In this video, the speaker delves into the intricacies of the cloud computing marketplace, highlighting the challenges and solutions for selecting the right cloud service providers. With a myriad of providers available across IaaS, PaaS, and SaaS levels, customers often face difficulty in making informed decisions that meet their Quality of Service (QoS) and Service Level Agreement (SLA) requirements. To address this, the concept of an "intelligent broker" or middleman is introduced, which uses tools like fuzzy inference engines to help match customer needs with the best service provider. Additionally, various techniques and simulations are discussed to demonstrate the effectiveness of these broking mechanisms in the ever-evolving cloud marketplace.
Highlights
Learn about the need for an intelligent broker in the cloud marketplace! 🤓
Understand why evaluating different providers' QoS is crucial. 🚀
Explore how fuzzy inference engines aid in provider selection. ⚙️
Delve into challenges like vendor lock-in and SLA maintenance. 🔑
See real-world simulations of cloud provider selection techniques. 🌐
Key Takeaways
Selecting the right cloud provider can be tricky with numerous options available! 🌥️
An intelligent broker can help match customers with the right cloud service, using tools like fuzzy inference engines. 🤖
Providers differ in quality, so evaluating QoS and SLAs are crucial for decision-making. 📊
Vendor lock-in can be a major issue, making it hard to switch providers. 🔒
A middleman or broker ensures customer interests are safeguarded while choosing providers. 🛡️
Overview
Navigating the cloud computing marketplace can be a daunting task, especially with so many providers offering IaaS, PaaS, and SaaS options. Customers are often overwhelmed by the choice and need an efficient way to select the provider that best meets their quality of service (QoS) and service level agreement (SLA) requirements. Enter the concept of the cloud broker, a game-changer that simplifies decision-making.
The session explains how an intelligent broker acts as a middleman, helping safeguard customer interests while matching their needs with the appropriate cloud service provider. This involves leveraging sophisticated tools like fuzzy inference engines to evaluate the capabilities and trustworthiness of providers, ensuring the best possible match for the customer’s requirements.
Moreover, the video highlights several challenges faced in the cloud marketplace, such as the risk of vendor lock-in when moving services and the complexities involved in monitoring SLA adherence. Through various techniques and real-world simulations, the effectiveness of these intelligent broking mechanisms is vividly demonstrated, bringing clarity to cloud service selection.
Chapters
00:00 - 01:30: Introduction to Cloud Marketplace and Providers The chapter introduces the concept of cloud marketplaces and providers in cloud computing.
01:30 - 04:00: Need for a Cloud Broker This chapter discusses the need for a cloud broker in the present-day scenario where there is a growing number of providers at various levels including IaaS, PaaS, and SaaS. With increasing customer expectations and specific requirements, a cloud broker is necessary to help navigate the complex landscape of providers and services. The chapter highlights the role of a cloud broker in bridging the gap between customer needs and provider offerings.
04:00 - 07:00: Motivations and Objectives of Cloud Brokering The chapter titled 'Motivations and Objectives of Cloud Brokering' discusses the challenges and goals involved in choosing a cloud service provider. It highlights the customer's need to select a provider that meets their expectations and honors service level agreements (SLA). Additionally, the provider's objective to maximize profit is also discussed, along with some mentioned security aspects.
07:00 - 10:00: Decision Making and Fuzziness in Provider Selection The chapter explores the complexities of decision-making in cloud marketplaces, emphasizing the challenges in selecting appropriate service providers among numerous options. It highlights the marketplace's dynamic nature, where both consumers and providers interact, and the presence of various criteria influencing decision-making. The chapter suggests a brokered approach to navigate these options effectively.
10:00 - 14:30: Approaches to Provider Selection The chapter titled 'Approaches to Provider Selection' discusses the rapid growth in available cloud services and highlights the variety of service providers. It notes that there are numerous providers with varying quality of services, and these differences are important because each provider caters to different customer use cases, each with specific requirements.
14:30 - 19:00: Fuzzy Inference Engine for Provider Selection The chapter discusses the use of a fuzzy inference engine for selecting providers. It highlights the variability in customer requirements, emphasizing that each customer's needs differ. The review of these requirements is essential and depends on specific use cases. Additionally, the chapter notes a reference point regarding the number of virtual machines (VMs) created by Amazon per day, particularly from the year 2007 onwards.
