IEEEITCIIE edited this page Jan 7, 2019 · 44 revisions

IEEE SIG on Intelligent Internet Edge (IIE)

Chair: Ruidong Li, National Institute of Information and Communications Technology (NICT), Japan, Email: lrd@nict.go.jp
Subarea-Chair: Michele Nogueira, Federal University of Paraná, Brazil, Email: michele.nogueira@ufpr.br
Subarea-Chair: Stefan Schmid, University of Vienna, Austria, Email: stefan_schmid@univie.ac.at
Subarea-Chair: Rongxing Lu, University of New Brunswick, Canada, Email: RLU1@unb.ca
Subarea-Chair: Alfredo Grieco, Politecnico di Bari, Italy, Email: alfredo.grieco@poliba.it
Subarea-Chair: Ka-cheong Leung, Hongkong University, Hongkong, Email: kcleung@eee.hku.hk
Subarea-Chair: Rami Langar, University Paris Est, France, Email: rami.langar@u-pem.fr
Subarea-Chair: Paolo Bellavista, University of Bologna, Italy, Email: paolo.bellavista@unibo.it
Advisor: Thomas Hou, Virginia Tech, USA, Email: thou@vt.edu
Advisor: Nirwan Ansari, New Jersey Institute of Technology, USA, Email: Nirwan.Ansari@njit.edu
Advisor: Yuanyuan Yang, Stony Brook University, USA, Email: yuanyuan.yang@stonybrook.edu
Advisor: Yi Qian, University of Nebraska–Lincoln, USA, email: yqian2@unl.edu
Advisor: Hitoshi Asaeda, National Institute of Information and Communications Technology (NICT), Japan, Email: asaeda@nict.go.jp
Advisor: Dijiang Huang, Arizona State University, USA, Email: dijiang@asu.edu

Scope and Objectives

Cloud computing provides a scalable services consumption and delivery platform and abundant resources to users. However, it is insufficient for the scenarios for fast and high-quality data retrieval, and sometimes suffers from the problems of security, reliability and availability. Therefore, it cannot satisfy the requirements from the emerging applications, such as augmented and virtual realities (AR/VR), Internet of Things (IoT), 4K/8K streaming, smart city, vehicular systems, and disaster services. Against this backdrop, there is a strong trend to move the computations from the cloud to the Internet edges closer to users.

There are diverse possibilities for the Internet edges, which may include a variety of entities, such as small data centers, end devices, and resource-sufficient networking nodes. Fog/Edge computing is usually expected to bring the resources including storage and computation resources closer to users. For this IIE SIG, we investigate the scope where the entities at edge not only can provide the resources to the users but also can collaboratively and intelligently provide in-network storage and computing services for users. They should be investigated comprehensively from several aspects: applications, software, hardware, networking, and computation.

At the Internet edge, the new computation technologies, such as big data analytics, modern machine learning technology, artificial intelligence (AI), blockchain, and security processing, have the great potential to be embedded into network to enable it to be intelligent and trustworthy. On the other hand, information-centric networking (ICN), data center network, software-defined network (SDN), network function virtualization (NFV), and network slicing have emerged as the novel networking paradigms for fast and efficient delivering and retrieving data. This triggers the convergence between the emerging networking concepts and the new computation technologies to reach the vision of an intelligent Internet edge. For intelligent Internet edge, there are many open challenges: what services should be provided at edges; how to enable the coordination and communications between the edge and the cloud; how to enable the intelligent collaborations and networking among the edges; what computations should be embedded at the entities at the edges; which entity should be enforced with the computation; how can the computations, such as big data analytics, security, AI, machine learning, blockchain, be seamlessly embedded into the edge and enable it to be efficient, trustworthy and accountable; how to fast locate the required and suitable computation entity; how to efficiently transfer data through series of computation entities; how to efficiently collect and process the big edge data; how to design system and network architectures to easily support the efficient and diverse services; how to design the hardware architecture; how to design the middleware for the edge; how to balance the working load at the edge; how to achieve ultra-low latency communications with distributed edge computations; how to define and manage the edge/fog systems; how to build and operate small data centers; how to regulate the distributed process execution; how to adapt the resources with the dynamic user demands; and how to migrate the Internet edge to the computation-enabled intelligent edge.

