Cost Performance Driven Service Mashup: A Developer Perspective

Service mashups are applications created by combining single-functional services (or APIs) dispersed over the web. With the development of cloud computing and web technologies, service mashups are becoming more and more widely used and a large number of mashup platforms have been produced. However, due to the proliferation of services on the web, how to select component services to create mashups has become a challenging issue. Most developers pay more attention to the quality of service (QoS) and cost of services. Beside service selection, mashup deployment is another pivotal process, as the platform can significantly affect the quality of mashups. In this paper, we focus on creating service mashups from the perspective of developers. A genetic algorithm-based method, genetic algorithm for mashup creation (GA4MC), is proposed to select component services and deployment platforms in order to create service mashups with optimal cost performance. A series of experiments are conducted to evaluate the performance of GA4MC. The results show that the GA4MC method can achieve mashups whose cost performance is extremely close to the optimal. Moreover, the execution time of GA4MC is in a low order of magnitude and the algorithm performs good scalability as the experimental scale increases.

[1]  Zibin Zheng,et al.  Web Service Recommendation via Exploiting Location and QoS Information , 2014, IEEE Transactions on Parallel and Distributed Systems.

[2]  Fabio Casati,et al.  Rapid development of spreadsheet-based web mashups , 2009, WWW '09.

[3]  Yanbo Han,et al.  Mashroom: end-user mashup programming using nested tables , 2009, WWW '09.

[4]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5]  Klara Nahrstedt,et al.  Analysis of Topology Aggregation techniques for QoS routing , 2007, CSUR.

[6]  Jinjun Chen,et al.  Combining Local Optimization and Enumeration for QoS-aware Web Service Composition , 2010, 2010 IEEE International Conference on Web Services.

[7]  Assaf Schuster,et al.  Data mining with differential privacy , 2010, KDD.

[8]  Zhaohui Wu,et al.  Toward Risk Reduction for Mobile Service Composition , 2016, IEEE Transactions on Cybernetics.

[9]  Thomas Risse,et al.  Selecting skyline services for QoS-based web service composition , 2010, WWW '10.

[10]  Soundar R. T. Kumara,et al.  A Web Service Composition Framework Using Integer Programming with Non-functional Objectives and Constraints , 2008, 2008 10th IEEE Conference on E-Commerce Technology and the Fifth IEEE Conference on Enterprise Computing, E-Commerce and E-Services.

[11]  Bruno Volckaert,et al.  The WTE+ framework: automated construction and runtime adaptation of service mashups , 2012, Automated Software Engineering.

[12]  Wei Zhang,et al.  QoS-Based Dynamic Web Service Composition with Ant Colony Optimization , 2010, 2010 IEEE 34th Annual Computer Software and Applications Conference.

[13]  Michael Mrissa,et al.  A Framework for Building Privacy-Conscious DaaS Service Mashups , 2011, 2011 IEEE International Conference on Web Services.

[14]  Yixin Chen,et al.  QoS-Aware Dynamic Composition of Web Services Using Numerical Temporal Planning , 2014, IEEE Transactions on Services Computing.

[15]  Gero Mühl,et al.  QoS-aware composition of Web services: a look at selection algorithms , 2005, IEEE International Conference on Web Services (ICWS'05).

[16]  Weimin Zheng,et al.  Response Time Based Optimal Web Service Selection , 2015, IEEE Transactions on Parallel and Distributed Systems.

[17]  Chuang Lin,et al.  Modeling, Analysis and Optimization of Dependability-Aware Energy Efficiency in Services Computing Systems , 2013, 2013 IEEE International Conference on Services Computing.

[18]  Athman Bouguettaya,et al.  QoS Analysis for Web Service Compositions with Complex Structures , 2013, IEEE Transactions on Services Computing.

[19]  Qingsheng Zhu,et al.  A correlation-driven optimal service selection approach for virtual enterprise establishment , 2014, J. Intell. Manuf..

[20]  E. Michael Maximilien,et al.  An Online Platform for Web APIs and Service Mashups , 2008, IEEE Internet Computing.

[21]  J. Leon Zhao,et al.  Service Selection for Composition with QoS Correlations , 2016, IEEE Transactions on Services Computing.

[22]  Piergiorgio Bertoli,et al.  Automated composition of Web services via planning in asynchronous domains , 2005, Artif. Intell..

[23]  Jing Zhao,et al.  A decomposition-based approach for service composition with global QoS guarantees , 2012, Inf. Sci..

[24]  D. Giuli,et al.  A semantic-driven integer programming approach for QoS-aware dynamic service composition , 2011, 2011 50th FITCE Congress - "ICT: Bridging an Ever Shifting Digital Divide".

[25]  Stephan Reiff-Marganiec,et al.  A Backwards Composition Context Based Service Selection Approach for Service Composition , 2009, 2009 IEEE International Conference on Services Computing.

[26]  Zhaohui Wu,et al.  Mobile Service Selection for Composition: An Energy Consumption Perspective , 2017, IEEE Transactions on Automation Science and Engineering.

[27]  Michael Mrissa,et al.  Privacy-Enhanced Web Service Composition , 2014, IEEE Transactions on Services Computing.

[28]  Mária Bieliková,et al.  QoS Aware Semantic Web Service Composition Approach Considering Pre/Postconditions , 2010, 2010 IEEE International Conference on Web Services.

[29]  Jun Zhang,et al.  An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[30]  Marlon Dumas,et al.  Aggregate Quality of Service Computation for Composite Services , 2010, ICSOC.

[31]  Fang Dong,et al.  TASS: Transaction Assurance in Service Selection , 2012, 2012 IEEE 19th International Conference on Web Services.

[32]  Benjamin C. M. Fung,et al.  D-Mash: A Framework for Privacy-Preserving Data-as-a-Service Mashups , 2014, 2014 IEEE 7th International Conference on Cloud Computing.

[33]  Gagan Agrawal,et al.  A Dynamic Approach toward QoS-Aware Service Workflow Composition , 2009, 2009 IEEE International Conference on Web Services.

[34]  Albert Y. Zomaya,et al.  Computation Offloading for Service Workflow in Mobile Cloud Computing , 2015, IEEE Transactions on Parallel and Distributed Systems.

[35]  Ying-Chang Liang,et al.  Joint Beamforming and Power Control for Multiantenna Relay Broadcast Channel With QoS Constraints , 2009, IEEE Transactions on Signal Processing.

[36]  Zhaohui Wu,et al.  Mobility-Enabled Service Selection for Composite Services , 2016, IEEE Transactions on Services Computing.

[37]  Maude Manouvrier,et al.  TQoS: Transactional and QoS-Aware Selection Algorithm for Automatic Web Service Composition , 2010, IEEE Transactions on Services Computing.