Broker-based Cross-Cloud Federation Manager

Cloud federation is the next step in creating a new cloud computing ecosystem. The absence of suitable business models for federation managers is hindering the wide deployment of this critical feature. In this paper we will study existing federation managers, specifically CCFM (Cross-Cloud Federation Manager) and analyze its strengths and limitations. To overcome its limitations and make cloud federation more competitive in the current business trends, we introduce a new node called “broker” which will play a significant role in the enhanced CCFM mechanisms. The discovery and match-making agents in CCFM will be modified to enforce a new business model which is more interesting for private investors. Finally, our new business model will be discussed and possible versions are stated.

[1]  Yunong Zhang,et al.  Acceleration-level repetitive motion planning of redundant planar robots solved by a simplified LVI-based primal-dual neural network , 2013 .

[2]  Haopeng Chen,et al.  Dynamic Resource Arrangement in Cloud Federation , 2012, 2012 IEEE Asia-Pacific Services Computing Conference.

[3]  F. Huet,et al.  Cooperative cloud computing in research and academic environment using Virtual Cloud , 2012, 2012 International Conference on Emerging Technologies.

[4]  Rubén S. Montero,et al.  IaaS Cloud Architecture: From Virtualized Datacenters to Federated Cloud Infrastructures , 2012, Computer.

[5]  Nai-Wei Lo,et al.  An efficient resource allocation scheme for cross-cloud federation , 2012, Anti-counterfeiting, Security, and Identification.

[6]  Guijun Wang,et al.  Compositional QoS Modeling and Analysis of Cloud-based Federated Ecosystems , 2012, 2012 IEEE 16th International Enterprise Distributed Object Computing Conference.

[7]  Liana L. Fong,et al.  Cloud federation in a layered service model , 2012, J. Comput. Syst. Sci..

[8]  Binghuang Cai,et al.  Different-Level Redundancy-Resolution and Its Equivalent Relationship Analysis for Robot Manipulators Using Gradient-Descent and Zhang 's Neural-Dynamic Methods , 2012, IEEE Transactions on Industrial Electronics.

[9]  Richard Hill,et al.  Large-Scale Context Provisioning: A Use-Case for Homogenous Cloud Federation , 2012, 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems.

[10]  Dana Petcu,et al.  MODAClouds: A model-driven approach for the design and execution of applications on multiple Clouds , 2012, 2012 4th International Workshop on Modeling in Software Engineering (MISE).

[11]  Daniel Díaz Sánchez,et al.  Trust-aware federated IdM in consumer cloud computing , 2012, ICCE.

[12]  Ashish G. Revar,et al.  Securing user authentication using single sign-on in Cloud Computing , 2011, 2011 Nirma University International Conference on Engineering.

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

[14]  Antonio Puliafito,et al.  Evaluating a Distributed Identity Provider Trusted Network with Delegated Authentications for Cloud Federation , 2011, CLOUD 2011.

[15]  Yunong Zhang,et al.  Performance analysis of gradient neural network exploited for online time-varying quadratic minimization and equality-constrained quadratic programming , 2011, Neurocomputing.

[16]  Rajkumar Buyya,et al.  Cloud Computing Principles and Paradigms , 2011 .

[17]  Noboru Sonehara,et al.  User-controlled Privacy Protection with Attribute-filter Mechanism for a Federated SSO Environment Using Shibboleth , 2010, 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

[18]  Long Cheng,et al.  Multicriteria Optimization for Coordination of Redundant Robots Using a Dual Neural Network , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  Antonio Puliafito,et al.  Three-Phase Cross-Cloud Federation Model: The Cloud SSO Authentication , 2010, 2010 Second International Conference on Advances in Future Internet.

[20]  Antonio Puliafito,et al.  How to Enhance Cloud Architectures to Enable Cross-Federation: Towards Interoperable Storage Providers , 2010, 2015 IEEE International Conference on Cloud Engineering.

[21]  Partha Dasgupta,et al.  P2P Reputation Management Using Distributed Identities and Decentralized Recommendation Chains , 2010, IEEE Transactions on Knowledge and Data Engineering.

[22]  Ayman I. Kayssi,et al.  Privacy as a Service: Privacy-Aware Data Storage and Processing in Cloud Computing Architectures , 2009, 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing.

[23]  Xiaolin Hu,et al.  Motion planning with obstacle avoidance for kinematically redundant manipulators based on two recurrent neural networks , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[24]  Yunong Zhang,et al.  Equivalence of velocity-level and acceleration-level redundancy-resolution of manipulators , 2009 .

[25]  Borja Sotomayor,et al.  Virtual Infrastructure Management in Private and Hybrid Clouds , 2009, IEEE Internet Computing.

[26]  Ke Chen,et al.  Author's Personal Copy Robotics and Autonomous Systems Repetitive Motion of Redundant Robots Planned by Three Kinds of Recurrent Neural Networks and Illustrated with a Four-link Planar Manipulator's Straight-line Example , 2022 .

[27]  Thomas Sandholm,et al.  What's inside the Cloud? An architectural map of the Cloud landscape , 2009, 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing.

[28]  Yunong Zhang,et al.  Infinity-norm acceleration minimization of robotic redundant manipulators using the LVI-based primal-dual neural network , 2009 .

[29]  Bin Zhou,et al.  Secure and Distributed P2P Reputation Management , 2008, J. Commun..

[30]  Hector Garcia-Molina,et al.  Taxonomy of trust: Categorizing P2P reputation systems , 2006, Comput. Networks.

[31]  Chih-Ming Chen,et al.  A Hierarchical Neural Network Document Classifier with Linguistic Feature Selection , 2005, Applied Intelligence.

[32]  Shuzhi Sam Ge,et al.  Design and analysis of a general recurrent neural network model for time-varying matrix inversion , 2005, IEEE Transactions on Neural Networks.

[33]  Mostafa H. Ammar,et al.  A reputation system for peer-to-peer networks , 2003, NOSSDAV '03.

[34]  Hector Garcia-Molina,et al.  The Eigentrust algorithm for reputation management in P2P networks , 2003, WWW '03.

[35]  Jianhua Huang,et al.  Research of peer discovery method in peer-to-peer network , 2002, 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. TENCOM '02. Proceedings..

[36]  Steffen Hölldobler,et al.  Recurrent Neural Networks to Approximate the Semantics of Acceptable Logic Programs , 1998, Australian Joint Conference on Artificial Intelligence.

[37]  Oussama Khatib,et al.  Optimization of the inertial and acceleration characteristics of manipulators , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[38]  Fan-Tien Cheng,et al.  Resolving manipulator redundancy under inequality constraints , 1994, IEEE Trans. Robotics Autom..

[39]  Leon O. Chua,et al.  Neural networks for nonlinear programming , 1988 .

[40]  Charles A. Klein,et al.  Review of pseudoinverse control for use with kinematically redundant manipulators , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[41]  Harikrishna B Jethva,et al.  Single-Sign-On ( SSO ) across open cloud computing federation * , 2012 .

[42]  J. Edwards,et al.  Advanced Peer to Peer Discovery and Interaction Framework , 2003 .

[43]  A. Liegeois,et al.  Automatic supervisory control of the configuration and behavior of multi-body mechanisms , 1977 .