|
|
|
An effective method for service components selection based on micro-canonical annealing considering dependability assurance |
Shichen ZOU1, Junyu LIN1,2( ), Huiqiang WANG1, Hongwu LV1, Guangsheng FENG1,3 |
1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
3. Department of Electrical and Computer Engineering, University of Victoria, Victoria V8W 3P6, Canada |
|
|
|
|
Abstract Distributed virtualization changes the pattern of building software systems. However, it brings some problems on dependability assurance owing to the complex social relationships and interactions between service components. The best way to solve the problems in a distributed virtualized environment is dependable service components selection. Dependable service components selection can be modeled as finding a dependable service path, which is a multiconstrained optimal path problem. In this paper, a service components selection method that searches for the dependable service path in a distributed virtualized environment is proposed from the perspective of dependability assurance. The concept of Quality of Dependability is introduced to describe and constrain software system dependability during dynamic composition. Then, we model the dependable service components selection as a multiconstrained optimal path problem, and apply the Adaptive Bonus-Penalty Microcanonical Annealing algorithm to find the optimal dependable service path. The experimental results show that the proposed algorithm has high search success rate and quick converges.
|
| Keywords
service components selection
dependability assurance
distributed virtualization
microcanonical annealing
|
|
Corresponding Author(s):
Junyu LIN
|
|
Just Accepted Date: 28 March 2017
Online First Date: 06 March 2018
Issue Date: 08 April 2019
|
|
| 1 |
F QYang, JLv, HMei. Technical framework for Internetware: an architecture centric approach. Science in China Series F: Information Sciences, 2008, 51(6): 610–622
https://doi.org/10.1007/s11432-008-0051-z
|
| 2 |
HLi, HZhao, WCai, J Q Xu, JAi. A modular attachment mechanism for software network evolution. Physica A: Statistical Mechanics and its Applications, 2013, 392(9): 2025–2037
https://doi.org/10.1016/j.physa.2013.01.035
|
| 3 |
MSharifi, M Najafzadeh, HSalimi. Co-management of power and performance in virtualized distributed environments. In: Proceedings of International Conference on Grid and Pervasive Computing. 2011, 23–32
https://doi.org/10.1007/978-3-642-20754-9_4
|
| 4 |
TVoith, KOberle, MStein. Quality of service provisioning for distributed data center inter-connectivity enabled by network virtualization. Future Generation Computer Systems, 2012, 28(3): 554–562
https://doi.org/10.1016/j.future.2011.03.011
|
| 5 |
Z SXu. Intuitionistic fuzzy aggregation operators. IEEE Transactions on Fuzzy Systems, 2007, 15(6): 1179–1187
https://doi.org/10.1109/TFUZZ.2006.890678
|
| 6 |
TKorkmaz, MKrunz. Multi-constrained optimal path selection. In: Proceedings of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2001). 2001, 834–843
https://doi.org/10.1109/INFCOM.2001.916274
|
| 7 |
Z PGao, JChen, X SQiu, L M Meng. QoE/QoS driven simulated annealing-based genetic algorithm for Web services selection. Journal of China Universities of Posts and Telecommunications, 2009, 16(S1): 102–107
https://doi.org/10.1016/S1005-8885(08)60347-7
|
| 8 |
X CZhao, B QSong, P YHuang, Z C Wen, J LWeng, YFan. An improved discrete immune optimization algorithm based on PSO for QoS-driven Web service composition. Applied Soft Computing, 2012, 12(8): 2208–2216
https://doi.org/10.1016/j.asoc.2012.03.040
|
| 9 |
Q WWu, Q SZhu. Transactional and QoS-aware dynamic service composition based on ant colony optimization. Future Generation Computer Systems, 2013, 29(5): 1112–1119
https://doi.org/10.1016/j.future.2012.12.010
|
| 10 |
BZhou, D Llewellyn-Jones, QShi, MAsim, M Merabti, DLamb. Secure service composition adaptation based on simulated annealing. In: Proceedings of the 6th Layered Assurance Workshop. 2012, 49–55
|
| 11 |
MCreutz. Microcanonical monte carlo simulation. Physical Review Letters, 1983, 50(19): 1411
https://doi.org/10.1103/PhysRevLett.50.1411
|
| 12 |
J JXu. A study on the theory and applications of meta-heuristic optimization algorithms. Dissertation for the Doctoral Degree. Beijing: Beijing University of Posts and Telecommunications. 2007
|
| 13 |
P SKeila, D B Skillicorn. Structure in the Enron email dataset. Computational and Mathematical Organization Theory, 2005, 11(3): 183–199
https://doi.org/10.1007/s10588-005-5379-y
|
| 14 |
H FSalama. Multicast routing for real-time communication of highspeed networks. Dissertation for the Doctoral Degree. Raleigh, NC: North Carolina State University. 1996
|
| 15 |
L GLiu, Y XPeng, W QXu. To converge more quickly and effectively- mean field annealing based optimal path selection in WMN. Information Sciences, 2015, 294: 216–226
