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Introduction

Nowadays, when we face with numerous data, when data cannot be classified into regular relational databases and new solutions are required, and data are generated and processed rapidly, we need powerful platforms and infrastructure as support. Extracting valuable information from raw data is especially difficult considering the velocity of growing data from year to year and the fact that 80% of data is unstructured. In addition, data sources are heterogeneous (various sensors, users with different profiles, etc.) and are in different situations or contexts. Cloud Computing, which concerns large-scale interconnected systems with the main purpose of aggregation and efficient exploiting the power of widely distributed resources, represent one viable solution. Resource Management and Task Scheduling play an essential role, in cases where one is concerned with optimized use of resources. Therefore, Cloud Computing infrastructures runs reliably and permanently to provide the context as a “public utility” to different services.
The vision of Edge Computing consider that tasks are not exclusively allocated on centralized Cloud platforms, but are distributed towards the edge of the network (as in the Internet-of-Things and Fog Computing paradigms), and transferred closer to the business thanks to Content Delivery Networks. The traditional gateway becomes a set-top-box machine, with additional computation and storage capabilities, where micro-tasks can be offloaded first, instead of directly to the cloud. Mobile Edge Computing can also be a more suitable approach to extract knowledge also from privacy sensitive data, which are not to be transferred to third party entities (global cloud operators) for processing. The proliferation of the networking connectivity and the progressive miniaturization of the computing devices have paved the way to the sensor networks and their success in the automation of the several monitoring & control applications. Such networks are built in an ad hoc manner and deployed in an unsupervised manner, without an a-priori design. The consequent availability of long-range communication means at certain nodes of those networks has enabled the possibility of the Internet connection of the sensor network, to make use of cloud-based services.
As a major goal of the workshop is to explore new directions and approaches for reasoning about smart services for Edge and Cloud Computing, based on novel models, methods and algorithms, and to encourage the submission of ongoing work, as well as position papers and case studies of existing verification projects. Also, the workshop offers a forum for both academics and practitioners to share their experience and identify new and emerging trends in this area.

Call for paper

Important date

2017-02-20
Draft paper submission deadline
2017-03-05
Draft paper acceptance notification
2017-04-02
Final paper submission deadline

Submission Topics

The scope of the workshop includes, but is not limited to:

  • Novel models and architectures of Edge-centric computing

  • Fog-to-Cloud integrations and protocols

  • IoT architecture, platforms and services

  • Edge and Cloud computing in IoT systems

  • Crowd-sensing and crowdsourcing information

  • Data harvesting and analytics in challenged networking

  • Information centric and content-centric networking

  • Distributed storage services

  • Heterogeneity of edge systems

  • Foundational Models for Resource Management in Cloud

  • Distributed Scheduling Algorithms

  • Load-Balancing and Co-Allocation

  • Dynamic, Adaptive and Machine Learning based Distributed Algorithms

  • Self-* and Autonomic Edge and Cloud Systems

  • Cloud Composition, Federation, Bridging, and Bursting

  • Cloud Resource Virtualization and Composition

  • Fault Tolerance, Reliability, Availability of Cloud Systems

  • Cloud Workload Profiling and Deployment Control

  • Cloud Quality Management and Service Level Agreement (SLA)

  • High Performance Cloud Computing

  • Many-Task Computing

  • Mobile Cloud Computing

  • Green Cloud Computing

  • Cloud Computing Platforms for Big Data

  • Resource Management in Big Data Platforms

  • Scheduling Algorithms for Big Data Processing

  • Smart Cloud-baes Services for Cognitive Computing

  • Resource Management for different Applications: Smart-Cities, e-Health, Cyber-Physical Systems, etc.

  • Simulation and Performance Evaluation

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Important Date
  • Conference Date

    May 29

    2017

    to

    May 31

    2017

  • Feb 20 2017

    Draft paper submission deadline

  • Mar 05 2017

    Draft Paper Acceptance Notification

  • Apr 02 2017

    Final Paper Deadline

  • May 31 2017

    Registration deadline