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1. SMART CLOUD STORAGE SERVICE SELECTION BASED ON FUZZY LOGIC,
THEORY OF EVIDENCE AND GAME THEORY
Abstract—Cloud platforms encompass a large number of storage services that can
be used to manage the needs of customers. Each of these services, offered by a
different provider, is characterized by specific features, limitations and prices. In
presence of multiple options, it is crucial to select the best solution fitting the
requirements of the customer in terms of quality of service and costs. Most of the
available approaches are not able to handle uncertainty in the expression of
subjective preferences from customers, and can result in wrong (or sub-optimal)
service selections in presence of rational/selfish providers, exposing untrustworthy
indications concerning the quality of service levels and prices associated to their
offers. In addition, due to its multi-objective nature, the optimal service election
process results in a very complex task to be managed, when possible, in a
distributed way, for well-known scalability reasons. In this work, we aim at facing
the above challenges by proposing three novel contributions. The fuzzy sets theory
is used to express vagueness in the subjective preferences of the customers. The
service selection is resolved with the distributed application of fuzzy inference or
Dempster-Shafer theory of evidence. The selection strategy is also complemented
by the adoption of a game theoretic approach for promoting truth-telling ones
among service providers. We present empirical evidence of the proposed solution
effectiveness through properly crafted simulation experiments.

2. EXISTING SYSTEM:
In the context of this work, a cloud based storage service infrastructure can be
modeled as a collection of M storage
services implemented within a hybrid cloud, or even a federation of clouds. The
profile of the j-th service, indicated
as Oj , consists of a quantitative measure of the quality features of the storage
service, and is represented by a vector of N numeric values, namely f_(j) 1 ; : : : ;
_(j) N g, one for each specific feature _(j) i . Since there is no common agreement
on which specific features have to be considered in order to assess the quality of a
cloud storage service, we have decided to keep such elements as generic as
possible, without discriminating if they are related to performance, reliability or
security. The i-th feature can assume values lying within the interval [B(i)
lower;B(i) upper], e.g., [0; 1] for reliability and availability, [0 ms; 50 ms] for
latency. Due to the heterogeneity of the quality features, their values have not only
different ranges, but also opposing meaning, causing difficulties in their
comparison. For a concrete example, if a service exhibits a higher value of
reliability than another one, this indicates that the first service has a better quality.
On the contrary, a higher value for response time states that the first service has a
worse quality. We indicate the first kind of features as positive, while the second
one as negative. Such heterogeneity requires normalization in order to have all the
quality features assuming float numbers lying between 0 and 1 and having the
same meaning.

3. PROPOSEDSYSTEM:
However, most of the customers do not have any data locality constraints and let
the platform to autonomously decide where to locate their data. The selection
process has to be driven by a multi-objective optimization strategy resulting in the
storage service that best fits the budget and demand of customers, typically
consisting in a set of QoS features (e.g., performance, reliability and/or security)
often expressed in a quite vague way, by introducing a certain
margin of uncertainty in the whole process. In addition, in all the available
solutions, the offered QoS features must
match with those announced by the corresponding SSPs, which, however, in some
cases may act in a rational way,
by cheating in their QoS announcements, in order to attract customers and
maximize their revenues. The whole process usually operates in a centralized way
(often at the storage resource broker level), resulting in a high computational
burden and hence in a performance bottleneck limiting the cloud scalability as well
as in a critical single point of failure. Our work faces these problems by proposing
three novel solutions. First, the fuzzy logic is used to properly support the
qualitative and vague preferences from non expert customers. Second, two

4. different solutions based on fuzzy inference or Dempster-Shafer theory of evidence
are proposed to conduct the service selection in a distributed way. Third, the game
theory is applied to promote truth telling ones among SSPs, by maximizing the
satisfaction of user requirements and minimizing the price of the storage service to
be paid. We have tailored our solution for storage service selection; however, it is
naive to notice that it can be extended to the broader class of service selection with
slight changes in the formulation of the problem.
Module 1
Fuzzy Logic
Fuzzy logic is an approach to computing based on "degrees of truth" rather than the
usual "true or false" (1 or 0) Boolean logic on which the modern computer is
based. The idea of fuzzy logic was first advanced by Dr. Lotfi Zadeh of the
University of California at Berkeley in the 1960s. Dr. Zadeh was working on the
problem of computer understanding of natural language. Natural language (like
most other activities in life and indeed the universe) is not easily translated into the
absolute terms of 0 and 1. (Whether everything is ultimately describable in binary
terms is a philosophical question worth pursuing, but in practice much data we
might want to feed a computer is in some state in between and so, frequently, are
the results of computing.) Fuzzy logic includes 0 and 1 as extreme cases of truth
(or "the state of matters" or "fact") but also includes the various states of truth in