https://doi.org/10.1016/j.ins.2014.10.001
|
| 16 |
DTsesmetzis, I Roussaki, ESykas. Modeling and simulation of QoSaware Web service selection for provider profit maximization. Simulation, 2007, 83(1): 93–106
https://doi.org/10.1177/0037549707079229
|
| 17 |
TZhou, X LZheng, W WSong, X F Du, D RChen. Policy-based Web service selection in context sensitive environment. In: Proceedings of IEEE Congress on Services. 2008, 255–260
https://doi.org/10.1109/SERVICES-1.2008.30
|
| 18 |
LTang, X YHuai, M SLi. An approach to dynamic service composition based on context negotiation. Journal of Computer Research and Development, 2008, 45(11): 1902–1910
|
| 19 |
W RNie, JZhang, K JLin. Estimating real-time service process response time using server utilizations. In: Proceedings of IEEE International Conference on Service-Oriented Computing and Applications. 2010, 1–8
https://doi.org/10.1109/SOCA.2010.5707133
|
| 20 |
X YWang, J KZhu, Y HShen. Network-aware QoS prediction for service composition using geolocation. IEEE Transactions on Services Computing, 2015, 8(4): 630–643
https://doi.org/10.1109/TSC.2014.2320271
|
| 21 |
WAhmed, YWu, WZheng. Response time based optimal Web service selection. IEEE Transactions on Parallel and Distributed Systems, 2015, 26(2): 551–561
https://doi.org/10.1109/TPDS.2013.310
|
| 22 |
Z BZheng, M RLyu. Selecting an optimal fault tolerance strategy for reliable service-oriented systems with local and global constraints. IEEE Transactions on Computers, 2015, 64(1): 219–232
https://doi.org/10.1109/TC.2013.189
|
| 23 |
BUpadhyaya, YZou, IKeivanloo, J Ng. Quality of experience: user’s perception about Web services. IEEE Transactions on Services Computing, 2015, 8(3): 410–421
https://doi.org/10.1109/TSC.2014.2387751
|
| 24 |
F ZFilali, B Yagoubi. Global trust: a trust model for cloud service selection. International Journal of Computer Network and Information Security, 2015, 7(5): 41
https://doi.org/10.5815/ijcnis.2015.05.06
|
| 25 |
SGupta, V Muntes-Mulero, PMatthews, JDominiak, A Omerovic, JAranda, SSeycek. Risk-driven framework for decision support in cloud service selection. In: Proceedings of the 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. 2015, 545–554
https://doi.org/10.1109/CCGrid.2015.111
|
| 26 |
S GDeng, L THuang, YLi, H GZhou, Z HWu, X F Cao, M YKataev, LLi. Toward risk reduction for mobile service composition. IEEE Transactions on Cybernetics, 2016, 46(8): 1807–1816
https://doi.org/10.1109/TCYB.2015.2446443
|
| 27 |
USadiq, MKumar, APassarella, MConti. Service composition in opportunistic networks: a load and mobility aware solution. IEEE Transactions on Computers, 2015, 64(8): 2308–2322
https://doi.org/10.1109/TC.2014.2360544
|
| 28 |
S LLiu, Y XLiu, FZhang, G F Tang, NJing. Dynamic Web services selection algorithm with QoS global optimal in Web services composition. Ruan Jian Xue Bao (Journal of Software), 2007, 18(3): 646–656
https://doi.org/10.1360/jos180646
|
| 29 |
G A GLlinas, RNagi. Network and QoS-based selection of complementary services. IEEE Transactions on Services Computing, 2015, 8(1): 79–91
https://doi.org/10.1109/TSC.2014.2299547
|
| 30 |
MAlrifai, TRisse, WNejdl. A hybrid approach for efficient Web service composition with end-to-end QoS constraints. ACM Transactions on the Web, 2012, 6(2): 1–31
https://doi.org/10.1145/2180861.2180864
|
| 31 |
L ZZeng, B Benatallah, A H HNgu, MDumas, J Kalagnanam, HChang. QoS-aware middleware for Web services composition. IEEE Transactions on Software Engineering, 2004, 30(5): 311–327
https://doi.org/10.1109/TSE.2004.11
|
| 32 |
I KGupta, JKumar, PRai. Optimization to quality-of-service-driven Web service composition using modified genetic algorithm. In: Proceedings of International Conference on Computer, Communication and Control. 2015, 1–6
https://doi.org/10.1109/IC4.2015.7375538
|
| 33 |
DSachan, S KDixit, SKumar. QoS aware formalized model for semanticWeb service selection. International Journal ofWeb and Semantic Technology, 2014, 5(4): 83–100
|
| 34 |
S YHwang, C CHsu, C HLee. Service selection forWeb services with probabilistic QoS. IEEE Transactions on Services Computing, 2015, 8(3): 467–480
https://doi.org/10.1109/TSC.2014.2338851
|
| 35 |
Z ZLiu, Z PJia, XXue, J Y An. Reliable Web service composition based on QoS dynamic prediction. Soft Computing, 2015, 19(5): 1409–1425
https://doi.org/10.1007/s00500-014-1351-4
|
| 36 |
AKlein, F Ishikawa, SHoniden. Efficient heuristic approach with improved time complexity for QoS-aware service composition. In: Proceedings of IEEE International Conference on Web Services. 2011, 436–443
https://doi.org/10.1109/ICWS.2011.60
|
| 37 |
AKlein, F Ishikawa, SHoniden. Efficient QoS-aware service composition with a probabilistic service selection policy. Service-Oriented Computing, 2010, 6470: 182–196
https://doi.org/10.1007/978-3-642-17358-5_13
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
