# An Efficient Applications Cloud Interoperability Framework Using I-Anfis

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Proposed System

#### 3.1. Feature Extraction

#### Task Manager

_{m}) contains several features, such as task speed (I

_{1}), task cost (I

_{2}), task weight (I

_{3}), and size of the data (I

_{4}). These features are extracted for the process of resource allocation. The explanation for some of the features is given below:

_{1}is the task speed, ${\mathsf{\Gamma}}_{a}$ is the turnaround time for allocating cloud resources and $\omega $ is the amount of time taken to respond to the cloud consumer from the time of the request.

_{2}is the task cost, $\tau $ is the rate at with cloud resource are allocated to the cloud consumer and ${\mathsf{\Gamma}}_{d}$ is the amount time taken to respond to the cloud consumer from the time of the request.

_{4}denotes the user’s data size, T

_{s}represents the total size of the user’s request and $e$ denotes the allowed probability of committing an error in selecting a small representative of the user’s request.

#### 3.2. Cloud Server Features

_{5}), bandwidths (I

_{6}), disk space (I

_{7}) and number of requests (I

_{8}) are extracted [29]. The explanations of some of the cloud server features are given below:

#### 3.2.1. Memory

_{5}denotes the memory of the cloud server, S

_{L}represents the number of storage locations, and S

_{s}denotes the size of each storage location.

#### 3.2.2. Bandwidth

_{6}denotes the bandwidth of the cloud server and N

_{t}represents a number of tasks, u

_{w}denotes the usage weight.

#### 3.2.3. Disk Space

_{7}denotes the disk space of the cloud server, f

_{s}and u

_{s}represents the free space and the used space of the cloud server.

#### 3.2.4. Number of Requests

_{8}denotes the number of requests sent, and R

_{1}+ R

_{2}, …, R

_{n}denotes the requests from the user.

_{(A,C)}denotes the extraction of the features from “1” to n-th values of the application and cloud servers.

## 4. Proposed Algorithm

#### 4.1. HSSG Algorithm

- Step 1:
- Random Initialization

_{i,j}represents the j-th dimension of i-th flying squirrel. A uniform distribution (Equation (2)) is used to allocate the initial location of each flying squirrel in the forest.

_{L}and I

_{U}are lower and upper bounds respectively of i-th flying squirrel in j-th dimension and U (0, 1) is a uniformly distributed random number in the range (0, 1).

- Step 2:
- Fitness evaluation

_{EVAL}= (F

_{EVAL1}, F

_{EVAL2}, …, F

_{EVALn}

_{)}is the fitness value for each flying squirrel location.

- Step 3:
- Sorting, declaration and random selection

_{HT}), then the next three best flying squirrels are considered to be on the acorn nut trees (I

_{AT}) and the rest are considered as normal trees (I

_{NT}).

- Step 4:
- Generate new locations

- Scenario 1: The new location generated by the flying squirrels when it tends to move from acorn nut trees to hickory nut trees. The new locations can be generated as follows:

- Scenario 2: The new location generated by the flying squirrel when it tends to move from normal trees to acorn nut trees. The new locations can be generated as follows:

_{2}is a function which returns a value from the uniform distribution on the interval (0, 1).

- Scenario 3: The new location generated by the flying squirrels when it tends to move towards hickory tree if they have already fulfilled their daily energy requirements in this scenario, the new location of squirrels can be generated as follows:

_{3}is a function which returns a value from the uniform distribution on the interval (0, 1).

- Step 5:
- Seasonal monitoring condition

_{a}and u

_{b}are two functions which return a value from the uniform distribution on the interval (0, 1), $\kappa $ is the constant and $\xi $ is the Levy Index and it is calculated as:

- Step 6:
- Crossover and Mutation

- When the fitness of a flying squirrel is greater than the average one in the population (total features), it shows that the squirrel is better, and ${\u019b}_{C}$ and ${\u019b}_{m}$ should be correspondingly reduced.
- When the fitness of a flying squirrel is smaller than the average one, it indicates that the squirrel has a poor performance, and ${\u019b}_{C}$ and ${\u019b}_{m}$ should keep their initial configurations.
- When the fitness of a flying squirrel is close to the maximum one, ${\u019b}_{C}$ and ${\u019b}_{m}$ should be kept as small as possible to retain the best individual.

