# Dynamic Service Selection Based on Adaptive Global QoS Constraints Decomposition

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Problem Formulation

**Definition**

**1.**

**Definition**

**2.**

**Definition**

**3.**

**Definition**

**4.**

**Definition**

**5.**

**Definition**

**6.**

#### 3.1. QoS Aggregation for a Composite Service

#### 3.2. Utility Function

## 4. Dynamic Service Selection Based on Global QoS Constraints Decomposition

## 5. Adaptive Adjustment Approach Based on Fuzzy Logic

#### 5.1. The Initialization of Quality Level

#### 5.2. General Fuzzy Logic System

#### 5.3. Adaptive Quality Level Based on Fuzzy Logic

## 6. Global QoS Constraints Decomposition Based on CGA

#### 6.1. Near-Optimal Quality Level Scheme

#### 6.2. Cultural Genetic Algorithm

#### 6.3. Global QoS Constraints Decomposition Algorithm Based on CGA

#### 6.3.1. Belief Space Renewal

#### 6.3.2. Population Space Evolution

- If the parent individual ${X}_{i}^{t}$ belongs to the feasible region or semi-feasible region, it continues mutating near the feasible region. Its computation formula is as follows:$${x}_{i,j}^{t+1}={x}_{i,j}^{t}+\gamma \times \left({u}_{j}^{t}-{l}_{j}^{t}\right)\times a$$Among them, the ${x}_{i,j}^{t}$ is the $j$th independent variable of the $t$th generation individual ${X}_{i}$; $\gamma $ is a artificial positive value; ${u}_{j}^{t}$ and ${l}_{j}^{t}$, respectively, respect the upper limit and lower limit of the independent variable of $t$th generation for $N\left[j\right]$; $a$ represents a random value between 0 and 1.
- If the parent individual ${X}_{i}^{t}$ belongs to the infeasible region, we adopt the concept of the sliding window based on interval, ${X}_{i}^{t}$ move to four BC according to different probability. The computation formula is as follows:$${x}_{i,j}^{t+1}=moveTo\left(choose\left(C\left[m\right]\right)\right)$$$moreTo$ () is a migration function that moves the parent generation of an unfeasible domain to the selected target belief unit. $choose\left(C\left[m\right]\right)$ represents that according to the probability ${W}_{i}$ of each BC $C\left[i\right]$, selecting the target BC by the roulette selection method for $moreTo$ () invoking.When the $i$th BC is selected, the formula of $moreTo$ () is as follows:$${X}_{i}^{t+1}=Lef{t}_{i}+uniform\left(0,1\right)\times siz{e}_{i}$$$Lef{t}_{i}$ is a $1\times n$ array, representing the most left position of the BC $C\left[i\right]$; $siz{e}_{i}$ is a $1\times n$ array, representing the size of BC $C\left[i\right]$ in each dimension. $uniform\left(0,1\right)$ is a $1\times n$ array generated according to uniform distribution.

Algorithm 1 The optimal global QoS constraints decomposition based on CGA |

Input: The initial quality partition set of each service class |

Output: The optimal global QoS constraints decomposition scheme |

1. Initialize the population space |

2. Initialize the belief space |

3. Do |

4. Evaluate the individuals in the population space by Equation (15) |

7. Do selection operation by Equation (18) |

8. Do crossover operation by Equations (19) and (20) |

9. Do mutation operation by Equations (22) and (23) |

10. Calculate the number of excellent individuals transferred to the belief space Equation (10) |

11. Update the constraints knowledge in the belief space by Equation (11)–(14) |

10. While the termination condition is not met |

11. Return the optimal constraints decomposition scheme |

## 7. Local Service Selection

## 8. Experimental Evaluation

- Running time: the CPU time consumed by an algorithm.
- Approximation ratio: the ratio of the global utility achieved by an algorithm to the optimal utility. It can be computed as follows:$$approximationratio=\frac{U\left(CS\right)}{{U}_{optimal}\left(CS\right)}$$