In summary, this SIG will focus on the technical challenges and applications to enable the Internet edge to be intelligent. We envision that the intelligent Internet edge will provide more efficient support for the emerging applications. The areas of interests include, but are not limited to, the following:

• Intelligent edge system and networking architecture and protocols for integrating storage, computation, and communications
• Intelligent coordination and networking between edge, fog, and cloud
• Hardware architectures for intelligent Internet edge
• Software architectures and toolkits for intelligent Internet edge
• Coordination and networking among intelligent edges
• Coordination and networking among the entities in the intelligent edges
• Integration of edge/fog computing with networking
• Migration to the intelligent Internet edge
• Intelligent service function/computation chaining
• Network function virtualization, software-defined network, and network slicing for distributed computations
• Information-centric networking with/for computation
• In-network computation for future networks, inter-data center networking and 5G
• In-network computations for big data, 4K/8K streaming, IoT, 5G, and AR/VR
• Trust, security, privacy for intelligent Internet edge
• Accountability, reliability, and resiliency for intelligent Internet edge
• Quality of services and energy efficiency for intelligent Internet edges
• Programming models and scheduling for intelligent Internet edge
• Storage systems for intelligent Internet edge
• Load balancing for intelligent Internet edges
• Distributed artificial intelligence with/for edge networking
• Integrating Blockchain with distributed edges and Internet finance
• Software and hardware infrastructure for small data centers
• The construction and operations for intelligent small data centers
• Machine learning, data mining and big data analytics in networking
• Collection and processing for big network data
• Applications for intelligent Internet edge, such as AR/VR, IoT, 5G, cyber-physical system, smart city, vehicular system, healthcare
• Quantum computing at intelligent edge
• Deployment and management for intelligent Internet edge
• Performance monitoring, metering, modelling, and evaluations for intelligent Internet edge

Founding Members

  1. Ruidong Li, National Institute of Information and Communications Technology (NICT), Japan
  2. Michele Nogueira, Federal University of Paraná, Brazil
  3. Rose Qingyang Hu, Utah State University, USA
  4. Stefan Schmid, University of Vienna, Austria
  5. Xiaohua Xu, Kennesaw State University, USA
  6. Alfredo Grieco, Politecnico di Bari, Italy
  7. Rongxing Lu, University of New Brunswick, Canada
  8. Ka-cheong Leung, Hongkong University, Hongkong
  9. Ning Wang, University of Surrey, U. K.
  10. Nirwan Ansari, New Jersey Institute of Technology, USA
  11. Yuanyuan Yang, Stony Brook University, USA
  12. Hitoshi Asaeda, NICT, Japan
  13. Dijiang Huang, Arizona State University, USA
  14. Jian Ren, Michigan State University, USA
  15. Mohammad Shojafar, University of Padua, Italy
  16. Kaigui Bian, Peking University, China
  17. Thierry Turletti, INRIA, France
  18. Wan Du, University of California, Merced, USA
  19. Paul S Pang, Unitec Institute of Technology, New Zealand
  20. Thomas Hou, Virginia Tech, USA
  21. Lisong Xu, University of Nebraska at Lincoln, USA
  22. Hamada Alshaer, The University of Edinburgh, U.K.
  23. Zhuo Lu, University of South Florida, USA
  24. Alex Sim, Lawrence Berkeley National Laboratory, USA
  25. Yaoqing Liu, Clarkson University, USA
  26. Peter Muller, IBM Research GmbH, Switzerland
  27. Jenq-Haur Wang, National Taipei University of Technology, Taiwan
  28. Mingkui Wei, Sam Houston State University, USA
  29. Nishanth Sastry, King's College London, U. K.
  30. Wei Bao, University of Sydney, Australia
  31. Gregorio Martinez Perez, University of Murcia, Spain
  32. Hong-Ning Dai, Macau University of Science and Technology, Macau
  33. Qi Li, Tsinghua University, China
  34. Jung-Min (Jerry) Park, Virginia Tech, USA
  35. Xinheng (Henry) Wang, University of West London, U. K.
  36. Yi Qian, University of Nebraska–Lincoln, USA
  37. Frank Li, University of Agder, Norway
  38. Kaiping Xue, University of Science and Technology of China, China
  39. Rami Langar, University Paris Est, France
  40. Kun Yang, University of Essex, U. K.
  41. Tarik Taleb, Aalto University, Finland
  42. Huajie Shao, University of Illinois at Urbana-Champaign, USA
  43. Brij Gupta, National Institute of Technology Kurukshetra, India
  44. Maode Ma, Nanyang Technological University, Singapore
  45. Avi (Avigdor) Gal, Israel Institute of Technology, Israel
  46. Burak Kantarci, University of Ottawa, Canada
  47. Hongyi Wu, Old Dominion University, USA
  48. Mohammad Aazam, Carnegie Mellon University, USA
  49. Paolo Bellavista, University of Bologna, Italy
  50. Xinyu Que, IBM T. J. Watson Research Center, USA
  51. Renato Lo Cigno, University of Trento, Italy
  52. Mingyue Ji, University of Utah, USA
  53. Ke Xu, Tsinghua University, China
  54. Kandaraj Piamrat, University of Nantes, France

Webpage: https://github.com/IEEEITCIIE/Home/wiki

Linkedin Group: https://www.linkedin.com/groups/10413513/

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