Fourth Quarter 2020
Manuscript Submission Deadline
Call for Papers
Edge computing, by pushing the services, applications and data from the centralized cloud to the network edge, can significantly lower the service latency and hence improve the service quality experienced by the end users. Therefore, edge computing is widely regarded as an alternative or complementary solution to centralized cloud computing, with the advantages of vast distribution and user proximity. Edge computing is supposed to better support a variety of bandwidth-intensive and latency-sensitive applications such as IoT data processing, autonomous driving, healthcare applications. Edge computing imposes a convergence trend that all resources, e.g., sensing, communication, computation, and storage, shall be managed in a joint manner. A more flexible resource management approach is thus called for. Fortunately, recent developments in various technologies like programmable sensors, software-defined networking (SDN), network function virtualization (NFV), and container are engineering toward a softwarization trend in edge computing. These technologies together “soften” the system such that system administrators can flexibly and jointly manage all the resources in edge computing, other than relying on previously built-in rules or policies. Meanwhile, recent advances in artificial intelligence (AI) also have inspired the trend of applying AI technologies in the management of edge computing. The openness and flexibility resulted from the softwarization are readying edge computing to be empowered by various AI technologies.
At the initial stage in this trend, there are still many open challenges to be tackled. For example, we need to consider how to integrate various AI technologies with these softwarization technologies, which target at different aspects, or even with different management granularities and interfaces. On the other hand, many different AI technologies with different characteristics and capabilities are available options. We first need to understand how to appropriately adopt the right technology for a specific problem, and how these technologies perform in comparison with traditional policy-based methods. Moreover, AI technologies usually adopt a data-driven system management approach. How to choose the appropriate data for analysis and mining becomes a critical issue. At the same time, when talking about the adoption of AI technologies, other than simply applying the intelligent technologies, we shall also customize these technologies according to the problem characteristics.
To this end, this special issue will be focusing on various problems in applying AI technologies to embrace the softwarization trend in edge computing. We also welcome works on related technologies, such as machine learning, microservice, next-generation networking, network security, IoT, vehicular networks, and big data.
Possible topics of interest include but are not limited to:
- Software defined technologies in edge computing
- Intelligent flow scheduling in SDN-managed edge computing
- Reinforcement learning for edge computing management
- Microservice management in edge computing
- Interplay of container and next-generation networking technologies in edge computing
- Edge service access pattern mining and analysis
- Intelligent networking for edge computing
- Intelligent security framework and protocol for edge computing
- Intelligent privacy protection in edge computing
- Intelligent edge computing for healthcare
- Intelligent edge computing for industrial applications
- Protocols and standardization for edge computing
- Emerging technologies on machine learning for edge computing
- Novel intelligent edge computing architecture and frameworks
- Testbed, prototype of intelligent edge computing
- Performance evaluation and analysis of intelligent edge computing
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Lead Guest Editor
Deze Zeng, China University of Geosciences, Wuhan, China
Celimuge Wu, The University of Electro-Communications, Japan
Md Zakirul Alam Bhuiyan, Fordham University, NY, USA
Shui Yu, University of Technology Sydney, Australia
Rajendra Akerkar, Western Norway Research Institute, Norway
Nirwan Ansari, New Jersey Institute of Technology, USA
For inquiries regarding this Special Issue, please contact the Lead Guest Editor.