fog computing

FOG COMPUTING

B A S E D O N PA P E R S B Y I V A N A N D F L A V I O

• Virtualization Technologies

• High Bandwidth Availability – Improvements in Physical Layer

• Universal Software Interoperability Standards

BIRTH OF THE CLOUD

A BRIEF HISTORY OF CLOUD

’99 – Salesforce delivers enterprise applications through a website.

2002 – AWS launches. “on demand” workforce through mechanical turk.

2006 – AWS offers small companies “cloud computing” ahead of Microsoft.

2009 – Google offers Google Apps.

WHY IS CLOUD COMPUTING POPULAR?• Economy at scale.

• Pay as you go.

• Globalize workforce.

• Accessibility and flexibility.

• Minimize licensing.

INTERNET OF THINGS

INTERNET OF THINGS

• Desire to connect all devices.

• Increase machine to machine communication.

• Integrating sensors to the network.

• Smart appliances, smart homes, smart vehicles etc.

DEPLOYMENT PROBLEMS• Lack of shared infrastructure.

• Lack of standards.

• A missing piece of technology to help deploy IoT.

THE MISSING PIECE

FOG COMPUTING – THE MISSING PIECE.

• Extending the cloud to the edge of the network.

• Not a trivial extension!

WHAT IS THE FOG?

• Computing capabilities where they matter.

• Take analytics, processing and storage to the edge of the network.

• Impractical to send all data from all devices to the cloud for processing.

DEFINING CHARACTERISTICS OF THE FOG

• Location Awareness

• Low latency.

• Geographical distribution. Not centralized like the cloud!

• Can leverage large scale sensor networks.

• Large, large number of nodes.

DEFINING CHARACTERISTICS OF THE FOG

• Support for mobility.

• Real time performance, analytics and interactions.

• Predominantly wireless access.

• Seamless operation. Example: streaming.• Requires interoperability.

• Federation.

INTERPLAY OF FOG AND CLOUD COMPUTING

• Analytics - center of our applications.

• We need fog for context aware, local analytics.

• We need the cloud for centralized, heavy duty, global analytics.

• Protection and control information requires real time processing.

• Several tiers of hierarchical filtering of data.

INTERPLAY OF FOG AND CLOUD COMPUTING

• Highest tier involves human interaction- like visualization.

• From seconds in lowest tier to days in highest tier.

SUMMARIZING CLOUD AND FOG COMPUTING

Cloud Fog

Data and applications processed in the cloud.

Processing takes place at the edge of the network.

Bandwidth limitation, since data is sent through cloud channels.

Bandwidth not a limitation.

Centralized Distributed

Slow response(high latency) Low latency applications.

Scalability issues. Scalable as the number of nodes increases.

APPLICATIONS OF FOG COMPUTING

• Smart Grids.

• Smart Traffic Lights - Ambulance Sensors for example.

• Connected Vehicles.

• Wireless sensors and actuator networks.

• Smart Building.

• IoT.

• SDN(Software Defined Networking)

IOT AND CYBER PHYSICAL SYSTEMS

• Tight coupling of engineered systems and physical reality. • Embedded Systems with networking capabilities.


• Physical systems are noisy, dynamic and uncertain.

• Software components are precise.

• Can we give intelligence to these physical systems?• Or derive intelligence from it?


• Examples:• Intelligent medical devices.

• Smart highways.

• Smart buildings.

• Smart factories.

• Smart agriculture.

• Robotics.

SOFTWARE DEFINED NETWORKING

• Traditional networks are:• Stagnant and difficult to perform experiments on.

• Closed systems that are vendor specific.

• Meaningful vendor collaboration is difficult.

• HUGE barrier for ideas in networking.

• SDN is:• A software architecture that decouples the control and data plane.

• Programmatic interface into network equipment.

• Centralized controller can operate an entire network.


What does SDN with Fog Computing Provide?

• Specifically solve vehicular communication problems:• Intermittent connectivity.

• Collisions.

• High packet loss rate.

• How?• Better vehicle to vehicle communication.

• Better vehicle to infrastructure communication.

