Call for Papers: Convergency of AI and Cloud/Edge Computing for Big Data Applications

Overview:

After the big success of cloud computing technology in industry, edge computing is now extending the various cloud services from central server farms to the venues close to applications. The cloud/edge computing infrastructure can provide powerful, flexible and pervasive computation and storage capabilities which have been fostering many big data applications. The advances in cloud and edge computing technologies, which have been changing the way of software development and deployment, have also been influencing the design and implementation of artificial intelligence algorithms and models. A typical example is the recent emergence of federative machine learning models, which takes the computing infrastructure into consideration when design the algorithms for the privacy-preservation reasons. Actually, the increasing computing and storage power are one of the key enablers for deep learning models, which have drawn the attention of most if not all researchers in the related areas. Meanwhile, the advances and progress have been widely adopted in cloud/edge computing platforms and applications, given the high complexity in their development, management and deployment. For example, deep learning techniques are used to monitor the health of the cloud/edge platforms which can consists of a huge number of computing servers, routers, sensing devices, and actuators. It can be seen that these two lines of technology have achieved a stage of convergency through taking both the computing infrastructure and the computation models into consideration to achieve a whole solution to the big data challenges. This has led to a plethora of successful big data applications in the domains like smart cities, Internet of things, e-commerce, driverless cars, etc. As such, it is the high time to investigate the challenges and opportunities brought by the convergency of AI and the cloud/edge computing technologies, as well as the benefits and consequences to the big data applications. Some examples are how such convergency incurs security and privacy concerns in machine/deep learning models, and how to design a more suitable cloud/edge computing platformer deployment for a specific AI algorithms or models. This special issue aims to bring academic researchers and industry practitioners together to share and discuss the challenges, recent advances and future trends of the convergency of AI and cloud/edge computing for big data applications.

Topics

Topics of interest include, but are not limited to, the following scope:

  • Cloud/edge architectures and advances for AI
  • Machine/deep learning models for cloud/edge
  • AI-based optimization for resource scheduling
  • Cloud/edge QoS based on machine learning
  • Tools/platforms combining AI and cloud/edge
  • Privacy, security and trust issues in the convergency of AI and cloud/edge
  • Cloud-driven AI for big data analytics
  • Intelligent edge for IoT applications
  • Cloud/edge computing for deep learning
  • Scalable/distributed machine learning
  • Streaming data mining with cloud/edge
  • Combing AI and cloud/edge for smart cities, e-commerce, etc.

Important Dates

  • Manuscript submission deadline: Monday 01/06/2020
  • Notification of acceptance: Monday 31/08/2020
  • Submission of final revised paper: Friday 30/10/2020
  • Publication of special issue (tentative): Monday 30/11/2020

 Submission Procedure

Authors should follow the MONET Journal manuscript format described at the journal site. Manuscripts should be submitted on-line through http://www.editorialmanager.com/mone/.

A copy of the manuscript should also be emailed to the Guest Editors at the following email address(es): xuyun.zhang@mq.edu.au; lianyongqi@gmail.com; and yyuan@msu.edu.  Authors need to register to submit their papers.

Guest Editors: