Fog Computing-I

Summary

The video discusses the concept of fog computing, an evolution of cloud computing that emphasizes processing data closer to its collection point to reduce latency and improve real-time processing. Fog computing acts as an extension of cloud services, handling local data processing and reducing the need for constant cloud communication. With IoT and smart devices generating massive data, traditional cloud systems face challenges in bandwidth and response times. Fog computing helps distribute the data processing load, enabling faster responses, better resource utilization, and improved system efficiency, all while maintaining cloud computing's benefits like scalability and minimal infrastructure requirements.

Highlights

• Fog computing is about processing data nearer to the source to reduce latency. 🚀
• The traditional cloud model offloads local processing entirely, which may lead to higher response times. 🛠️
• Fog acts as a middle layer, reducing the data sent to the cloud and handling some processing itself. 🏭
• Huge data from IoT devices can congest cloud systems, fog computing eases this burden. 🏗️
• Security with fog computing requires careful management due to data being processed at multiple points. 🔐

Key Takeaways

• Fog computing pushes data processing closer to the data source for quicker responses. ⚡
• Cloud computing is still vital but fog computing enhances it by handling certain tasks locally. 🌫️
• IoT and smart devices generate massive data volumes, necessitating efficient data management. 📊
• Fog computing minimizes latency and improves real-time application performance. ⏱️
• Security concerns are elevated with fog computing due to decentralized data processing. 🔒

Overview

Fog computing is a revolutionary extension of cloud computing, designed to manage the overwhelming data generated by IoT and smart devices. By processing data closer to its source, fog computing reduces latency and improves the performance of real-time applications. This system distributes computing tasks between local devices and the cloud, ensuring faster response times and easing the burden on central cloud servers.

The introduction of fog computing addresses the challenges faced by traditional cloud systems, such as data congestion and slow response times due to centralized processing. By acting as an intermediary between edge devices and the cloud, fog computing handles some of the data processing on-site, which not only saves bandwidth but also supports mobile and geographically distributed applications effectively.

While fog computing maintains the benefits of cloud services like scalability and pay-as-you-go models, it introduces heightened security concerns due to its decentralized nature. Each intermediate device involved in data processing becomes a potential security risk. However, the agility and reduced latency offered by fog computing make it an essential component in handling modern data processing demands efficiently.

Chapters

• 00:00 - 00:30: Introduction to Cloud Computing In this chapter, the focus is on introducing the concept of cloud computing. The discussion revolves around how cloud computing involves offloading tasks and resources to a cloud-based infrastructure. This allows for more efficient resource management and scalability.
• 00:30 - 01:00: Cloud Computing Offloading Cloud computing involves offloading computing processes and data to the cloud, where it's managed by a third party. This allows the customer or user to focus on business processes rather than the technical aspects. Despite various technical complexities in the backend, the primary aim is to simplify and offload workload from the user's end.
• 01:00 - 02:00: Backbone Network and Data Transmission The chapter discusses the importance of a strong backbone network to ensure efficient data transmission. It highlights the need for a robust network that is always operational and capable of handling large volumes of data, especially as technological development advances.
• 02:00 - 04:00: Internet of Things and Sensor Data The chapter discusses the influx of data generated by digitally enabled devices and the challenges associated with transmitting this data from consumers to cloud service providers. The process involves sending large volumes of data to the cloud, processing it, and sometimes returning the results to the consumers or transmitting them elsewhere. The chapter highlights significant concerns related to this data transfer, particularly with the rise of the Internet of Things (IoT), which is leading to more data generation and associated challenges.
• 04:00 - 05:00: Edge Devices and Local Processing This chapter explores edge devices and local processing, highlighting the prevalence of varied sensors generating massive multimedia data. It discusses the need for efficient data transmission and the reliance on a robust cloud computing infrastructure. The cloud offers immense, seemingly infinite computing power far exceeding the capabilities of local devices.
• 05:00 - 06:00: Introduction to Fog Computing The chapter introduces the concept of Fog Computing, highlighting the increasing volume of data being generated and transmitted over the network. It discusses the evolution of mobile and other electronic devices that are becoming more powerful in computation and more resourceful in their capabilities.
• 06:00 - 12:00: Benefits of Fog Computing Fog computing minimizes latency by processing data closer to the source/sensor, optimizing resource usage, and reducing reliance on cloud transmission.
• 12:00 - 17:00: Fog Computing vs Cloud Computing In this chapter, the focus is on comparing Fog Computing and Cloud Computing. It begins with an example scenario in which a lab is equipped with ten temperature sensors. These sensors are meant to operate within a temperature range of eighteen to twenty-two degrees centigrade. The discussion highlights the process where all sensor data is transmitted to a server, potentially located in the cloud, to monitor and calculate if the temperature levels are maintained within the designated operating limits.
• 17:00 - 25:00: Enabling Technologies for Fog Computing The chapter discusses the role of enabling technologies in fog computing, using an example of a lab environment. It describes a scenario with multiple labs, each being monitored by sensors to manage and regulate temperature. These sensors collect data and communicate with a local decision system (ds), determining if the temperature is within an acceptable range. This reflects how fog computing utilizes local nodes (like sensors and ds) to process and act on data near the source, reducing the need for centralized cloud processing.
• 25:00 - 30:00: Advantages and Limitations of Fog Computing This chapter discusses the concept of fog computing, which involves processing data at the edge of the network rather than in a centralized data center. It highlights the advantages and limitations of this approach.
• 30:00 - 35:00: Security Concerns in Fog Computing The chapter discusses the concept of operating ranges and binary transmission using zero-one or yes-no signals. It explores the idea of sensing and transmitting data, and introduces the notion that some computing processes can occur at 'things' or local devices. The chapter suggests that with the increasing intelligence of intermediate devices, there is an opportunity to transfer some computing tasks from a centralized cloud to these more localized environments.
• 35:00 - 37:00: Conclusion The conclusion chapter discusses the transition from cloud computing to fog computing, emphasizing the movement of data processing from centralized cloud data centers, which can be either privately or publicly hosted. It highlights the challenges that current cloud systems face and introduces fog computing as a solution to those issues. Fog computing brings processing closer to the edge, allowing for more efficient handling of data.

