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.
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
05月29日
2017
05月31日
2017
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