Cloud Services Ranking by measuring Multiple Parameters using AFIS

Assigning a level to a number of choices is referred to as ranking. The concept of ranking is applied in many situations, wherein, team rankings, player rankings, university rankings, and country rankings are commonly used these days. Similarly, in cloud standardization, ranking the web services is a principal concern, as it is a relatively new approach, assigning ranks to cloud facilities has gained signiﬁcant attention from researchers across the globe. Furthermore, cloud services standardization is an important idea as it is necessary if it is required to assign ranking for cloud services. There are few limitations in cloud standardization as there is no technique to check valid services and its classiﬁcations, wherein, the standardization of cloud services will play a major role in controlling the redundancy of cloud services. In this article, a new cloud service ranking method is proposed using an Adaptive Fuzzy Inference System (AFIS).


Introduction
Standardization in cloud computing services is a developing concept.However, with time, the concept of standardization is getting importance because of the exploration of new services now and then.Many standards exist today; they all make implicit reference to cloud computing.Some of the standards are quite new; however, still, there are some deficiencies.Therefore, there exist a lack of maturity in this perspective.Cloud services activities take a technologydriven approach that focuses on various challenges like portability, efficiency and information security (Alkalbani & Shenoy, 2015).An automated method of cloud ranking is the key element in the field of cloud services standardization.The objective is to offer a standardized service provider, considering the gap between cloud identification standards and cloud service.This gap can be evaluated, given compatibility, deployment methods, data security and the types of service (Kadhim et al., 2018).It should be kept in mind, that the ranking in the cloud computing environment is different than other systems.The reason for the difference is the existing infrastructure of the cloud computing environment.This existing infrastructure connects different components through the internet, and most internet connections are not predictable.Due to unpredictable nature, a different level of quality of service has been allocated to different users, being a major reason that the concept of a ranking system came into being.This ranking system receives the requests from different users, which may differ w.r.t their requirements.Then, this system will look for some services for users and assign a possible rank according to the Quality of Service (QoS) (Mohammadkhanli & Jahani, 2014).However, it does happen that for the same cloud service, different users get different level of QoS.Therefore, a ranking system is the needful to facilitate the user requests with different levels, Sagheer Abbas et al.
to execute this task, a framework needs to complete these responsibilities.This framework must have the aptitude of getting data from users and decide on the superlative service.This ranking framework must evaluate the facilities and determine their importance.In cloud computing perspective, there are many cloud providers by whom facilities of different features are being offered, with different characteristics such as efficiency and cost, etc.It is normal, that when you have many options, the decision of choosing only one option is very difficult.Similarly, when there are many service providers, the decision of choosing only one cloud computing service is a tough and challenging job.It is obvious that before having an efficient ranking system, its standards should be considered first.It is important to select an optimum algorithm for service ranking, and it is equally important to measure all qualitative values of the services.In this research, we focus on reviewing these approaches.As the value of cloud computing is increasing day by day, therefore many tech giants such as Google, IBM, HP, and Amazon started offering cloud services as well.However, it is very difficult to identify whether a cloud service is good to use or not.That is the reason it is a challenging task to select the best cloud service among various cloud services.The selection at times becomes difficult to deliver (Qu & Buyya, 2014).Computational Intelligence has four branches, Fuzzy (Atta et al., 2018) (Jiang et al., 2018) etc.The organization of this article is as follows; in section 2; Cloud Indexing is presented, section 3 provides the indexing controller methodology followed in this article while section 4 elaborates the results and discussion.A summary of the article is provided in the Conclusion Section.

Cloud Indexing
Giving ranks mean assigning some value and then sorting that choice according to its value, wherein, normally the lowest value represents the best choice.The lowest the value, the best rank it will be.Ranking in cloud services is getting fame as the days pass on.However, in a cloud infrastructure, ranking is slightly different because of the naming convention and the existing cloud infrastructure.Nowadays cloud infrastructure connects with new cloud services (Alkalbani & Shenoy, 2015).User's point of view matters a lot, according to the user's demand, CSP offers services with different names.It is a complex procedure to know if a certain service is best fulfilling the user's demands or not.Due to this complex nature, right now there is no dedicated framework for automatically assigning the indexing and ranking of cloud services (Ghahramani et al., 2017).Furthermore, there are different levels of quality of service in cloud computing (Jelassi et al., 2017).When the indexing procedure is going on in cloud services, the key factor is that the requirement of the user should be satisfied.Such kind of framework is desired that will fulfil these requirements.In Fig. 1.It is presented how to manage the indexing.By looking on to the above figure, it is known that indexing manager will receive the information and after that, process it according to the ranking parameters like performance, usability, and cost.Indexing Manager will consider it for the best service as desired by user necessities.Indexing Administrator will also be answerable for other activities as well, i.e. taking characteristics for ranking, the track record of characteristic value, and ranking result.

Indexing Controller
Indexing controller has to keep an eye on the status of the cloud system and it is also responsible to gather the cloud services.Indexing Controller can be a benchmark for gathering the information about the quality of services.After performing the ranking parameter, Using Fuzzy Neural network to rank cloud services for the development of autonomous cloud crawler.Fig. 2 shows the Cloud Mapping Module.The major use of Fuzzy inference for reasoning problems and adaptive control in uncertain environments is useful.The fuzzy inference can deal with erroneous information sources.

Inference Engine
Characterizes administrators and defuzzifier utilized as a part of the surmising procedure.

Membership Functions
Participation work characterizes what degree the fluffy component has a place with the corresponding fuzzy set.In fuzzy inference system, four inputs like cost shown in table

Rule Base
It is a set of "If-Then" rule set that characterizes the derivation demonstrate.The control structure resembles: "If cloud parameter Then what is ranking of cloud".The deduction process, for the most part, includes five noteworthy steps as shown in Fig. 3: 2.5.1.Fuzzification.Input cloud services value into membership functions obtained equivalent membership degrees of to each input variable concerning exact, fuzzy set.

Applying Fuzzy Processes.
Get the membership degree of cloud services using "AND" and "OR" operators 2.5.3.Implication.Get the fuzzy set of each law using the well-defined implication operator.

Aggregation.
Aggregate yield fuzzy sets of full rules using well-defined aggregation administrator as shown in table 7.

Conclusion
Cloud ranking mechanism uses different parameters and determines their priority on given parameters.

table 5 .
1, performance in table 2, security in table 3, assurance in table 4 and output variable in table 5 has its arrangement of enrolment capacities.Mathematical & Graphical representation of the above mentioned I/O MF of AFIS Input variables are shown in

Table 2 .
PerformanceIn table 9 singleton values for the given input regions are shown.Total combinations of input for calculating singleton value are 64.

Table 5 .
Mathematical & Graphical MF of AFIS Input/output variables

Table 6 .
Mathematical & Graphical MF of AFIS Input/output variables

Table 8 .
Rule Mapping of Proposed AFIS

Table 9 .
Singleton Values of Proposed AFIS

Table 10 .
Singleton Values of Proposed AFISIn the cloud computing paradigm, different cloud service providers are offering different types of services with different qualitative characteristics such as cost, performance, security, and assurance.Choosing the best available cloud computing service for a specific application is a serious challenge for users.Ranking based services for selecting the most appropriate service has been proposed to select from the given number of providers.In this article, a new ranking computation system is based on the Adaptive Fuzzy inference system.After performing different ranking conditions, 5 Cloud Services Ranking by measuring Multiple Parameters using AFIS EAI Endorsed Transactions on Scalable Information Systems 06 2019 -07 2019 | Volume 6 | Issue 22 | e4

Table 12 .
Error Rate of Proposed AFIS