Fog Computing-I Transcription

• 00:00 - 00:30 hello ah so we will be continuing our discussion on cloud computing ah so what we have seen that ah in case of ah cloud computing ah what we are trying to do we are trying to ah offload
• 00:30 - 01:00 our computing and computing processes and data to the cloud right so that ah it is ah maintained by a third party and the on the other end the customer or the consumer or the user more concentrate on the business processes process so that is the basic ah objective of ah or model of the things right there are there are a lot of technical ah um technicalities at the backend but nevertheless we are offloading
• 01:00 - 01:30 the thing so what for that what we need a ah very strong backbone right or a strong backbone network which should be ah always up and ah and able to transfer data on a large volume as we see as as ah as along with the development and ah being most of the things
• 01:30 - 02:00 are digitally enabled we are what we are getting a huge volume of data in other sense a huge volume of data maybe need to be transmitted from this customer end or consumer end to this cloud service provider being executed the results in some cases are transmitted back or transmitted in other places right the major issue is this huge volume or transfer of data and what we have ah what we see in recent development with number of activities specially internet of things ah coming up and ah more only ah
• 02:00 - 02:30 and huge ah variety of sensors in place so we have lot of multimedia data which need to be transmitted right and that is one part of the story that we require a huge backbone and type of things as for as the cloud is concerned what we consider its a its a huge computing power much higher than what we what the devices can do and it it is some sort of infinite computing power is there on the other hand ah what we see that a huge
• 02:30 - 03:00 volume of data are being generated and being transmitted over the network again what we see that the devices starting from as we discussed about mobile devices ah smart mobile devices or other type of devices even intermediate ah network devices what they are becoming is more more powerful in terms of computation and more resourceful things ok
• 03:00 - 03:30 or in other sense we are not all the times exploiting the resources available in the ah things sic ah say consider a particular sense sensor node or a local sync node of a sensor which are collecting the data and transmitting to the ah in the upward path maybe to the cloud so this ah this could have been done some processing at the end like
• 03:30 - 04:00 i can say that suppose in this particular room or a particular lab i have ah say ten temperature sensors right so what what a what what is my basically business model this temperature should be varying may between ah say um eighteen to twenty two degree centigrade that is the that is the ah operating range of this temperature now what we are doing this all these ten or sensors are sending the data to the up up in the server maybe in a cloud that is calculating whether the within the limit and type of things
• 04:00 - 04:30 and i can have the ah say ten such labs so there are hundred such data are going on and if the temperature is varying somewhere it is sending error now if you consider this particular a a particular a enclosed lab or single things otherwise i have what i have could taken i taken a local ds and whether my temperature is ok in the lab by the by the sync node of the sensors which are collecting this data of this particular room and it takes a call that whether higher or upper and then sense that say its some
• 04:30 - 05:00 statistical data or what we say some aggregated data to the sensor it may be the average or it may be average with other standard deviation etcetera to the things in other sense it is not in ah in other than sensing this ah transmitting this ten ah sensors data i am sending one average data or ah and which has my purpose even you can say that the if the if my sync node is intelligent enough it can take a call that whether the
• 05:00 - 05:30 temperature is within this ah operating range yes ah or outside thing some zero one or yes no type of things and then transmit this in other sense this is taking a some part of computing at the things so with the intermediate devices becoming more intelligent whether there is a possibility of pushing the computing from logically centralized cloud to somewhere
• 05:30 - 06:00 more down the line right towards the edge of the things right thats exactly what we are trying to discuss today is what we say this sort of computing is fog or from cloud to fog right so cloud is the whole thing and fog computing so as as we see the challenges or the the data what the cloud computing todays is doing the processing of huge data in the datacenters datacenters may be privately hosted or publicly
• 06:00 - 06:30 available by paying rent that is it can be a public cloud or a private cloud all necessary information has to be uploaded or transmitted to the cloud for processing and extracting knowledge of it right so ah the whole data as we are discussing need to be ah transmitted to the ah ah cloud now ah also we have seen the typical characteristics of cloud for wa for which we ah we are ah the todays world is inclined towards is that