#### 8.1. Evaluation of Adaptability

#### 8.1.1. The Number of Quality Level for QCD

#### 8.1.2. Adaptability

#### 8.2. Evaluation of Scalability

## 9. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Liu, Z.Z.; Xue, X.; Shen, J.Q.; Li, W.R. Web service dynamic composition based on decomposition of global QoS constraints. Int. J. Adv. Manuf. Technol.
**2013**, 69, 2247–2260. [Google Scholar] [CrossRef] - Liu, C.Y.; Cao, J.; Wang, J. A Reliable and Efficient Distributed Service Composition Approach in Pervasive Environments. IEEE Trans. Mob. Comput.
**2017**, 16, 1231–1245. [Google Scholar] [CrossRef] - Wang, P.W.; Liu, T.; Zhan, Y.; Du, X.Y. A Bayesian Nash Equilibrium of QoS-aware Web Service Composition. In Proceedings of the 24th IEEE International Conference on Web Services, Honolulu, HI, USA, 25–30 June 2017; pp. 676–683. [Google Scholar]
- Min, X.Y.; Xu, X.F.; Liu, Z.Z.; Chu, D.H.; Wang, J.Z. An approach to resource and QoS-aware service optimal composition in the big service and internet of things. IEEE Access
**2018**, 6, 39895–39906. [Google Scholar] [CrossRef] - Bellavista, P.; Corradai, A.; Foschini, L.; Monti, S. Improved Adaptive and Survivability via Dynamic Service Composition of Ubiquitous Composition Middleware. IEEE Access
**2018**, 6, 33604–33620. [Google Scholar] [CrossRef] - Ardagna, D.; Pernici, B. Adaptive service composition in flexible processes. IEEE Trans. Softw. Eng.
**2007**, 33, 369–384. [Google Scholar] [CrossRef] - Siriweera, T.H.A.S.; Paik, I. QoS-Aware Rule-Based Traffic-Efficient Multiobjective Service Selection in Big Data Space. IEEE Access
**2018**, 6, 48797–448814. [Google Scholar] [CrossRef] - Zeng, L.; Benatallah, B.; Kalagnanam, J.; Chang, H. QoS-aware middleware for web services composition. IEEE Trans. Softw. Eng.
**2004**, 19, 311–327. [Google Scholar] [CrossRef] - Ding, Z.J.; Liu, J.J.; Sun, Y.Q.; Jiang, C.J.; Zhou, M.C. A Transaction and QoS-Aware Service Selection Approach Based on Genetic Algorithm. IEEE Trans. Syst. Man Cybern. Syst.
**2017**, 45, 1035–1046. [Google Scholar] [CrossRef] - Huo, L.; Wang, Z.L. Service Composition Instantiation Based on Cross-Modified Artificial Bee Colony Algorithm. China Commun.
**2016**, 13, 233–244. [Google Scholar] [CrossRef] - Yi, Q.; Wei, Z.; Chen, H.L. Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing. Int. J. Adv. Manuf. Technol.
**2018**, 96, 4455–4465. [Google Scholar] - Moustafa, A.; Zhang, M.J.; Bai, Q. Trustworthy Stigmergic Service Composition and Adaptation in Decentralized Environments. IEEE Trans. Serv. Comput.
**2016**, 9, 317–329. [Google Scholar] [CrossRef] - Yong, Z.; Wei, L.; Luo, J.Z.; Zheng, X. A Novel Two-Phase Approach for QoS-Aware Service Composition Based on History Records. In Proceedings of the Fifth IEEE International Conference on Service-Oriented Computing and Applications (SOCA), Taipei, Taiwan, 17–19 December 2012; pp. 1–8. [Google Scholar]
- Yu, T.; Zhang, Y.; Lin, K.J. Efficient algorithms for Web services
selection with end-to-end QoS constraints. ACM Trans. Web
**2007**, 1, 6–12. [Google Scholar] [CrossRef] - Lu, W.; Wang, W.D.; Bao, E. FAQS: Fast Web Service Composition Algorithm Based on QoS-Aware Sampling. IEICE Trans. Fundam. Electron. Commun. Comput. Sci.
**2016**, E99A, 826–834. [Google Scholar] [CrossRef] - Wang, L.J.; Shen, J. A Systematic Review of Bio-Inspired Service Concretization. IEEE Trans. Serv. Comput.
**2017**, 10, 493–505. [Google Scholar] [CrossRef] - Tang, M.L.; Ai, L.F. A hybrid genetic algorithm for the optimal constrained web service selection problem in web service composition. In Proceedings of the World Congress on Computational Intelligence, Barcelona, Spain, 18–23 July 2010; pp. 1–8. [Google Scholar]
- Lecue, F.; Mehandjiev, N. Seeking Quality of Web Service Composition in a Semantic Dimension. IEEE Trans. Knowl. Data Eng.
**2011**, 23, 921–959. [Google Scholar] [CrossRef] - Zheng, X.; Luo, J.; Song, A. Ant Colony System Based Algorithm for QoS-Aware Web Service Selection. In Proceedings of the 4th International Conference on Grid Service Engineering and Management (GSEM), Leipzig, Germany, 25–26 September 2007; pp. 39–50. [Google Scholar]
- Xia, Y.; Chen, J.; Meng, X. On the Dynamic Ant Colony Algorithm Optimization Based on Multi-Pheromones. In Proceedings of the Seventh IEEE ACIS International Conference on Computer and Information Science (ICIS ’08), Portland, OR, USA, 14–16 May 2008; pp. 619–635. [Google Scholar]