19:00 - 24:30: Monitoring and Evaluation of Providers In 2011, there was a significant shift in demand, necessitating the need for a middleman in the form of an agent, broker, or intelligent broker. This middleman would serve to recommend the best cloud provider to customers and ensure their protection.
24:30 - 30:00: Simulation and Case Studies The chapter discusses the role of a 'middleman' or intelligent broking service in protecting the interests of customers when selecting a cloud service provider. It highlights the need for such services to ensure customers get the best provider based on their requirements.
30:00 - 35:00: Future Directions and Research Opportunities The chapter titled 'Future Directions and Research Opportunities' delves into the ongoing efforts and important considerations in selecting cloud providers using intelligent agents or brokers. A major theme discussed is the concept of trustworthiness and its critical role in decision-making regarding cloud services. The chapter highlights the necessity of flexible selection processes and the importance of developing methods to calculate the trustworthiness of cloud service providers.
35:00 - 40:30: Conclusion and Closing Remarks In this final chapter, the discussion revolves around various aspects of cloud security, specifically focusing on trustworthiness and service monitoring. The chapter highlights the importance of ensuring services are closely monitored, emphasizing the critical aspect of vendor lock-in as a significant challenge within cloud services. Concluding remarks reiterate these key points, underscoring them as crucial elements to consider in cloud security strategies.
Cloud Computing Broker for Cloud Marketplace Transcription
00:00 - 00:30 hello ah today we will discuss ah a aspect
of cloud computing where ah we will see that if there are ah number of ah providers so
how do i select whether there is a way to
00:30 - 01:00 select the things and how what what should
be the approach of the things so what we see in a present day scenario that ah there are
increasingly availability of number of ah provider at various level is level pas level
saas level ah so at ah various level and ah there are several requests on the from the
ah customer side right so customer has a expectation or requirement
per se and the providers provides a particular
01:00 - 01:30 things with some miscalis right so ah first
of all the customer wants to know that where ah which provider how to select a provider
out of a branch of ah providers available where ah the customers expectation will be
satisfied and the sla will be honored ah and ah the providers also wants to maximize his
profit right some of the aspects already we have been seen
so given all these security and other aspects
01:30 - 02:00 in in place we dont to look at that whether
in this cloud quote unquote marketplace how do i ah whether whether the what whether there
is a possibility or approach to look at the things so um ah thats exactly we like to look
at so it is broker for cloud marketplace so what we look at there is a number of providers
available there are several customers so its a marketplace to how to select the thing
so if you ah look if we if we see that there
02:00 - 02:30 is a rapid growth on available cloud services
right so it is ah there are several providers several service providers right and ah there
is a rapid growth of the ah things like and huge number of providers with varying quality
of services are things are there right so different providers has different quality
of services and type of things different type of customer use use cases so customer based
use cases each with different requirements
02:30 - 03:00 or with several requirements customer use
cases are there so thus the review the the requirements of the customer is ah based on
the need of the customer it varies from customer to customer so that is another aspects and
ah from a from some reference what we see that ah this number of amazons vms ah created
per day ah from two thousand seven to somewhere
03:00 - 03:30 two thousand eleven it has it has remarkably
changed right so there is a huge demand for the things
so keeping all those ah things in the ah in the um offering or in keeping in this mind
there is a need of a middleman right so i we there is a need of a middleman we can called
as a agent or broker or sometimes it it may be a intelligent broker to suggest the best
cloud provider to the customer or safeguard
03:30 - 04:00 the interest of the customer right
so ah i require a quote unquote middleman or intelligent broker serve broking service
which safeguard the ah definitely safeguard the interest of the customer and also ah suggest
the best cloud provider or cloud service provider of the customer based on its ah requirements
right so this is nee this is a need of the
04:00 - 04:30 our or people are working on it that how ah
how can i select how can i broke ah how can i ah select a things with a intelligent agent
or broker so there are definitely motivation that these
flexible selection of cloud provider there is one of the things trustworthiness of the
provider plays a important role how much i ah trust or i can be able to trust a provider
ah is there how do i calculate the trust is
04:30 - 05:00 a big challenge yes some of the aspects we
have looked into or discussed during the security ah when we discussed about cloud security
so starts worthiness is important right so ah that is one of the factor monitoring
the service that ah whatever the the things are there how services are monitored and ah
of course there is important aspects of vendor lock in right so whenever there is a vendor
