Web Service Composition in multi-cloud environment: A bi-objective genetic optimization algorithm

By advent of multi cloud computing, abundant web services are published by several providers to their world-wide users. Web service composition technology attracted a lot of attention for the sake of reduction in software development cost. In multi cloud environment (MCE), each atomic web service published by any cloud provider with same functionality has different price and quality of service (QoS). Each cybersecurity attack on security tenets can make business financial loss or even failure. Literature review in this ambit indicates that the current techniques seldom solve a mission-critical business process since majority of them pay attention only on QoS and network parameters and do not take security tenets into consideration. A bi-objective genetic optimization algorithm is presented in which it solves web service composition problem in MCE by cost and risk viewpoints. The result of implementations show that the proposed bi-objective genetic algorithm is sustainable against a single objective algorithm which minimizes only service costs and disregards security risks.

[1]  Maria Luisa Villani,et al.  An approach for QoS-aware service composition based on genetic algorithms , 2005, GECCO '05.

[2]  Fuyuki Ishikawa,et al.  Towards network-aware service composition in the cloud , 2012, WWW.

[3]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[4]  Yang Yang,et al.  A genetic-based approach to web service composition in geo-distributed cloud environment , 2015, Comput. Electr. Eng..

[5]  Yixin Chen,et al.  AI Planning and Combinatorial Optimization for Web Service Composition in Cloud Computing , 2010 .

[6]  Amin Jula,et al.  Cloud computing service composition: A systematic literature review , 2014, Expert Syst. Appl..

[7]  Ayaz Isazadeh,et al.  QoS-aware service composition in cloud computing using data mining techniques and genetic algorithm , 2017, The Journal of Supercomputing.

[8]  Ali Mili,et al.  Quantifying security threats and their potential impacts: a case study , 2010, Innovations in Systems and Software Engineering.

[9]  Mara Nikolaidou,et al.  An Integrated Approach to Automated Semantic Web Service Composition through Planning , 2012, IEEE Transactions on Services Computing.

[10]  Praveenkumar Kumar,et al.  Selection of Multi-Cloud Storage Using Cost Based Approach , 2013 .

[11]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[12]  Ali Mili,et al.  A cybersecurity model in cloud computing environments , 2013, J. King Saud Univ. Comput. Inf. Sci..

[13]  Xiaomeng Su,et al.  A Survey of Automated Web Service Composition Methods , 2004, SWSWPC.

[14]  Bin Li,et al.  Ant colony optimization applied to web service compositions in cloud computing , 2015, Comput. Electr. Eng..

[15]  Heba Kurdi,et al.  A combinatorial optimization algorithm for multiple cloud service composition , 2015, Comput. Electr. Eng..

[16]  Frank Leymann,et al.  Service-Oriented Computing , 2008, Lecture Notes in Computer Science.

[17]  Frank Gens,et al.  Cloud Computing Benefits, risks and recommendations for information security , 2010 .