- Wang, W.; Sun, Q.; Zhao, X.; Yang, F. An Improved Particle Swarm Optimization Algorithm for QoS-Aware Web Service Selection in Service Oriented Communication. Int. J. Comput. Intell. Syst.
**2012**, 3, 18–19. [Google Scholar] [CrossRef] - Cho, J.H.; Choi, J.H.; Ko, H.G.; Ko, I.Y. An Adaptive Quality Level Selection Method for Efficient QoS-aware Service Composition. In Proceedings of the IEEE 36th International Conference on Computer Software and Applications Workshops, Izmir, Turkey, 16–20 July 2012; pp. 20–25. [Google Scholar]
- Jiang, H.H.; Yang, X.H.; Yin, K.T.; Jerry, A. Multi-path QoS Aware Service Composition using Variable Length Chromosome Genetic Algorithm. Comput. Integr. Manuf. Syst.
**2011**, 10, 113–119. [Google Scholar] [CrossRef] - Wang, L.; He, Y. A Web Service Composition Algorithm Based on Global QoS Optimizing with MOCACO. In Proceedings of the 2011 International Conference on Informatics, Cybernetics, and Computer Engineering (ICCE 2011), Melbourne, Australia, 19–20 November 2011; Volume 111, pp. 79–86. [Google Scholar]
- Zhao, X.; Song, B.; Huang, P.; Wen, Z.; Weng, J.; Fan, Y. An Improved Discrete Immune Optimization Algorithm Based on PSO for QoS-Driven Web Service Composition. Appl. Soft Comput.
**2012**, 12, 2208–2216. [Google Scholar] [CrossRef] - Sun, S.A.; Zhao, J.
A decomposition-based approach for service composition with global QoS guarantees. Inf. Sci.
**2012**, 199, 138–153. [Google Scholar] [CrossRef] - Wang, S.G.; Sun, Q.B.; Yang, F.C. Web service dynamic selection by the decomposition of global QoS constraints. J. Softw.
**2011**, 22, 1426–1439. [Google Scholar] [CrossRef] - Zhang, W.X.; Lang, Y.S. The Mathematical Basis of Genetic Algorithm (Version 2); Jiaotong University Press: Xi’an, China, 2004; pp. 66–72. [Google Scholar]
- Bakhshi, M.; Mardukhi, F. A Fuzzy-Based Approach for Selecting the Optimal Composition of Services According to User Preferences. In Proceedings of the 2nd International Conference on Computer and Automation Engineering (ICCAE), Singapore, 26–28 February 2010; pp. 129–135. [Google Scholar]
- Sharifara, P.; Yari, A.; Mansour, R.K. An Evolutionary Algorithmic based Web Service Composition with Quality of Service. In Proceedings of the 7th International Symposium on Telecommunications, Tehran, Iran, 9–11 September 2014; pp. 56–65. [Google Scholar]
- Kashyap, N.; Tyagi, K. Dynamic Composition of Web Services Based on Qos Parameters Using Fuzzy Logic. In Proceedings of the International Conference on Advances in Computer Engineering and Applications (ICACEA), Ghaziabad, India, 19–20 March 2015; pp. 308–782. [Google Scholar]
- Wu, Z.P.; Yuan, M. User-Preference-Based Service Selection Using Fuzzy Logic. In Proceedings of the 2010 International Conference on Network and Service Management, Niagara Falls, ON, Canada, 25–29 October 2010; pp. 321–345. [Google Scholar]
- Silvana, D.G.A.; Karim, D. Fuzzy Logic Based QoS Optimization Mechanism for Service Composition. In Proceedings of the 2013 IEEE Seventh International Symposium on Service-Oeiented System Engineering, Redwood City, CA, USA, 25–28 March 2013; pp. 182–191. [Google Scholar]
- Chuang, S.N.; Chan, A.T.S. Dynamic QoS adaptation for mobile middleware. IEEE Trans. Softw. Eng.
**2008**, 34, 738–752. [Google Scholar] - Wang, H.B.; Zou, B.; Guo, G.B.; Yang, D.R.; Zhang, J. Integrating Trust with User Preference for Effective Web Service Composition. IEEE Trans. Serv. Comput.
**2017**, 10, 574–588. [Google Scholar] [CrossRef] - Branson, J.S.; Lilly, J.H. Incorporation, Characterization, and Conversion of Negative Rules into Fuzzy Inference Systems. IEEE Trans. Fuzzy Syst.
**2001**, 9, 253–268. [Google Scholar] [CrossRef] - Niu, S.; Zou, G.B.; Gan, Y.L.; Xiang, Y.; Zhang, B.F. Towards Uncertain QoS-aware Service Composition via Multi-objective Optimization. In Proceedings of the IEEE 24th International Conference on Web Services, Honolulu, HI, USA, 25–30 June 2017; pp. 894–897. [Google Scholar]
- Roy, R.; Dehuri, S.; Cho, S. A Novel Paricle Swarm Optimization Algorithm for Multi-Objective Combinational Optimization Problem. Int. J. Appl. Metaheurisitic Comput.
**2011**, 2, 41–57. [Google Scholar] [CrossRef] - Al-Masri, E.; Mahmoud, Q.H. The QWS Dataset. Available online: http://www.uoguelph.ca/~qmahmoud/qws/index.html (accessed on 18 March 2019).
- Ren, L.F.; Wang, W.J.; Xu, H. A Reinforcement Learning Method for Constraint-Satisfied Services Composition. IEEE Trans. Serv. Comput.
**2018**, 7, 32–39. [Google Scholar] [CrossRef]