so there is a problem of vendor lock in so
05:00 - 05:30 that means ah you work with some provider
and you get locked with the provider because of the services right now if the providers
ah is not giving the service up to the mark or the provider ah or the ah csp ah does not
provide the service then going to another vendor is difficult and it is not only that
now you need to go for a ah in the terms and condition of the or the customer has to go
for the terms and condition of the provider so vendor lock in is also important aspects
which we need to look into ah the thing so
05:30 - 06:00 there are several motivations there may be
several other motivation but end of the day what we ah look at that ah i i may need i
need a the mechanisms to ah or a broker to find in find the best possible cloud ah things
so based on this motivation if you look at the objectives so one is the selection of
most suitable provider ah satisfying customers
06:00 - 06:30 qos requirements that is the major objective
calculation of the dis degree of sla satisfaction trustworthiness of the provider may be one
of the subjo sub objective right like how ah how i can guarantee that the degree of
sla satisfaction and trustworthiness of the provider that is the other objective which
one is selecting and type of things decision making system for dynamic service ah migration
based on the experienced qos is another aspects
06:30 - 07:00 right
so it is ah if the if the if the provider ah if if i dont get that appropriate service
or ah that is from the customer point of view or from the aspects of the that ah a middle
ware that whether i can migrate the service from one provider to another provider right
like ah we look at ah that vm migration if the my if there is a um there is a any ah
outage on the vm whether the application will
07:00 - 07:30 be migrated on the things
this type of things has been supported by various various organization as we look at
ah on the on the hardware and type of things like at the at the vm level but whether this
type of migration on the whole application from the one provider to another provider
that is may be there so once we do a migration or any of this or many of these aspects we
need to take a call all right so there should be a decision making process
into the things right um that is one aspect
07:30 - 08:00 so i need to have a some sort of a decision
making ah support which allows me to do that secondly another aspects is there that ah
most of the cases ah things are not very crisply defined right the requirement wise the ah
your um performance wise the thing is the thing is the all these parameters not very
crisply reach us there is lot of fuzziness specially in fine in giving the details of
the customer so i what i what we need to do
08:00 - 08:30 we need to account for the overall those aspects
also so there are ah different approaches what
ah its tried few of them ah are here but there are different approaches people have tried
like ah one is that cloud cmp a tool that compares cloud providers in order to measure
the qos the offer and helps user to select
08:30 - 09:00 a cloud so there is a cloud there is a tool
ah which ah compares the cloud providers in te in order to measure the quality of service
they provide so based on that the a user or customer can select a cloud there can be fuzzy
selection mechanism right so there is there can be fuzzy provider selection mechanism
so that is fuzzy provide a selection mechanism so that if which takes care of the fuzziness
that is also which ah well suited because
09:00 - 09:30 there are a lot of fuzziness into the description
there is a framework with a measure of satisfaction ah with a provider satisfaction with the provider
for keeping in mind the fuzzy nature of the user requirements right
so keeping considering the fuzzy nature of the user requirement so measure of satisfaction
with a provider ah how to calculate the that based on this measure the um consumer can
take a ah call provider selection framework
09:30 - 10:00 which takes into account the trustworthiness
and competence of the provider so there is ah other way of another way of looking at
it is that provide there is a folk provider selection framework which takes into account
the trustworthiness and competence of the provider all right this ah trustworthiness
competence of the provider so if you look at these different approaches
so ah this one is there that it attempts to find a ah suitable provider for the things
secondly there are ah several overlaps between
10:00 - 10:30 those type of things right may overlap in
the sense that how they consider the user or the customer requirements or inputs ah
the overall ah mechanism of selection and so and so forth right
so if you look at customer qos parameters some of the things like if we consider infrastructure
as a service ah so what we ah look at that
10:30 - 11:00 ah twenty four cross seven availability ah
with hundred percent requirement like it is its the i get a returned what i pay for it
and there are requirements in terms of the bandwidth requirements ah other type of things
ah in case of a software wise service the uptime requirement may be male vary again
ah the response times is critical rather if you look at this these are the several type
of a components which are there so more qos
11:00 - 11:30 parameters can be there are like in terms
of things like even i can talk about we can talk about storages we can talk about ah other
ah um ah quality of ah services like even security trustworthiness competence risk and
so and so so forth like availability and so and so forth