• 06:30 - 07:00 dynamic scalability i can scale up or scale down based on my need so another is that no ah infrastructure management or practically ah minimal infrastructure management at the user end so if i i i offload everything that computing etcetera on the cloud so i require very less infrastructure management ah at my user end and secondly ah and finally what we have a metered service right pay as you go model
• 07:00 - 07:30 so these three things that dynamic scalability ah minimal management or all my ah infrastructure management pushing it to the cloud and metered service pay as you go model these are primary ah features of the cloud which makes is popular there are several other things which are which are there but never the less these are the three things which are the driving force so whatever we do we do not want to lose out of the things if we compromise on those type
• 07:30 - 08:00 of ah features then the very ah motivation to going towards cloud may be challenged now there are issues with cloud only computing so what we say that only the cloud is computing register sitting duck or maybe the issues especially in todays applications which variety of sensors variety of real time operations ah and lot of redundant data right there are lot of data which ah are redundant like if i am sending temperature things it may not
• 08:00 - 08:30 mean may be meaningless to sense all the sensors data which are which are more or less same information unless there is a different in some sensor data i may not want to send the data all are reporting between around twenty degree centigrade it does not require a cloud to take a call it could have been done as a much lower level so that or in other sense i have a huge amount of digital data to be transmitted so communication takes place takes a long time due to hum human smart for interaction
• 08:30 - 09:00 and type of things if the state still datacenters are centralized right datacenters and in some woodson so all the data from different region can cause congestion in the core right so being transmitted ah things especially in case of exigencies where a lot of volumes of data suddenly pushed into the thing right in case of say some disaster or ah some ah huge amount of in flux due to some event this is a lot of volume of data suddenly in flux
• 09:00 - 09:30 so there is a huge volume of data to be ah transmitted and there can be congestion and such a task requires very low response time to prevent further crashes so if i have this sort of things which has a some sort of accident some accident prevention mechanisms can into ah should be activated so where we require a very low response time so immediately need to be act acted so ah waiting for that cloud to take a call revert
• 09:30 - 10:00 back and all those things may take lot of time so that is another problem so ah so the emergence of a concept called fog computing so on the cloud we are to the we are talking about fog that is little bit bringing tao down to the ground or in other sense ah we are pushing this computing thing from the from the centralized datacenter or
• 10:00 - 10:30 the cloud datacenters to this edges right or intermediate or the ah edges of the network aj edge of the network so fog computing also known as fogging and edge computing though some people have little other views of that ah edge computing but nevertheless it is a fogging or edge computing it is a model in which data process applications are concentrated in devices at the network edge rather than existing almost entirely
• 10:30 - 11:00 on the cloud so now not only the cloud at the centralized things the data application and processes are distributed between the edge right which which somes effect of some way of distributing this whole processing whether things what it helps us it helps us in reducing the data load in the communication i can have a local decision and which is not needed for the global type of things they say smart traffic light ah management system
• 11:00 - 11:30 the traffic light management system in kolkata is nothing to do with the traffic light management this system in delhi apparently right for day to day traffic management right ah so i could have done it locally or even i can say that a region of a particular city may may have only aggregated data which need to be transmitted at the higher level for traffic management right so that basic ah intermediate management could be done locally
• 11:30 - 12:00 so those things could be done in a in a concept of what we say fogging or ah fog computing the term fog computing was ah first introduced ah by cisco as a new model to ease wireless data transfer to distributed devices in the internet of things network paradigm so as iot is becoming omnipresent or iot is becoming ah a everywhere it is there internet of things
• 12:00 - 12:30 so its huge volume of data devices which mass computing capability or resources ah much higher resources can do a bit of a job which could have been solved at a at a lower level so ciscos vision if we look at that ah fog computing is to enable application on billions of connected devices to run directly on the network edge since cisco is primarily a network driven organization so it has a huge number of devices across the world and those devices
• 12:30 - 13:00 are somewhat ah managed etcetera managed by ah a a a some sort of a homogeneity is therefore because upon the one make and there are resourceful devices which could have done some sort of ah computing ah things and i can even run applications on the devices and doing so on and so forth right so user can develop manage run software application of cisco framework of network devices including harden hardened routers switches etcetera
