Decentralized Adaptive Latency-Aware Cloud-Edge-Dew Architecture for Unreliable Network

Smart end-user devices are connected to the global ecosystem explosively and producing an enormous amount of network traffic at the backhaul. Moreover, Real-time applications such as remote surgery, self-driving cars, and other new technologies required high quality of user experience. To address the challenges Cloud Computing is extended to a new paradigm known as Dew Computing which brings cloud services and capabilities closer to end user devices based on proximity through a decentralized exchange of data and information. However, there is still a user requirement for Ultra-low latency and reliability so that, we introduced Cloud-Edge-Dew architecture to form adaptive local resource utilization and computational offloading during unreliable network to facilitate the collaboration between the various layer in the hierarchy. Moreover, smart end-user devices establish a peer communication or accessing the micro-services which are delivered from Dew Servers and Edge Server. As a result, our scheme provides a decentralize local computation which is more efficient in response time, availability and storage.

[1]  Andrei V. Gurtov,et al.  Lightweight Dew Computing Paradigm to Manage Heterogeneous Wireless Sensor Networks with UAVs , 2018, ArXiv.

[2]  Yingwei Wang,et al.  Definition and Categorization of Dew Computing , 2016, Open J. Cloud Comput..

[3]  Yingwei Wang,et al.  Cloud-dew architecture , 2015, Int. J. Cloud Comput..

[4]  Shuhui Yang,et al.  Doing More with the Dew: A New Approach to Cloud-Dew Architecture , 2016, Open J. Cloud Comput..

[5]  Karolj Skala,et al.  Views on the role and importance of dew computing in the service and control technology , 2016, 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[6]  Marjan Gusev,et al.  A dew computing solution for IoT streaming devices , 2017, 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[7]  Naixue Xiong,et al.  Post-cloud computing paradigms: a survey and comparison , 2017 .

[8]  Marc Frîncu,et al.  Architecting a hybrid cross layer dew-fog-cloud stack for future data-driven cyber-physical systems , 2017, 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[9]  K. Skala,et al.  Augmented Coaching Ecosystem for Non-obtrusive Adaptive Personalized Elderly Care on the basis of Cloud-Fog-Dew computing paradigm , 2017, 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[10]  Yingwei Wang,et al.  Dew Computing: The Complementary Piece of Cloud Computing , 2016, 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom).

[11]  Marjan Gusev,et al.  Dew Computing and Transition of Internet Computing Paradigms , 2019 .

[12]  Yingwei Wang The relationships among cloud computing, fog computing, and dew computing , 2015 .

[13]  Karolj Skala,et al.  The dawn of Dew: Dew Computing for advanced living environment , 2017, MIPRO.

[14]  Partha Pratim Ray,et al.  An Introduction to Dew Computing: Definition, Concept and Implications , 2018, IEEE Access.

[15]  Yingwei Wang,et al.  Integrating SaaS and SaaP with Dew Computing , 2016, 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom).

[16]  Himanshu Agrawal,et al.  Cloud-fog-dew architecture for refined driving assistance: The complete service computing ecosystem , 2017, 2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband (ICUWB).

[17]  Peter Brezany,et al.  Cloud-Dew computing support for automatic data analysis in life sciences , 2017, 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[18]  H. T. Kung,et al.  Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[19]  Sasko Ristov,et al.  Implementation of a Horizontal Scalable Balancer for Dew Computing Services , 2016, Scalable Comput. Pract. Exp..

[20]  Karolj Skala,et al.  Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew Computing , 2015, Open J. Cloud Comput..