so ah ah so that is ah in case of any cloud
11:30 - 12:00 provider we have a set of ah parameters which
the ah provider provide or the promised qos values right they based on thing that they
provider promise different type of ah provider different type of qos values or qos parameters
like i the provider says there is a ninety nine point nine nine percent uptime or so
much ah security level or ah maybe this much
12:00 - 12:30 bandwidth available and so on and so forth
and ah there are based on that we can have different trust on those things the trust
can be overall over all the provider or i can even trust on a on a ah on different parameters
like i like availability things i say that that this based on the experience this trust
on these things ah trust on ah a particular ah provider is something right zero two nine
scale i trust it in zero point ah zero two
12:30 - 13:00 one scale i trust it in ah point eight point
eight value and trust value of point eight and so so forth right
so there are different ah trust values and they have been kept independent as they pertain
to different parameters right there the trust value can be kept independent as there can
be different type of parameters for looking at the ah as as those ah trust values like
promise a a particular promise one can have
13:00 - 13:30 a trust value one promise two and trust value
two and there are different mechanism for trust calculation several things for trust
computation type of things it goes for a for ah history of those things and ah looking
at the history and then calculate and or third party auditor or third party monitoring units
which monitors those trusts and there are mechanisms to calculate the trust
so if we look at a typical marketplace architecture
13:30 - 14:00 so there can be several components right one
is a there are set of providers right like a provider one two three p one p two p three
pk ah there are components like the major black box may be the provider selection decider
right so the customer comes with a set of requirement and requests the ah this particular
ah selection ah box and it takes the other
14:00 - 14:30 inputs and and decide the things
the other ah other blo blocks are like maybe there can be monitoring module which are constantly
monitoring whether there is a whether there is a request or not it is monitoring the providers
right there can be a migration module so this was monitoring module it says that the qos
experienced by the ah vms for different providers and you put it put to the provider trust calculator
right and there is other component is a migration
14:30 - 15:00 module which is the migration deciders ah
so based on this provider trust calculation i can this is it has goes for a input of the
migration decider and this migration module ah no keep a track that which are the providers
there what are their loading capacity and type of things and based on those it goes
on a which need to be which providers need
15:00 - 15:30 to be selected so it goes for a provider selection
right so ah one side that requests requirement so
the selection procedure provider selection procedure is dictated primarily this monitoring
of the different qos parameters and type of things so this may be one one such approaches
but there are a lot of nitty gritty into the things like customer requirement how it will
be taken whether it is a very crisply defined
15:30 - 16:00 or there are fuzziness of the things if there
is a fuzziness how to handle this fuzziness or how to account for these ah fuzziness so
there can be furry fuzzy inference engine which are can be deployed out here this particular
ah things which we are discussing is to of my students work for their ah projects one
for b tech and one for m tech projects on working on an intelligent ah um ah broker
for cloud market phase using ah a fuzzy approach
16:00 - 16:30 using a fuzzy inference engine
so ah there are ah a um ah major block diagram of this architecture so provider selection
if you look at selection of the provider is done using a fuzzy inference engine right
so there can be other approaches but in this case what we did in fuzzy inference engine
so input is the qos offered by a provider
16:30 - 17:00 and its trustworthiness right so it takes
the input as a qos from ah offered by the provider and its trustworthiness output is
suitability of the provider for the customer right
so i have that one side that ah providers ah competence so to say that ah quality of
services offered by things and its trustworthiness other size what we have these ah customers
or the users requirement right so ah and these
17:00 - 17:30 ah this fuzzy inference engine in this case
takes care or consider these two and finally give a output that which should be the ah
base providers for this particular customer so that is ah that that is the goal of the
finding a suitable provider in a cloud marketplace right customer request request is dispatched
to provide with maximum ah suitability membership
17:30 - 18:00 function are build using user requirement
so ah the membership for function on the providers ah on the customer side can based on user
requirement that can be build on the fly and then ah put into the system
so they are not only that there will be a set of ah rule set on the customer side that
need to be accounted for also so in from the
18:00 - 18:30 customer requirement input membership function
for the ah fuzzy ah system and ah there is a rule list and there is output membership
function based on those things right whereas the there is providers offerings like what