• 13:00 - 13:30 cisco brings say open source ah linux and network operating system together in a single network devices so it it helped to do ah not only computing but it was if you want to do computing you need to give some sort of a platform to run the applications for the computing things right so those things are they are in the devices and this this ah this is possible because of there are ah resources available at different layer of the network towards
• 13:30 - 14:00 the edge so if we look at a view so this cloud are at the top it is still there and it should be there there are intermediate devices which we are now helping only a so far was only transmitting the data now can they do ah some sort of a computing what we say fogs fog computing and there are end user devices ah which are
• 14:00 - 14:30 spread over different locations starting form say ah smart vehicles or ah which can communicate devices servers ah smart cameras and ah anything which can do write any any any any device which can ah capture detailed data compute and transmit right so bringing intelligence down from cloud closer to the end user of the edge of the network
• 14:30 - 15:00 that is one of the thing cellular base station network routers wifi gateways will be capable of running these applications right so there are because whenever i communication we have ah cellular networks wifi router into place and if those are having surplus resources and they are able to do that so that a my my application can run say i want to run a application for monitoring the environment of different labs starting from temperature
• 15:00 - 15:30 to ah humidity may be some sort of a what sort of ah air pollution or air content etcetera so this sort of things can be done ah end devices like sensors are able to perform basic data processing right so ah the sensors can do a basic data processing processing close to the devices lowers the response time enabling real time applications right so ah whenever we process close to the devices so the response time reduces that is ah obvious and i can
• 15:30 - 16:00 do lot of real time processing of the things right so i can do a real time processing of a of a say of of any applications like i do a application based on that ah what we say dynamic ah signalling mechanism of a traffic light based on the traffic on the road so the cameras which are on the road capturing that how many ah what is the traffic ah flow
• 16:00 - 16:30 based on that ah i ah the traffic signalling may change if that is the that is the need of this traffic management so that is local right local to a particular portion local to a region local to a city right so that ah definition of locality may vary from application to application but what we require that your ah devices like that traffic light device etcetera should be able to run this application which can take a call right so those are things
• 16:30 - 17:00 nevertheless this is this is about the fog so if we look at fog computing enable some transactions and resources at the edge of the cloud rather than establishing channels for the cloud storage and you utilization so rather than just transmitting it do some sort of a transaction processing or application running on the things fog computing reserves reduces the need of bandwidth by not sending every bit of information to the cloud channels over the cloud channel instead aggregating at a certain axis ah point
• 17:00 - 17:30 so it aggregates and send the aggregate data this kind of distributed strategy may help in lowering cost and improve efficiency so this sort of it is a distributed ah strategy and this type of distributed phenomena may help us in lowering the overall cost not only in terms of monetary if the cost of transmission in terms of time etcetera and i can we can do efficiency right i can i can run several applications which can be real time and type
• 17:30 - 18:00 of things so ah this motivation is obvious already ah whatever we have discussed the motivation the fog computing a paradigm that extends ah cloud and its services to the edge of the network ah fog provides data compute storage application services to the end user if you see it says some sort of a small form of a ah instance of the cloud for that local type of things right so its its a doing some sort
• 18:00 - 18:30 of a computing or giving some sort of a cloud service at that time the at ah at that portion of that ah um region ah recent and we and there is another ah side of the things because we have ah several series of developments one is the smart grid ah other is the smart traffic lighting in cities specially cities connected vehicles or strong regular networks
• 18:30 - 19:00 which is coming up and also the software defined network so these are the different aspects which are itself is a ah um topic ah to work at but the smart grid smart traffic lighting smart vehicles as ah software defined network and so on and so forth they are becoming pretty popular and in turn they generate huge volume of data right everyone is generating huge volume of data which are being transmitted
• 19:00 - 19:30 at the higher ah up in the layer for doing that so all ah this ah different aspects has motivated or what a what it has pushed the push the processing towards doing it at the edges or intermediate layer rather than pushing everything to the cloud so this is this ah is what we look at the fog
