Yes Yes No
Privacy Limited Yes Limited
Off-line mode (Internet Connectivity) Limited Yes Limited (w/ MicroDC)
Multi-cloud interworking Yes Yes No
Scalability Yes Yes Limited
Real-time traffic handling Yes Yes Yes / Limited
Security End-to-end End-to-end Limited to devices
Analytics scope Multiple devices Multiple devices Single device
IoT verticals integration Yes Yes Limited
Virtualization Yes (NFV) Yes (Hyper/Container) Yes/Limited
Type of users Mobile Mobile / Stationary Mobile / Stationary
Standardisation ETSI OpenFog IIoT, ECC
Maturity / commercial availability prototypes Nebbiolo, FogHorn, Cisco MS Azure, AWS, Google, Intel
NOT interchangeable terms!
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13. Examples of proximity computing deployments
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14. MEC deployments Bump in the wire:
Distributed EPC:
ETSI White Paper No. 24 – February 2018
MEC Deployments in 4G and Evolution Towards 5G

15. E.g. MEC deployment for C-V2X
ETSI GR MEC 022 - September 2018
Multi-access Edge Computing(MEC): Study on MEC Support for V2X Use Cases
15

16. E.g. MEC deployment for Industrial IoT
ETSI PoC 9 - September 2017
MEC platform to enable low-latency Industrial IoT
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17. Edge computing deployment
Gateways
Industrial PCs
Embedded cards
EdgeCloud DC
Internet
Micro DC
• Focus on devices at the very edge
(limited network scope)
• Vertical scaling
• North-South interfaces
• No orchestration
• (Internet) off-line mode w/ MicroDC
• Limited integration of IoT verticals
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18. Fog computing deployment
Corporate LAN,
City-wide MAN,
etc
FogNode
FogNode
FogNode
GW/CPE
Internet
Cloud DC
Fog
• Focus on fog network infrastructure
(wide network scope)
• Vertical & Horizontal scaling
• North-South & East-West interfaces
• Orchestration of FogNodes resources
• (Internet) off-line mode
• IoT verticals integration
• Capability to host virtualised
controllers (MES)
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19. Comparing Edge & Fog deployments
Gateways
Industrial PCs
Embedded cards
Edge
Raw data &
data processing
Intelligence
creationCloud DC
Micro DC
Internet
Corporate LAN,
City-wide MAN,
etc
FogNode
FogNode
FogNode
GW/CPE
Internet
Cloud DC
Fog
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20. 20
E.g. Fog deployment for Smart Factories
FogNode
FogNode
FogNode
GW/CPE
Cloud DC
Wireless
Wired
Factory LAN
Scenario requirements dictate the
number of tiers:
- Amount/type of work in each tier
- Capabilities of the nodes at each tier
- Latency between nodes and latency
between sensors and actuation
- Reliability/availability of nodes
- Number of sensors
Internet
Machines
& devices
Manufacturing
cells
Assembly Line
Factory
Enterprise
unlicensed
licensed

21. E.g. Fog deployment for Smart City
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C. Byers, T. Zhang (Cisco, 2016)

