IaaS Service Selection Revisited

Cloud computing is a paradigm that has revolutionized the way service-based applications are developed and provisioned due to the main benefits that it introduces, including more flexible pricing and resource management. The most widely used kind of cloud service is the Infrastructure-as-a-Service (IaaS) one. In this service kind, an infrastructure in the form of a VM is offered over which users can create the suitable environment for provisioning their application components. By following the micro-service paradigm, not just one but multiple cloud services are required to provision an application. This leads to requiring to solve an optimisation problem for selecting the right IaaS services according to the user requirements. The current techniques employed to solve this problem are either exhaustive, so not scalable, or adopt heuristics, sacrificing optimality with a reduced solving time. In this respect, this paper proposes a novel technique which involves the modelling of an optimisation problem in a different form than the most common one. In particular, this form enables the use of exhaustive techniques, like constraint programming (CP), such that both an optimal solution is delivered in a much more scalable manner. The main benefits of this technique are highlighted through conducting an experimental evaluation against a classical CP-based exhaustive approach.

[1]  Dimitris Plexousakis,et al.  Novel Optimal and Scalable Nonfunctional Service Matchmaking Techniques , 2014, IEEE Transactions on Services Computing.

[2]  Geir Horn A vision for a stochastic reasoner for autonomic cloud deployment , 2013, NordiCloud '13.

[3]  Bu-Sung Lee,et al.  Optimization of Resource Provisioning Cost in Cloud Computing , 2012, IEEE Transactions on Services Computing.

[4]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[5]  Rajkumar Buyya,et al.  Compatibility-Aware Cloud Service Composition under Fuzzy Preferences of Users , 2014, IEEE Transactions on Cloud Computing.

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

[7]  Peng Zhang,et al.  Energy-Saving Virtual Machine Placement in Cloud Data Centers , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[8]  Elizabeth Chang,et al.  Cloud service selection: State-of-the-art and future research directions , 2014, J. Netw. Comput. Appl..

[9]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[10]  Calton Pu,et al.  Improving Performance and Availability of Services Hosted on IaaS Clouds with Structural Constraint-Aware Virtual Machine Placement , 2011, 2011 IEEE International Conference on Services Computing.

[11]  Dimitris Plexousakis,et al.  Multi-cloud Application Design through Cloud Service Composition , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[12]  Daniel A. Menascé,et al.  Autonomic resource provisioning in cloud systems with availability goals , 2013, CAC.

[13]  Dimitris Plexousakis,et al.  Towards Knowledge-Based Assisted IaaS Selection , 2016, 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom).

[14]  Patrick Martin,et al.  QuARAM Service Recommender: A Platform for IaaS Service Selection , 2016, 2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC).

[15]  Toby Walsh,et al.  Handbook of Constraint Programming , 2006, Handbook of Constraint Programming.

[16]  Pascal Van Hentenryck,et al.  Strategic directions in constraint programming , 1996, CSUR.