Price | Response Time | Availability | Throughput | Successful Execution Rate |
---|---|---|---|---|

$P={\displaystyle {\displaystyle \sum}_{i=1}^{n}}{P}_{i}$ | $T={\displaystyle {\displaystyle \sum}_{i=1}^{n}}{T}_{i}$ | $A={\displaystyle {\displaystyle \prod}_{i=1}^{n}}{A}_{i}$ | $R={\mathrm{min}}_{i=1}^{n}\left\{{R}_{i}\right\}$ | $S={\displaystyle {\displaystyle \prod}_{i=1}^{n}}{S}_{i}$ |

QoS | ${\mathit{k}}_{1}$$\text{}=\text{}\mathbf{Price}$ | ${\mathit{k}}_{2}\text{}=\text{}\mathbf{Response}\text{}\mathbf{Time}$ | ${\mathit{k}}_{3}\text{}=\text{}\mathbf{Throughput}$ |
---|---|---|---|

Weight | 0.2 | 0.5 | 0.3 |

Constraint | $\le 140$ | $\le 400$ | $\ge 75$ |

IF | THEN | |
---|---|---|

$NFp$ | $N{p}_{k}$ | $c{p}_{k}$ |

B | B | M |

B | M | M |

B | L | M |

M | B | L |

M | M | L |

M | L | B |

L | B | L |

L | M | L |

L | L | B |

Fuzzy Label | Membership |
---|---|

L | 0 |

M | 0.35 |

B | 0.1 |

Fuzzy Label | Membership |
---|---|

L | 0.1 |

M | 0.35 |

B | 0 |

IF | THEN | |
---|---|---|

$\mathit{N}\mathit{F}\mathit{p}$ | $\mathit{N}{\mathit{p}}_{\mathit{k}}$ | $\mathit{c}{\mathit{p}}_{\mathit{k}}$ |

M | L | B |

M | M | L |

B | L | M |

B | M | M |

Optimal Quality Level | ${\mathit{k}}_{1}$ | ${\mathit{k}}_{2}$ | ${\mathit{k}}_{3}$ |
---|---|---|---|

${S}_{1}$ | 50 | 100 | 75 |

${S}_{2}$ | 52 | 148 | 86 |

${S}_{3}$ | 44 | 123 | 98 |

QoS | ${\mathit{k}}_{1}\text{}=\text{}\mathbf{Price}$ | ${\mathit{k}}_{2}=\text{}\mathbf{Response}\text{}\mathbf{Time}$ | ${\mathit{k}}_{3}=\text{}\mathbf{Throughput}$ |
---|---|---|---|

${S}_{1}$ | $45\le {k}_{1}\le 50$ | $90\le {k}_{2}\le 100$ | $75\le {k}_{3}\le 80$ |

${S}_{2}$ | $43\le {k}_{1}\le 52$ | $140\le {k}_{2}\le 148$ | $81\le {k}_{3}\le 86$ |

${S}_{3}$ | $36\le {k}_{1}\le 44$ | $110\le {k}_{2}\le 123$ | $94\le {k}_{3}\le 98$ |

Item | Price | Response Time | Availability |
---|---|---|---|

constraints | $<140$ dollars | $<130$ s | $>0.7$ |

preferences | 0.45 | 0.3 | 0.25 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Yuan, Y.; Zhang, W.; Zhang, X.; Zhai, H.
Dynamic Service Selection Based on Adaptive Global QoS Constraints Decomposition. *Symmetry* **2019**, *11*, 403.
https://doi.org/10.3390/sym11030403

**AMA Style**

Yuan Y, Zhang W, Zhang X, Zhai H.
Dynamic Service Selection Based on Adaptive Global QoS Constraints Decomposition. *Symmetry*. 2019; 11(3):403.
https://doi.org/10.3390/sym11030403

**Chicago/Turabian Style**

Yuan, Yuan, Weishi Zhang, Xiuguo Zhang, and Huawei Zhai.
2019. "Dynamic Service Selection Based on Adaptive Global QoS Constraints Decomposition" *Symmetry* 11, no. 3: 403.
https://doi.org/10.3390/sym11030403