• 19:30 - 20:00 so this is ah the same thing what we discussed so we have one in this cloud so which has a datacenter with huge capability massive parallel data processing big demand big data mining machine learning algorithms etcetera so which is they are and should be their intermediate layer which is more near to this ah edge or the devices so they are can act as a fog so
• 20:00 - 20:30 they are ah they can be there can be fog sides with real time data processing data caching computation of offloading and those type of things so these are not so powerful at that but as such they are intermediate devices we are which are used for transmitting data so that these are this can be used at the at the end or the at the front end or the edge or the last mind what we say what we have the sensors we are connecting different type of data perform data pre processing and compression mobile ah device serve as a human
• 20:30 - 21:00 computer interfaces like this these are the different type of things which are transmitting out here and in turn transmitting to the things so these some sort of communication yes if we see that both way arrow can be taken a call at this end itself right without transmitting the whole data at the things it may be some sort of aggregated reporting and type of things or aggregating the data and taking putting it to the cloud for ah running some intelligent
• 21:00 - 21:30 algorithm and machine learning based algorithm and type of things so we have more interactive and more responsive end to more computing power and more storage end at the other end so ah instead of just putting a channel to transmit everything to the cloud and compute and come back we are doing some intermittent the provisioning of intermediate ah processing for ah to serve the application based so this is this is ah
• 21:30 - 22:00 definitely a major motivation and ah what we try to look at that has as we have seen that the typical properties of ah cloud that ah having ah here infinite scalability theoretically or quote unquote infinite scalability ah or ah off loading or having ah infrastructure ah no need of maintaining infrastructure at the client end or meter services those need
• 22:00 - 22:30 to be ah need to be preserved or respected right ah in case of a fog and those are ah definitely are still there as what we are discussed so there are fog computing there are several enablers as as ah those are true for our cloud computing also one is that virtualization so virtual machines can be used as the edge devices right so there are there can be virtual machines containers ah or containers services
• 22:30 - 23:00 ah are reduces the overhead of resource management by using lightweight virtualization or what we say container based application or services ah is one of the popular container is ah docker container right so it ah the idea is it docks into that particular things and run on the thing so you dont have to that dependencies is carries along with the thing right so its
• 23:00 - 23:30 a again a separate topic ah um it possible we will discuss sometime but ah that is this docking or container services are becoming very popular so that is another enabling technology out here such this oriented architecture as as we have ah discussed which is a ah enabling technology for cloud also ah is ah here also that soa is a style of software design where services are provided to the other components by application
• 23:30 - 24:00 component a component through a communication protocol over a thing so you have a service oriented architecture which three major component of service provider service comma consumer and service registry so so that heterogeneous loosely coupled ah parties can talk to each other right so in soa architecture is one of the driving ah enabling technology and also what we are looking ah seeing at is the software defined network right sdn so sdn is an approach of using open protocols like for example open flow to apply
• 24:00 - 24:30 globally aware software control at the edges of the network to access network switches routers that are typically would use closed and proprietary from one so this is this is ah another ah technology which which as which is becoming pretty popular or already popular in software defined ah network and which which is a which is enabling technology for our
• 24:30 - 25:00 for for fog also so with this ah several enabling technology fog is a becoming a reality and being deployed and used in several cases so in looking at so we should not see that fog as a replacement of cloud it is it is not a replacement of count not ah neither a ah competitor in that sense right it is basically offloading some of these workload
• 25:00 - 25:30 from the cloud to this ah edge devices because the resources are available because there are applications which are real time and needs more ah more quick responses and overall process may be cost effective and efficient so fog edge devices are there to help cloud datacenters to better response time for real time applications right handshaking amount
• 25:30 - 26:00 fog and cloud is needed appropriate handshaking or synchronization ah between these ah fog and cloud is ah very much needed broadly benefits of flog computing can be that low latency and location awareness so it is aware that which location is operating widespread geographical distribution ah especially with the sensors etcetera so it has ah thing mobility there is another important things like nowadays devices are we have lot of mobile devices right so ah the distance from the cloud ah or the intermediate devices which which a