the provider can offer fuzzification of those things ah there is a rule evaluation based
on the customer ah rule list and defuzzification and providers fitness to the ah customer requirement
there is other aspects of the ah other aspect
18:30 - 19:00 of the thing right
so this whole thing lead to provider ah selection so this ah this basically fits into the system
and it keeps ah provider selection so like a typical scenario where ah for this experimentation
that provider selection input membership function with different thing like extremely poor poor
ah very poor medium good very good excellent and this type of different ah um ah different
states and ah using ah this ah input fuzzy
19:00 - 19:30 membership function so what we do that is
based on the qos and based on these that what is ah the ah input variable is the ah i means
with input has trust that ah having that membership function again at different ah scale then
we construct that ah output membership function generally the output membership function ah
with ah those levels of extremely poor very
19:30 - 20:00 poor poor medium good very good and ah excellent
based on this we calculate that ah what should be the suitable suitable ah provider for the
things there is a monitoring ah module which monitors
the ah overall processes so ah um ah it takes different ah ah different um available experie
experience values that is availability experienced
20:00 - 20:30 by the particular vm bandwidth experienced
by a particular vm or a particular ti time then the what are the different promise for
different vms by a particular provider and so and so forth and generate that sla satisfaction
for a particular vm i on availability sla satisfaction of a particular vm i on ah um
bandwidth and so and so ah things
20:30 - 21:00 so performance of ah this ah sort of ah service
provider or service instances for a particular purp service instance i in the current monitoring
period is is generated so that means what we try to do this they are we are trying to
monitor be service they are performances of different providers and based on different
service instances right or a size right that
21:00 - 21:30 how they ah how they are performing this is
important for selecting a particular provider so what we are doing if you look at the big
story so we have one side the customer it has some requirement i need to fit this requirement
to some provider or a set of provider and such as the customer that see this is the
provider is there for the provider in there is a continuous monitoring of the thing right
that with ah even with ah performance histories
21:30 - 22:00 right so what are the different performance
what is what is the what is the promise value and what is the experienced value right one
is the promise what the ah cloud provider gives and what the experience ah and the ah
based on this promise like availability bandwidth how much is experienced by the user or the
customer with ah at ah different point of time
so while calculating a at time t i consider
22:00 - 22:30 for different provider this monitoring unit
gives that for different service instances what how is the performance of this ah of
a particular provider or ah at a particular provider i based on that and taking the suitability
of the customer we the system sets a then this is the possible ah provider or it can
even rank that is the first one second one and so and so forth
there will be a different other factors like
22:30 - 23:00 here may because we are considering availability
and bandwidth there can be other different other factors even cost is a factor and requirement
ah plays a big role that whether mine is a time critical or ah say ah data critical or
more accuracy is more important or the time is more important and type of things come
into play so ah if if the if there is something running
on a particular system provider then if there
23:00 - 23:30 is a need of migration from one provider to
another so there is a need to have a ah thing called fuzzy ah there is a there is a another
decider block is needed for migrating ah for the migration decision right so it ah used
again it uses the fuzzy inference engine there can be different input f one ah f two f three
f three for ah different providers ah and output will be the degree of a sla satisfaction
for a for a service instance i right if the
23:30 - 24:00 degree of a sla satisfaction is less than
threshold is then migrate so what i what we are doing i am think i i
am my particular from the customers particular ah service instance is running so and degree
of satisfaction is being calculated if the degree of satisfaction is less than the threshold
there is a need to migrate to the to a new provider or a new ah service instance right
here also ah this ah it is done through fuzzy
24:00 - 24:30 ah inference engine and this is a typical
ah instance of a output membership function with ah different ah levels i will shows that
the degree of sla satisfaction based on that ah it is decided that ah whether the migration
is ah needed or migration is ah need to be executed for the for a particular ah service
instance
24:30 - 25:00 so similar the ah selection of the target
provider is somewhat similar there similar to this provider selection procedure which
is being monitored and so on and so forth selection done using a fuzzy in inference
engine ah in this case also so what we what we have ah here is that that
ah it is true for any type of service provider
25:00 - 25:30 though primarily it is more ah pertinent for
is type of market place where this ah infrastructure as a service it is being provided but it is
