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JNTUA College of Engineering (Autonomous), Ananthapuramu
Department of Computer Science & Engineering
Edge and Fog Computing
Course Code: Honor Degree(R20) L T P C : 3 1 0 4
Course Objectives:
• To move computing and storage away from the confines of a centralized data center and
distribute those resources to one or more additional locations across the wider networked
environment. Ideally, the decentralized resources will be closer to the point where work is
being performed. This work could be data collection or user request processing
Course Outcomes:
CO1: Explore research, frameworks, applications in edge and fog computing.
C02: Review underlying technologies, limitations, and challenges along with future research direction
and discuss generic conceptual framework for optimization problems in fog computing.
CO3:Design and develop simulation scenarios for Edge and Fog Computing using network simulator
UNIT – I:
Internet of Things (IoT) and New Computing Paradigms: Introduction, Relevant Technologies,
Fog and Edge Computing Completing the Cloud, Advantages of FEC: SCALE, How FEC Achieves
These Advantages: SCANC, Hierarchy of Fog and Edge Computing, Business Models, Opportunities
and Challenges.
Addressing the Challenges in Federating Edge Resources: Introduction, The Networking
Challenge, The Management Challenge, Miscellaneous Challenges.
Integrating IoT + Fog + Cloud Infrastructures: System Modeling and Research Challenges:
Introduction, Methodology, Integrated C2F2T Literature by Modeling Technique, Integrated C2F2T
Literature by Use-Case Scenarios, Integrated C2F2T Literature by Metrics.
UNIT – II:Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds
Introduction, Background, Network Slicing in 5G, Network Slicing in Software-Defined Clouds
Network Slicing Management in Edge and Fog.
Optimization Problems in Fog and Edge Computing: Introduction, Background / Related Work,
Preliminaries, The Case for Optimization in Fog Computing, Formal Modeling Framework for Fog
Computing, Metrics, Further Quality Attributes, Optimization Opportunities along the Fog
Architecture, Optimization Opportunities along the Service Life Cycle, Toward a Taxonomy of
Optimization Problems in Fog Computing, Optimization Techniques
Middlewares: Introduction, Need for Fog and Edge Computing Middleware, Design Goals, State-of-
the-Art Middleware Infrastructures, System Model, Proposed Architecture
UNIT – III A Lightweight Container Middleware for Edge Cloud
Introduction, Background/RelatedWork, Clusters for Lightweight Edge Clouds, Architecture
Management – Storage and Orchestration, IoT Integration, Security Management for Edge Cloud
Architectures.
Data Management in Fog Computing : Introduction, Background , Fog Data Management
Predictive Analysis to Support Fog Application Deployment: Introduction, Motivating Example:
Smart Building, Predictive Analysis with Fog Torch Motivating Example (continued).
Using Machine Learning for Protecting the Security and Privacy of Internet of Things (IoT)
Systems: Introduction Background, Survey of ML Techniques for Defending IoT Devices, Machine
Learning in Fog Computing
Fog Computing Realization for Big Data Analytics:Introduction, Big Data Analytics, Data
Analytics in the Fog, Prototypes and Evaluation, Architecture.

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