Fog ComputingApplications

• Implemented at Network Edge

• Low Latency

• Location Awareness

• Improved QOS

• Supports Heterogeneity

• End-User devices

• Access Points

• Edge Routers and Switches

SMART GRID

• Load Balancing Devices

• Help to switch to alternative energy sources

• Fog Collectors

• Process data sent by sensors

• Filter data locally and send rest to

higher Tiers

• Fog supports ephemeral storage

SMART TRAFFIC LIGHTS

• Video Cameras can automatically change lights

depending on situations

• Interact locally with sensors to detect traffic

• Measure Speed and distance of vehicles

• Send warning signals to approaching vehicles

CCONNECTED VEHICLES• Enable Real-Time interaction

• Cars

• Access Points

• Traffic Lights

• FOG Clusters analyze data locally to reroute traffic and maintain

flow

WIRELESS SENSOR & ACTUATOR NETWORKS

• Traditional sensor networks need actuators to exert physical

actions

• Fog Devices can control measurement by creating closed-loop

• Sensors can monitor heat levels on train’s ball bearing, to stop the

train in case of emergencies

• Sensors on air vents regulate the flow of air

DECENTRALIZED SMART BUILDING CONTROL

• Wireless sensors deployed in buildings to measure

temperature and humidity levels

• Sensors combined to form better measurements

• Increase or decrease the temperature depending on reading

• Can be used to conserve energy, water and other resources

WIND FARMS• Fog can be used to regulate the windmills based on weather data

• Better control of turbines

• Based on Elevation

• Topography of the terrain

• Can be used to collect and use long term data

CCONTENT DELIVERY AND CACHING

• Traditional Web content are not optimized for user-side

requests.

• Fog enables dyanamic customizable optimization.

• Using caching techniques latency is further reduced and can

result in bandwidth usage reduction

HEALTHCARE AND OIL/GAS

• Healthcare

• Patient monitoring system in Real Time in critical care

units

• Reduction in latency might result in saving lives

• Oil & Gas

• Pipeline monitoring for leaks, fire, theft etc.

AGRICULTURE & RETAIL

• Agriculture

• Smart forms with crop monitoring and irrigation control

systems.

• Retail

• Tracking of shopping carts and automatic billing systems.

• Results in time saving measures

FOG COMPUTING APPLICATIONS

MOBILE FOG: A PROGRAMMING MODEL FOR LARGE–SCALE APPLICATIONS ON THE INTERNET OF THINGS

• As a High level programming model for geo-spatially distributed, large-scale and latency sensitive Internet applications.

• Mobile Fog consists of a set of event handles and functions that an application can call.

• Not a generic model but built for particular application, while leaving out functions that deal with technical challenges of involved image processing primitives.


• Vehicle tracking using Cameras• Camera processes are the leaves of the tree and are

responsible for sensing the environment and delivering parent processes.

• Traffic monitoring using MCEP

MIGCEP: OPERATOR MIGRATION FOR MOBILITY DRIVEN DISTRIBUTED COMPLEX EVENT PROCESSING

• Increasing deployment of powerful mobile sensors and large scale sensor networks. E.g. Smartphones, CCTV

• Complex Event Processing (CEP) is a key paradigm to realize such applications.

• Mobile CEP, consumers and sensors are mobile. • Cost associated with each migration.

• Propagation of state across the network is expensive.

MIGCEP: OPERATOR MIGRATION FOR MOBILITY DRIVEN DISTRIBUTED COMPLEX EVENT PROCESSING

• Exploit application knowledge of the MCEP system and predicted mobility patterns to plan the migration ahead of time.• Amortize migration costs.

• Reduces Bandwidth.

• Ensures application-defined end-to-end latency restrictions.

IMPROVING WEB SITES PERFORMANCE USING EDGE SERVERS IN FOG COMPUTING ARCHITECTURE

• Users are connected to Internet via edge servers. All web requests that the user makes first goes through the edge servers.

• Fog server can optimize the incoming portions of the webpage based on portions that have been examined already.

• Fog server has the distinct advantage of knowing the network conditions local to an end user.

