Distributed and Cloud Computing | MIT Syllabus | TU

Distributed and Cloud Computing

Course Title: Distributed and Cloud Computing                                           Full Marks: 45+30

Course No: MIT551                                                                                       Pass Marks: 22.5+15

Nature of the Course: Theory + Lab                                                             Credit Hrs: 3

Semester: II

Course Description:

The course introduces the concepts of distributed and cloud computing including cloud computing, cloud service models, parallel and distributed computing, cloud networks, cloud resource management and scheduling, concurrency in cloud and emerging concepts in cloud.

Course Objectives:

The main objective of this course is to make students familiar with the concepts of distributed and cloud computing so that upon completion of the course students will be able to use and develop the distributed and cloud computing models.

Course Contents:

Unit 1: Introduction (4 Hrs.)

Cloud Computing, Impact of Cloud Computing, Ethical Issues in Cloud Computing, Factors affecting Cloud Computing Service Availability, Network Centric Computing and Network Centric Content, Virtualization and Cloud Computing, Types of Virtualization

Unit 2: Cloud Ecosystem (6 Hrs.)

Cloud Computing Delivery Models and Services, AWS, Google Clouds, Azure, IBM Clouds, Cloud Storage Diversity and Vendor Lock-In, Cloud Interoperability, Service and Compliance Level Agreements, User Challenges and Experience, Challenges in Cloud Computing

Unit 3: Parallel and Distributed Computing (5 Hrs.)

Introduction to Parallel and Distributed Computing, Elements of Parallel Computing, Elements of Distributed Computing, Technologies for Distributed Computing

Unit 4: Cloud Access and Cloud Interconnection Networks (9 Hrs.)

Packet Switched Network and Internet, TCP Congestion Control, Content Centric Network, Software Defined Networks, Interconnection Networks for Computer Clouds, Multistage Interconnection Networks, Storage Area Networks and Fiber Channel, Scalable Data Center Communication Architectures, Network Resource Management Algorithms (Fair Queuing, Class- Based Queuing), Content Delivery Networks, Vehicular Ad Hoc Networks

Unit 5: Cloud Resource Management and Scheduling (10 Hrs.)

Policies and Mechanisms for Resource Management, Scheduling Algorithms for Computer Clouds, Delay Scheduling, Data-Aware Scheduling, Apache Capacity Scheduler, Start-Time Fair Queuing, Borrowed Virtual Time, Cloud Scheduling Subject to Deadlines, Resource Bundling and Combinatorial Auctions for Cloud Resources, Resource Management and Dynamic Application Scaling, Control Theory and Optimal Resource Management, Two Level Resource Allocation

Architecture, Feedback Control on Dynamic Thresholds, Autonomic Performance Managers, Utility Model for Cloud-Based Web Services

Unit 6: Concurrency and Cloud Computing (8 Hrs.)

Concurrency, Communication and Concurrency, Computational Models, Communicating Sequential Processes, Bulk Synchronous Parallel Model, Model for Multicore Computing, Modeling Concurrency with Petri Nets, Process State, Communication Protocols and Process Coordination, Logical Clocks and Message Delivery Rules, Runs and Cuts, Threads and Activity Coordination, Critical Sections, Locks, Deadlocks, Atomic Actions, Consensus Protocol, Load Balancing

Unit 7: Emerging Clouds (3 Hrs.)

Machine Learning on Clouds, Quantum Computing on Clouds, Vehicular Clouds

Laboratory Works:

The laboratory work should include the implementation and simulation of the concepts in above mentioned units using appropriate platforms and tools.


  1. Dan C. Marinescu, Cloud Computing Theory and Practice, 3rd Edition, Morgan Kaufmann Publishers, 2022
    1. Raj Kumar Buyya, Christian Vecchiola, S. ThamaraiSelvi, Mastering Cloud Computing Foundations and Applications Programming, Morgan Kaufmann Publishers
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