- Step 7:
- Stopping criterion

Algorithm 1 |

Input:$\mathrm{Extracted}\text{}\mathrm{Features}\text{}{Y}_{FS}=\left({\mathrm{I}}_{1,1},{\text{}\mathrm{I}}_{1,2},\mathrm{.....}{\mathrm{I}}_{1,\mathrm{d}}\right)$ |

Output: Relevant Features |

Begin |

Initialize the input parameters |

Initialize the location of squirrel randomly |

While (the stopping criterion is not satisfied) |

Calculate the fitness value using, |

${\mathrm{F}}_{EVAL}={\mathrm{F}}_{EVAL1}\left(\left[{\mathrm{I}}_{1,1},{\text{}\mathrm{I}}_{1,2},\mathrm{.....}{\mathrm{I}}_{1,\mathrm{d}}\right]\right)$ |

for t = 1 to U_{1} |

Generate new location scenario1: |

if $f({\mathrm{U}}_{1}\ge {P}_{AB}$) |

${\mathrm{I}}_{\mathrm{AT}}^{new}={\mathrm{I}}_{\mathrm{AT}}^{old}+{\lambda}_{g}K({\mathrm{I}}_{\mathrm{NT}}^{old}-{\mathrm{I}}_{\mathrm{AT}}^{old}$) |

else |

Random position of search space |

end for |

for t = 1 to U_{2} |

Generate new location scenario2: |

if $f({\mathrm{U}}_{2}\ge {P}_{AB}$) |

${\mathrm{I}}_{\mathrm{NT}}^{new}={\mathrm{I}}_{\mathrm{NT}}^{old}+{\lambda}_{g}K\left({\mathrm{I}}_{\mathrm{AT}}^{old}-{\mathrm{I}}_{\mathrm{NT}}^{old}\right)$ |

else |

Random position of search space |

end for |

for t=1 to U_{3} |

Generate new location scenario3: |

if $f({\mathrm{U}}_{3}\ge {P}_{AB}$) |

${\mathrm{I}}_{\mathrm{NT}}^{new}={\mathrm{I}}_{\mathrm{NT}}^{old}+{\lambda}_{g}K\left({\mathrm{I}}_{\mathrm{HT}}^{old}-{\mathrm{I}}_{\mathrm{NT}}^{old}\right)$ |

else |

Random position of search space |

end for |

Calculate seasonal constant ${\mathrm{Sea}}_{C}^{t}andSe{a}_{min}$ |

if ${\mathrm{Sea}}_{C}^{t}<Se{a}_{min}$ |

Randomly relocate flying squirrels using, |

${\mathrm{I}}_{\mathrm{NT}}^{new}={\mathrm{I}}_{L}+\mathrm{Levy}\left(\mathrm{n}\right)\times \left({\mathrm{I}}_{\mathrm{U}}-{\mathrm{I}}_{L}\right)$ |

end |

Evaluate crossover probability ${\lambda}_{c}$ and mutation probability ${\lambda}_{m}$ |

Locate the new location of squirrel on hickory nut tree obtained by crossover |

and mutation is the final optimal solution |

End While |

End Begin |

#### 4.2. Cloud Server Arrangements

#### 4.3. Cloud Server Selection

Algorithm 2 |

Input: Application and VM features |

Output: Cloud Server Selection |

Begin |

Initialize the linear parameters, ${\Im}_{i,}{x}_{i},{y}_{i}$ |

For i = 1 to n |

Generate fuzzy output from crisp input using, |

${{{\displaystyle \prod}}^{\text{}}}_{\mathrm{i}}^{1}\leftarrow \mathrm{Gaussian}\text{}\mathrm{kernal}\text{}\mathrm{membership}\text{}\mathrm{function}\text{}\left({\mathrm{I}}_{1}:c,\sigma \right)={\Omega}_{i}\left({\mathrm{I}}_{i}\right)$ |