• E.g. The type of device, Congestion in network• Per User Optimization for Inline or External Scripts

• Advantage of an inline JS or CSS is that HTTP requests are minimized.

• Fog server can observe each user based on their MAC addresses or local IP addresses and keep track of each user’s website requests.

SERVICE-ORIENTED HETEROGENEOUS RESOURCE SHARING FOR OPTIMIZING SERVICE LATENCY IN MOBILE CLOUD

• Pervasive mobile devices share their heterogenous resources and support services.

• Neighboring nodes in a local network form a group called a local Cloud.

• A local resource coordinator (LRC) serving as Fog device is elected from nodes in each local Cloud.

SECURITY AND PRIVACY CONCERNS

SECURITY ISSUES

• Main security issues are authentication at different levels of gateways as well as devices installed at the consumer’s end.

• Each smart meter and smart appliance has an IP address. A malicious user can either tamper with its own smart meter, report false readings, or spoof IP addresses.

SECURITY ISSUES – SOLUTIONS

• Solution for Authentication• Public Key Infrastructure (PKI) based solutions which

involve multicast authentication.• Diffie – Hellman key exchange based authentication

techniques.• Intrusion Detection Techniques can also be applied in Fog

computing.• Intrusion can be captured by using an anomaly-based

method where an observed behavior is compared with expected behavior to check if there is a deviation.

MAN-IN-THE-MIDDLE ATTACK

• A man-in-the-middle attack is one in which the attacker secretly intercepts and relays messages between two parties who believe they are communicating directly with each other.

• MITM attacks pose a serious threat to online security because they give the attacker the ability to capture and manipulate sensitive information in real-time while posing as a trusted party during transactions, conversations, and the transfer of data.

MITM IN THE CONTEXT OF FOG

• Man-in-the-middle attack has potential to become a typical attack in Fog computing.

• Gateways serving as Fog devices may be compromised or replaced by fake ones.

• E.g. KFC or Star Bar customers connecting to malicious access points which provide deceptive SSID as public legitimate ones.

STEALTH TEST

MITM only consume a small amount of resources in Fog devices, such as negligible CPU utilization and memory consumption.

In this scenario, a 3G user sends a video call to a WLAN user. Since the man-in-the-middle attack requires to control the communication between the 3G user and the WLAN user, the key of this attack is to compromise the gateway which serves as the Fog device.

STEALTH TEST

• In order to hijack and replay victims’ video communication, a hook program is inserted into the TCP/IP stack of the compromised system.

• Hook is a technique of inserting code into a system call in order to alter it.

WORK FLOW OF MITM

• Communication between 3G and WLAN needs a gateway to translate the data of different protocols into the suitable formats.

• MITM is divided into four steps• Hook process redirects data from 3G user to attacker.• Attacker replays or modifies the data of the communication locally.• Attacker sends the data back to gateway.• Gateway forwards the data from the attacker to the WLAN user.

• The attacker can monitor and modify the data sent from the 3G user to the WLAN user in the ‘middle’ of the communication.

RESULT OF STEALTH TEST

• Memory consumption and the CPU utilization of gateway during the attack, is measured and compared to normal utilization for anomaly detection.

• If MITM does not greatly change the features of the communication it can be proofed to be a stealthy attack.

MITM & FOG

• MITM is simple to launch but difficult to be addressed.

• Encryption may also not protect users as attackers can set up a legitimate terminal and replay the communication without decryption.

• Complex encryption and decryption not suitable for all scenarios.

PRIVACY ISSUES

EPPAAn Efficient and Privacy Preserving Aggregation Scheme for secure smart grid communications

• Super increasing sequence to structure multi-dimensional data and encrypt the structured data by the homomorphic cryptogram technique.

• Ensures privacy of data collected by smart meters but does not guarantee that the device transmits the correct report to other gateways.

• Data communications from user to smart grid operation center, data aggregation is performed directly on cipher-text at local gateways without decryption.

REFERENCES

THANK YOU FOR YOUR ATTENTION

fog computing

Documents

summarizing cloud

cloud channels

smart vehicles

cloud computinghighest

smart factories

smart homes

smart buildings

smart agriculture