• 26:00 - 26:30 div ah say end device passing through the intermediate devices will change ah based on the mobility of the things now this require a resynchronization reestablishment of the path had it been in locally somewhere it may is ah the computing and response time so low latency and location awareness widespread these mobility very large number of nodes
• 26:30 - 27:00 with as we are discussing with sensors and things predominant role of wireless access right huge volume of wireless accesses strong presence of streaming and real time applications so these days we are having a huge streaming and real time applications and which requires quick response time huge volume of data and type of things need to be processed quickly ah and may not require all data to be transmitted right so this huge volume of data can be locally ah processed and aggregated data can be transmitted
• 27:00 - 27:30 so that the overall response time improves in a considerable way ok strong presence of streaming and real time and heterogeneity different sort of devices different type of the mix and ah inner and the heterogeneous so i can have ah it some sort of a fog ah sort of ah intermediate ah um framework which basically talked to some devices which may
• 27:30 - 28:00 be different from other devices like i have a group of sensors i have a their sink node which talks to the sensor also do this aggregation which may be different in another ah other set of ah sensors which has a different sink note but nevertheless when they do this aggregated data that is in a more standardized format so i can handle heterogeneous devices so advantage is already we have already ah discussed ah so can be distinguished from cloud by proximity to the end user that is one of the advantage or over this free service
• 28:00 - 28:30 cloud dense geographical distribution and its support for mobility right so we can have ah instead of scintillate i can have a lot of distribution it provides low latency low awareness and improves quality of service and real time applications right so there is a there is a chance of ah better performing the things rather we try to look at it is not isolated fog but fog plot cloud o a as a whole can give a better ah service to these
• 28:30 - 29:00 consumers right in terms of ah cost in terms of scalability in terms of ah your efficiency and type of thing specially applications where we have high quality of services and real time services streaming videos and type of things there are of course some ah issues ah related to ah security so one is that ah as devices
• 29:00 - 29:30 are dispersed right so maintainability of the security ah protocols at different fog devices is a serious challenge right so it is at different location now had it been cloud you have a provider at a particular centralized things you can put lot of security mechanism in the place but if you once you distribute over the form then you have to maintain so many things ah on the on different edge devices so it is not only the data processing etcetera
• 29:30 - 30:00 so there can be ah security issues so an man in the middle attack type of things can happen so as devices are disparts differ a as as this computing ah data are being there in the in different edge devices so there is a issue of ah things of man in the middle attack can be there there are issues of privacy issues ah as as a as same that it is ah it
• 30:00 - 30:30 is being ah processed at different edges and ah whether the data leakage is there then whether you know about the things like if i consider smart grid or connected vehicles so if you do the intermediate ports processing whether you are basically tracking ah the vehicle or looking at the ah processing of the consumption of a individual house or home
• 30:30 - 31:00 and type of utilization those can be there like in case of a smart grid smart meter installed at the consumer home each smart meter and smart appliance as an ip address a malicious users ah can either tamper with its own smart meter report false reading or spoof ip addresses and so on and so forth so whatever it comes with ah typical network security related issues may also come ah may also be ah problem out here so may be a challenge
• 31:00 - 31:30 so there are definitely ah security issues there are security issues in the cloud but this extend that to much more things as you have ah different devices are activated so what we sees today that this fog is just not extension of the cloud its a its a necessity based on the different application huge volume of data and the devices intermediate devices becoming more ah resourceful right and they are able to capable to do this type of ah
• 31:30 - 32:00 calculations computation and secondly in case in order to in doing so i may not be doing all these high profile computation but i can we can basically do ah some sort of a aggregation of the informations and sending only the aggregated informations which lowers the ah basic ah bandwidth requirement ah intermediate bandwidth requirement also lowers the data load at the
• 32:00 - 32:30 cloud end so what we see it is a ah technology which is a need of the hour and ah especially with iots and other things coming in a big way so with this we will stop today thank you