Calculate firing strength for each rule using, |

${{{\displaystyle \prod}}^{\text{}}}_{\mathrm{i}}^{2}\leftarrow {\Omega}_{i}\left({\mathrm{I}}_{i}\right)\times {\Omega}_{i+1}\left({\mathrm{I}}_{i+1}\right)={\omega}_{i}$ |

Normalize the firing strength using, |

${{{\displaystyle \prod}}^{\text{}}}_{\mathrm{i}}^{3}\leftarrow \frac{{\mathrm{E}}_{\mathrm{y}}\left({\omega}_{i}\right)}{{{\displaystyle \sum}}^{\text{}}{\mathrm{E}}_{\mathrm{y}}\left({\omega}_{i}\right)}={\u1ff6}_{i}$ |

Calculate Defuzzify the output of 3^{rd} layer using, |

${{{\displaystyle \prod}}^{\text{}}}_{\mathrm{i}}^{4}\leftarrow {\text{}\mathrm{E}}_{\mathrm{y}}\left({\u1ff6}_{i}\right)\left({x}_{i}\left({\mathrm{I}}_{i}\right)\times {y}_{i+1}\left({\mathrm{I}}_{i+1}\right)\right)={\mathrm{E}}_{\mathrm{y}}\left({\u1ff6}_{i}\right)\times {\mathsf{\Phi}}_{i}$ |

Evaluate the output by converting fuzzy result into crisp output using |

${{{\displaystyle \prod}}^{\text{}}}_{\mathrm{i}}^{5}\leftarrow {{\displaystyle \sum}}^{\text{}}{\mathrm{E}}_{\mathrm{y}}\left({\u1ff6}_{i}\right)\times {\mathsf{\Phi}}_{i}$ |

End for |

End Begin |

## 5. Discussion and Results

#### 5.1. Performance Analysis

#### 5.1.1. Performance Analysis of Proposed HSSG Algorithm for Selecting Features

#### 5.1.2. Performance Analysis of Proposed I-ANFIS for Selection of Cloud Server

#### Training Time and Memory Usage analysis of Proposed I-ANFIS

#### 5.1.3. Performance Analysis of Proposed Quick Sort for Sorting the Selection Features

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 4.**Comparative analysis of proposed I-ANFIS technique based on precision, accuracy, recall and F-measure.

**Figure 5.**Comparative analysis of proposed I-ANFIS technique based on: (

**a**) CPU memory usage and (

**b**) Training time.

**Table 1.**Evaluation of proposed hybrid squirrel search genetic (HSSG) algorithm based on iteration vs. fitness.

Optimization Technique | FPA | CSO | GA | SSA | Proposed |
---|---|---|---|---|---|

5 | 35 | 39 | 41 | 53 | 63 |

10 | 50 | 54 | 57 | 65 | 73 |

15 | 59 | 63 | 68 | 70 | 86 |

20 | 67 | 71 | 75 | 85 | 96 |

25 | 79 | 84 | 89 | 92 | 102 |

Techniques/Performance Metrics | Existing SVM | Existing KNN | Existing ANN | Existing ANFIS | Proposed I-ANFIS |
---|---|---|---|---|---|

Precision | 84.33 | 85.01 | 85.22 | 85.5 | 87.58 |

Recall | 87.11 | 87.87 | 88.12 | 88.36 | 91.19 |

F-measure | 87.02 | 87.33 | 88.57 | 89.91 | 90.51 |

Accuracy | 88.33 | 89.66 | 91.55 | 92.55 | 94.24 |

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Ramalingam, C.; Mohan, P.
An Efficient Applications Cloud Interoperability Framework Using I-Anfis. *Symmetry* **2021**, *13*, 268.
https://doi.org/10.3390/sym13020268

**AMA Style**

Ramalingam C, Mohan P.
An Efficient Applications Cloud Interoperability Framework Using I-Anfis. *Symmetry*. 2021; 13(2):268.
https://doi.org/10.3390/sym13020268

**Chicago/Turabian Style**

Ramalingam, Chithambaramani, and Prakash Mohan.
2021. "An Efficient Applications Cloud Interoperability Framework Using I-Anfis" *Symmetry* 13, no. 2: 268.
https://doi.org/10.3390/sym13020268