Replicating the geographical cloud: Provisioning omnipresence, omniscience and omnipotence

This paper discusses the current state-of-art and proposes a novel evolution of cloud computing and communications. New attributes, introduced continuously, have additively improved and evolved cloud computing to what it is today. Grid computing, data-centers and High Performance Computing (HPC) are critically reviewed and fall-outs are analyzed to corroborate new solutions. We propound a futuristic paradigm, founded on symbiosis and utility-oriented ideas, and propose a new architecture/framework for systems of the future. The authors have also made an attempt to address the question of what is to transcend cloud computing and current networking paradigms. Several applications are discussed qualitatively and rudimentary approaches are discussed. Principal theoretic feasibility of one of the proposed hypothesis of cloud communications is established. In this proposed scenario we obtain a linear increase in communication capacity, with minimal energy requirement. Unified and distributed communication paradigm Green Symbiotic Cloud Communications.Design postulates for technological systems of future.Virtualization and abstraction are introduced creating a true sense geographical cloud.Architecture allows multiple users to access multiple mediums concomitantly.Linear capacity maximization with minimal power utilization is derived and proved.

[1]  Qiang He,et al.  An energy consumption model and analysis tool for Cloud computing environments , 2012, 2012 First International Workshop on Green and Sustainable Software (GREENS).

[2]  Jie Wu,et al.  ProHet: A Probabilistic Routing Protocol with Assured Delivery Rate in Wireless Heterogeneous Sensor Networks , 2013, IEEE Transactions on Wireless Communications.

[3]  James Sexton,et al.  Enabling High-Performance Computing as a Service , 2012, Computer.

[4]  Ralf Steinmetz,et al.  Threat as a Service?: Virtualization's Impact on Cloud Security , 2012, IT Professional.

[5]  Stefan Brueck Heterogeneous networks in LTE-Advanced , 2011, 2011 8th International Symposium on Wireless Communication Systems.

[6]  Guy Pujolle,et al.  Improving Network I/O Virtualization for Cloud Computing , 2014, IEEE Transactions on Parallel and Distributed Systems.

[7]  Mohamed Cheriet,et al.  Toward a Zero-Carbon Network: Converging Cloud Computing and Network Virtualization , 2012, IEEE Internet Computing.

[8]  Shaojie Tang,et al.  Scaling laws on multicast capacity of large scale wireless networks , 2009, IEEE INFOCOM 2009.

[9]  Naixue Xiong,et al.  Green cloud computing schemes based on networks: a survey , 2012, IET Commun..

[10]  Frederica Darema,et al.  Grid Computing and Beyond: The Context of Dynamic Data Driven Applications Systems , 2005, Proceedings of the IEEE.

[11]  F. Richard Yu,et al.  Energy-Efficient Resource Allocation for Heterogeneous Cognitive Radio Networks with Femtocells , 2012, IEEE Transactions on Wireless Communications.

[12]  John M. Cioffi,et al.  Uniform power allocation in MIMO channels: a game-theoretic approach , 2003, IEEE Transactions on Information Theory.

[13]  Kwang Mong Sim,et al.  Agent-Based Cloud Computing , 2012, IEEE Transactions on Services Computing.

[14]  Yu Cheng,et al.  Capacity of Cooperative Wireless Networks Using Multiple Channels , 2010, 2010 IEEE International Conference on Communications.

[15]  Özgür Ulusoy,et al.  Exploiting interclass rules for focused crawling , 2004, IEEE Intelligent Systems.

[16]  Ekram Hossain,et al.  Two-Tier HetNets with Cognitive Femtocells: Downlink Performance Modeling and Analysis in a Multichannel Environment , 2014, IEEE Transactions on Mobile Computing.

[17]  Cristina Cervello-Pastor,et al.  On the optimal allocation of virtual resources in cloud computing networks , 2013, IEEE Transactions on Computers.

[18]  Catherine Rosenberg,et al.  Resource Allocation, Transmission Coordination and User Association in Heterogeneous Networks: A Flow-Based Unified Approach , 2013, IEEE Transactions on Wireless Communications.

[19]  Paramvir Bahl,et al.  MultiNet: connecting to multiple IEEE 802.11 networks using a single wireless card , 2004, IEEE INFOCOM 2004.

[20]  Lars Thiele,et al.  Coordinated multipoint: Concepts, performance, and field trial results , 2011, IEEE Communications Magazine.

[21]  Cheng Wang,et al.  Multicast Throughput for Hybrid Wireless Networks under Gaussian Channel Model , 2011, IEEE Trans. Mob. Comput..

[22]  Alexandru Iosup,et al.  Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing , 2011, IEEE Transactions on Parallel and Distributed Systems.

[23]  H. T. Mouftah,et al.  Designing an energy-efficient cloud network [Invited] , 2012, IEEE/OSA Journal of Optical Communications and Networking.

[24]  Rajkumar Buyya,et al.  A Review on Distributed Application Processing Frameworks in Smart Mobile Devices for Mobile Cloud Computing , 2013, IEEE Communications Surveys & Tutorials.

[25]  Domenico Talia,et al.  Clouds Meet Agents: Toward Intelligent Cloud Services , 2012, IEEE Internet Computing.

[26]  Matteo Sereno,et al.  Generalized Probabilistic Flooding in Unstructured Peer-to-Peer Networks , 2011, IEEE Transactions on Parallel and Distributed Systems.

[27]  Arun Venkataramani,et al.  Augmenting mobile 3G using WiFi , 2010, MobiSys '10.

[28]  Naga Bhushan,et al.  LTE-Advanced: Heterogeneous networks , 2010, 2010 European Wireless Conference (EW).

[29]  Casimer M. DeCusatis,et al.  Communication within clouds: open standards and proprietary protocols for data center networking , 2012, IEEE Communications Magazine.