22. Identify common traits: toward a generalised
architecture
Prox computing Node
Device / Physical
Sensors, actuators & Control
Compute Network Storage FPGA, GPU
Resource management
Orchestration
Scheduling
Reliability
Scalability
Applications
Performance,monitoring
Security
Dataprocessing&Storage
Analysis
Filtering
Trimming
Backup
Authentication
Encryption
Privacy
ID protection
System
monitoring
Performance
prediction
RT & QoS
assurance
Virtualisation
Application services
Application support
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23. Device / physical
Prox computing Node
Device / Physical
Sensors, actuators & Control
Compute Network Storage FPGA, GPU
Resource management
Applications
Performance,monitoring
Security
Dataprocessing&Storage
• Sensors, actuators & control
• IoT device that produce data
• They can be virtual
• Protocol abstraction layer
• make IoT device data digestible
by PCNs
• Proximity Computing Node (PCN)
• any device with computation,
storage, network and
acceleration capabilities
• e.g.: gateway, server, router
• Node-to-node communication
• multiple PCNs are needed to
reach an appropriate decision
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24. Resource management
Device / Physical
Resource management
Orchestration
Scheduling
Reliability
Scalability
Virtualisation
Applications
Performance,monitoring
Security
Dataprocessing&Storage
• Orchestration
• resource allocation and de-
allocation in PCNs
• Virtualisation
• hardware-based
• virtual machines
• containers
• Scheduling
• simultaneous apps running
• Scalability
• Horizontal & Vertical
• Reliability
• PCNs and sensors/actuators
failure & anomaly handling
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25. Applications
Device / Physical
Resource management
Applications
Performance,monitoring
Security
Dataprocessing&Storage
Use Cases are built by leveraging on
a set of Application services that
fulfill domain-specific needs.
• Application services
• SB interface toward IoT devices
• NB interface toward cloud & UI
• algorithms & analytics (sensor
fusion, Machine Learning, etc)
• Application support
• run-time engines (JVMs, .NET)
• web servers (Apache, Tomcat)
• message/event bus (RabbitMQ)
• application storage (SQL, Mongo)
• analytics tools (Hadoop, Spark)
Application services
Application support
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26. Cross-layers: performance, security, data processing
Device / Physical
Resource management
Applications
Performance,monitoring
Security
Dataprocessing&Storage
Analysis
Filtering
Trimming
Backup
Authentication
Encryption
Privacy
ID protection
System
monitoring
Performance
prediction
RT & QoS
assurance
• Performance & monitoring
• help selecting the most appropriate PCN resources
• predict PCN performance based on system load and re-
source availability
• QoS/priorities and Real-Time assurance (Time-Sensitive
Networking, time-critical computing, etc)
• Security
• devices & user autentications
• maintain encryption between communications
• users can specify privacy attributed on their own data
• Data processing & storage
• analysis and filtering of data
• data trimmering and reconstruction
• local storage or cloud-based (when & if needed)
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27. Example 1: OpenFog Reference Architecture
Device / Physical
Resource management
Applications
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28. Example 2: ETSI MEC architecture
Performance, monitoring
Device / Physical
Resource management
Applications
Security
Dataprocessing&Storage
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29. Open issues & research opportunities
Economics & standardisation:
• telco vs cloud providers
• new stakeholders
• interoperability
• simulation tools
• billing
• pricing
Resource management:
• orchestration
• optimization & scheduling
• deployment strategies
• H & V offloading
• routing (ad hoc, p2p)
• management & monitoring
• edge-constrained ML algo
Architectures:
• scalability
• federation
• heterogeneity
• autonomicity
• fault tolerance
• mobility
• UE/IoT devices battery-span
Communication:
• Industry-grade wireless (on
unlicensed spectrum)
• real-time communication
• quantum-safe
communication (e.g. QKD)
Security & Privacy:
• data ownership
• storing encrypted data
• malicious PC nodes
• secure virtualization
technology
Virtualisation:
• improving efficiency
• enabling innovative use
cases
• multi-tenancy with QoS
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30. On-going research @ FBK CREATE-NET
I. MEC caching via a lightweight MANO
II. Fog services orchestration
III. Multi-container deployments on IoT gateways
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31. MEC: caching via a lightweight MANO
• MEC scenarios with 10s/1000s of NFVI PoPs
• 10s or even 100s VNFs
lightMANO: a lightweight MEC solution, based
on a converged SDN&NFV platform
GTP
Encap/Decap
Encapsulated IP Traffic
(UE)
S1 Traffic
EPC
Docker
SquidMobile Edge Caching
virtualisation overhead
caching performance
management &
virtualization
R. Riggio et al., “LightMANO: Converging NFV and SDN at the Edges of the Network” IEEE/IFIP NOMS 2018

32. Fog services orchestration
Assuming a fog computing deployment with multiple regions of nodes,
loosely interconnected among each other and computing-intensive
applications like stream-mining
Problem:
• need to offload to neighboring regions to avoid hot-spots
Solution:
• MILP & heuristic identified to find optimal allocation
F. Faticanti et al, “Cutting Throughput with the Edge: App-Aware Placement in Fog Computing” Under Review
orchestration
& optimisation
Results can’t be
disclosed yet...

33. Edge computing: multi-container
deployments on IoT gateways
• AGILE open-source IoT gateway framework, based on microservices
• It demonstrates the advantages of a containerized environment for
in-contained and cross-container performance optimization
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K. Dolui, C. Kiraly, “Towards Multi-container Deployments on IoT Gateways” IEEE GLOBECOM 2018
http://agile-iot.eu

34. Questions, feedbacks?
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Elio Salvadori
esalvadori@fbk.eu
@esalvad

35. Short bibliography
• K. Dolui et al, "Comparison of Edge Computing Implementations: Fog Computing, Cloudlet and Mobile Edge
Computing”, IEEE 2017
• P. Garcia Lopez et al, "Edge-centric Computing: Vision and Challenges”, ACM SIGCOMM Computer
Communication Review, October 2015
• R. K. Naha et al, "Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions”,
IEEE Access 2018
• N. Hassan et al., “The Role of Edge Computing in Internet of Things”, IEEE Communication Magazine,
November 2018
• Nebbiolo Technologies Whitepaper, "Fog vs Edge Computing", v. 1.1, 2017
• ETSI ISG on Multi-access Edge Computing https://www.etsi.org/technologies-clusters/technologies/multi-
access-edge-computing
• B. Varghese et al, "Feasibility of Fog Computing", arXiv.org > cs > arXiv:1701.05451, 2017
• R. Riggio et al., "LightMANO: Converging NFV and SDN at the Edges of the Network”, IEEE/IFIP NOMS 2018
• K. Douli, C. Kiraly "“Towards Multi-container Deployments on IoT Gateways” IEEE GLOBECOM 2018
FBK CREATE-NET software platforms for MEC & Fog:
• 5G-EmPOWER: https://5g-empower.io
• FogAtlas: https://fogatlas.fbk.eu
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