Call for Book Chapters: Artificial Intelligence in the Cloud: Architecture, Services and Operation

Editors:

  •   Mazin Yousif, T-Systems, International, mazin@computer.org

  •   Victor C.M. Leung, The University of British Columbia, vleung@ece.ubc.ca

  •   Song Guo, The Hong Kong Polytechnic University, song.guo@polyu.edu.hk

    Artificial Intelligence (AI) has been progressing quite remarkably in the last few years thanks to much higher computation power, graphics processors and custom hardware such as neuromorphic chips, advances in data and computer sciences, but also, to a great extent, the cloud. Cloud has become the de- facto hosting platform for all innovations and has helped extensively, by not only providing computational power, but also the flexibility to experiment as broadly as required without costing an arm or a leg. Both AI and cloud will evolve in expansive ways in the foreseeable future and will impact global economies in the same, or larger, way than how electricity changed the world more than a century back. Also, everyone is looking at marrying AI and cloud, which is happening now and will continue in a way where intelligence becomes a standard component of all cloud services and every component of a cloud architecture stack.

    The industry has been circulating names like Intelligent Cloud, Intelligent Edge and variations of them. The basic premise behind these names is that the cloud or edge will have enhanced capabilities through AI, which will allow users to do more than just computation. What the major cloud providers are doing is to add specific functions in their cloud offering, which allow users to use Intelligence to perform cognitive tasks such as analysis, gaining insights from data, computer vision or language comprehension. This is basically AI-as-a-Service (AIaaS). It is a natural fit, because AI often requires massive volumes of structure and unstructured data and massive computational power that only the cloud can provide. Cloud providers continue to extend their AIaaS as soon as they realize technologies to enable users to do more; so we expect to see a continuous flow of AI enhancements from cloud providers in the next few years. An intelligent edge will include intelligence to analyse captured data from whatever sensors deployed at the edge or far edge.

    On the other hand, cloud providers rely on intelligence to improve the efficiencies of their operations. For example, cloud providers are using intelligence to extract patterns of usage among a large number of users and use the outcome to improve efficiencies and reduce cost. Another example is the use of AI to enable cloud providers to establish use patterns of customers and suggest lowest cost deployments for them. Yet, as another example, some cloud providers are already optimizing datacenters’ energy usage by exploiting AI.

    Our book is intended to help practitioners establish a full understanding of how/where/when AI plays a role in cloud; and help researchers understand what the main challenges for bringing AI to the Cloud research challenges, integration challenges, etc. are.

    TENTATIVE CONTENT

    The book will be organized in three parts:

  •   Part I Introduction

  •   Part II - Intelligence in the Cloud - Intelligent Cloud Architecture

Part III - Intelligence for the Cloud - Intelligent Cloud Management and Operations

TOPICS

The aim of this edited book is to cover the state of the art in technological developments/advancements for intelligence in the cloud with a special focus on, but not limited to, the following topics:

  1. Distributed architectures for machine learning;

  2. Machine learning engines in the cloud;

  3. Analytics architectures, frameworks, and models for complex intelligent systems;

  4. Intelligent cloud applications or services such as intelligent traffic, intelligent buildings, intelligent

    environments, intelligent businesses, and so on;

  5. Cloud resource allocation and optimization through machine-learning algorithms;

  6. Machine learning for cloud resource management;

  7. Combining human and machine intelligence in the cloud; and

  8. Security and privacy issues for intelligent systems in the cloud.

IMPORTANT DEADLINES

  •   Submission of chapter Synopses: 1 September 2018

  •   Book Editors to send finalized list of chapter authors to IET: 15 September 2018

  •   IET to send contributor's agreements to chapter authors: 1 October 2018

  •   Writing chapters and chapter submission: 1 February 2019

  •   Book editors to review chapters: 1 April 2019

  •   Corrections and final chapter submission: 1 June 2019

  •   Send full manuscript to IET with files and copyright permissions: 1 July 2019

  •   Publication both electronic and in print: Dec 2019/Jan 2020

    SUBMISSION GUIDELINES

    Please submit your chapter proposal to editors by 1 September 2018. The proposal should consist of a brief outline of your chapter and a biography of each contributor. Upon acceptance of your proposal, IET will send you additional information (templates, formatting, permission form, etc.) with the contributor's agreement.

    Each chapter should be around 20-25 pages long, and can be submitted as a Word or Latex File. We expect original contents for this book with new materials and results. You can of course reuse published materials but the percentage of reused materials in the chapter should be less than 45%. More details about IET Author Guides can be downloaded here.


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