5. between so that, for example, the result of a comparison between two things could
be not "tall" or "short" but ".38 of tallness." Fuzzy logic seems closer to the way
our brains work. We aggregate data and form a number of partial truths which we
aggregate further into higher truths which in turn, when certain thresholds are
exceeded, cause certain further results such as motor reaction. A similar kind of
process is used in artificial computer neural network and expert systems. It may
help to see fuzzy logic as the way reasoning really works and binary or Boolean
logic is simply a special case of it.
Module 2
Managing subjective qos preferences Bymeans of fuzzy variables
The first problem to be addressed is that customers have to indicate their
preferences with respect to the quality features that the service to be selected must
provide. In our opinion, the preferences are more useful if expressed as fuzzy
variables accepting a number of fuzzy linguistic terms , since they convey the
customer feeling about their requirements and are approximate rather than fixed
and exact in order to be quantifiable with a number. To deal with it, we propose a
fuzzy inference approach, similar to the ones presented , combining both subjective
and objective quality features for the selection of cloud storage services. The
typical way of indicating customer preferences by means of a number is not viable,

6. especially in the case of a nonexpert customer, together with the alternative
solution of expressing the provided QoS within the range (acceptable, optimal).
For example, let us consider a customer requesting a cloud storage service with a
QoS feature whose values lying within an interval [a; b], which is within the
interval of allowed values [B]. Examples can be the following ones: the reliability
degree should be between 0.8 and 0.9 or the response time between 10 ms and 50
ms. Such a solution allows us to define a set of acceptable values and a
membership function _(x)
Module 3
Selectionbasedon Fuzzy Inference
The fuzzification process consists in finding a fuzzy representation of crisp
numbers, and this is typically one by applying the membership functions
associated to each fuzzy set. Let us consider the reliability of the storage service as
the QoS feature of interest, and that the three membership functions have been
tuned as follows after concluding the genetic algorithm described before: a 1 = 0:2;
b 1 = 0:4; a 2 = 0:25; b = 0:45; c 2 = 0:6; d 2 = 0:8; a 3 = 0:65; b 2 3 = 0:8: If a
storage service exhibits a reliability of 0.75, we can transform such a crisp number
in the following fuzzy representation: fMEDIUM; _ (0:75)g, meaning that the
reliability provided by the given service is considered MEDIUM with a certain

7. degree of membership _ MEDIUM MEDIUM (0:75); HIGH; _ HIGH (0:75) and
HIGH with another degree of membership, namely _ (0:75).
Module 4
Selectionbasedon the Dempster-Shafertheory
A different solution is based on the Technique for Order Preference by Similarity
to Ideal Solution TOPSIS) approach , which computes the distances of a possible
solution to the ideal solution and to the negative ideal solution within the context
of a specific selection problem, which is resolved by the solution with the optimal
distances, we propose to jointly use fuzzy set theory and Dempster-Shafer theory
of evidence to support TOPSIS with the needed flexibility to deal with the
subjective preferences expressed by customers. As above, we have to handle both
fuzzy sets for the subjective preferences and the specific weight for each QoS
feature, and crisp numbers for the objective measures of the provided QoS for each
service involved in the selection process. To treat such values, we decide to
defuzzify the subjective preferences and weights by using the centroid method by
considering the membership function of the chosen fuzzy set altered by the
selected linguistic hedge.After having normalized the crisp value of the provided
QoS level and defuzzified the customer preference for the given QoS feature, a
distance between the provided and the required QoS level is computed according

8. to the function ReqSatis(s) where _(s)I indicates the normalized QoS level
provided by the service s, _is the defuzzified QoS level required by the customer.
The scope of the TOPSIS algorithm is to find he service exhibiting the maximum
value for the weighted combination of distances for all the QoS features under
consideration and the percentage of saved budge.
Module 5
Distributing the storage services selectionprocess
The previous two approaches have the limitation of requiring the centralization of
the overall process at the broker level. This can lead to a performance bottleneck
that can compromise the overall infrastructure. A better solution is to distribute the
selection process among the participants, in order to have a more scalable
approach. At the beginning of the selection based on fuzzy inference, every time
the statistics of the QoS features for a given service are updated, the j th SSP sends
the vector of the provided QoS levels O , together with the corresponding prices
and capacities _ j j and _ to the storage broker. The broker collects such
information from all the SSPs and runs the above-presented genetic algorithm to
determine the values to be assigned to the parameters of the membership functions
for each QoS feature. Such configurations for the membership functions are
returned to the SSPs. When a

9. request from the customer, containing the chosen linguistic terms and hedges for
all the QoS features as well as the required capacity and available budget, reaches
the storage broker, it redirects such request to all the available SSPs.