true for any type of ah any type of ah cloud right so here we did some ah case study is
to ah so that that ah whether how much effective it was ah that ah whether it makes sense to
fuzzify the or or using this fuzzy approach
25:30 - 26:00 so we did a simulation with ten providers
with varying offer here varying ah offered keyways hund five hundred requests for vm
ah are considered ah there is a done through a year long simulation that means over a year
the time period has been taken as a year taking on a ah daily basis few provider exhibit performance
degradation so degraded qos ah parameter follow
26:00 - 26:30 a gaussian distribution so the if there is
ah a some sort of a ah degraded ah qos has been ah done using a gaussian distribution
and comparison made with conventional minimum cost crisp broker
so that means ah there is no this sort of ah fuzzy system if there is a crisp broker
that means it takes a call yes or no type
26:30 - 27:00 of a things then ah with a with ah minimal
cost on those um decision making so and then how how it performs compared to that like
one the one is showed that the average availability this ah blue color is the user requirement
ah green is the for the crisp ah broker and red is the fuzzy so what we see that it is
it may not be always following that user requirement
27:00 - 27:30 but ah in a sense it is better than ah the
crisp ah approach similarly this is for average bandwidth again
here blue is the user ah requirement red is the the fuzzy and green is the crisp ah broker
so here also we can ah see that is ah better than this crisp ah broking type of thing similarly
average cost per vm per hour ah the if we
27:30 - 28:00 look at the same way that ah over a scale
the fuzzy gives a ah better result so similarly though it is proba predominantly
for ah saas ah is type of ah provider but we did ah some experimentation on the saas
or some simulation on the saas marketplace so here also some sort of a ah ten providers
are considered with five hundred ah service
28:00 - 28:30 requests again simulated over three sixty
five days few provider exhibit performance degradation qas parameter follow a gaussian
ah means qos ah degraded degradation degraded qos follow a gaussian thing and compression
with the conventional crisp thing here also what we see that ah you the dotted line is
the user requirement ah red is ah the um fuzzy
28:30 - 29:00 and crisp is the green so over again arrange
the fuzzy seems to be ah following better that may not be always the case but it is
ah it is likely to be better ah it is likely to be mapping the user requirement better
way that fuzzy inference engine similarly this is on the ah average ah response
time ah over ah over again ah the particular
29:00 - 29:30 period of here in this case thirty days and
that how how the overall performance is there with fuzzy crisp and ah with respect to user
requirement similarly this is the average cost ah of ah a particular the um instance
of the things so what we see that ah we basically implement
ah ah this sort of a fuzzy inference engine
29:30 - 30:00 in order to map these ah ah user requirement
two way to the rather mess the user requirement with the providers ah offerings and try to
feed the best provider with the ah with the user ah with the particular user requirement
or set ah a for a particular user ah of the
30:00 - 30:30 ah for a particular service instances
and what we like to look at there are ah several future scope especially in the ah research
formed for a birth for this particular one there is a ah specification of flexibility
in the user qos requirement is there right that is ah whether we can make it flexible
of ah qos the requirement comparison again existing approaches on production workload
right so that ah on a life workload ah whether
30:30 - 31:00 we can compare several classes of customers
now there can be ah you see there customers can be categorized into different classes
of customers right it is instead of taking individual customer or user i say that this
is user is this category of customer this user is this class area customer and ah so
and so forth and it all ah and also depends
31:00 - 31:30 on the type of applications type of ah services
is looking for ah in those type of things and extending the things for any type of services
or x rays type of services may be a important aspects of the things
never delays this ah this type of ah finding appropriate match in a cloud marketplace is
gaining a lot of interest ah both from the ah real life point of view or commercial point
of view and from the research point of view
31:30 - 32:00 how to find a suitable things how how to measure
or how to say it formally that what i what has been selected suitable is the best possible
things right if not the ah if not the best but it is something near based with some some
some sort of a ah guaranteed type of services so ah this is again those who are interested
in future study research so this is a important aspects of a because it involves lot of aspects
right one is that how to monitor them how
32:00 - 32:30 to calculate trust competence and ah sla guarantees
or sla values says or how slas has been ah satisfied over time and based on that at a
current at a at a present time t how to project that what are the things are there so it is
it plays the important role in in ah modern day ah cloud infrastructure or making clou
cloud ah computing more popular across the
32:30 - 33:00 things
so with this discussion we we stop today and ah we will we will be continuing with some
of some of the new some of these aspects of cloud computing from different ah perspective
in our future lectures thank you